Some things just lie on the surface, while others take time to find. In OSINT, finding the right data often means digging deep. Before you reach a conclusion, there must be solid evidence to support it, and data acquisition is always the most time consuming part of this process. The success of your investigation depends on how well you can find information and connect the dots.
OpenPlanter can automate part of this process.
OpenPlanter
Essentially, OpenPlanter is a recursive language model investigation agent. It ingests different kinds of data, which can be corporate registries, campaign finance records, government contracts and more. It then resolves entities across them and surfaces connections through evidence-based analysis. You can also use it to build profiles of individuals based on publicly available information.
OpenPlanter has both a desktop GUI and a terminal interface. The second one is more convenient.
Setting Up
The setup process is quick. We just need to create a Python environment that will host the needed libraries.
kali > git clone https://github.com/ShinMegamiBoson/OpenPlanter.git
kali > cd OpenPlanter
kali > python3 -m venv venv; source venv/bin/activate
kali > pip install -e .
Once it’s done, we need to give it our API keys.
To make web searches, OpenPlanter needs the Exa API. Exa is cheap to use and gives free credits for new accounts, so you don’t have to pay upfront. OpenRouter API is also needed to run the tool. OpenRouter has free AI models, but there is a daily usage limit. Make an account there and get your free API key.
To configure keys, run this command and paste them:
kali > openplanter-agent --configure-keys
At this point, you can use the tool.
Using OpenPlanter with OpenRouter
The daily API usage limit is enough to run a couple of basic tests, like the one below.
OpenPlanter will use Exa API key to find information. Without Exa, it burns tokens faster and gives incomplete results.
Normally, the tool saves the results in a text file in the current directory, but it doesn’t always happen. Be careful and make sure you don’t lose anything.
Here is our first report.
To make things more interesting, we asked it to find a complete list of Tatneft executives. Tatneft is one of the largest oil and gas companies in Russia.
The report was well organized, but all this information is readily available on the internet, due to the size of the Russian company.
When it was asked to find more information on a specific person from the list above, it struggled to find much and ended up with some generic data and a wrong social media account. Well, maybe that person is hard to find, so we gave it a second chance and picked a unique name from the same list: Nail Ulfatovich Maganov.
kali > openplanter-agent --task "Find as much information as you can on Nail Ulfatovich Maganov who works at Tatneft. If possible, find his Vkontakte, phone number, address, email and check if his email has been in data leaks. Save the results in a text file" --provider openrouter --model openrouter/free
The results can be seen below. OpenPlanter did find his LinkedIn account and extracted information from various places.
It also found OpenSanctions records associated with Nail Maganov.
But he is a well known figure in Russia. What about regular employees at a large Russian company? We will use Sibur for this example. Founded in 1995, it’s Russia’s largest petrochemical company.
We tried two individuals. During the first attempt, the tool didn’t find the correct person. After the second attempt with a different employee, it gave the results.
It found Svetlana’s position (Head of HR). This information was in her LinkedIn account. The rest of the information deserves further validation. Keep in mind, Russia has undergone a massive data blackout, systematically dismantling its open data and public statistics infrastructure. No wonder it’s hard to find things there.
Using OpenPlanter with Ollama – Locally
OpenPlanter’s own docs push toward frontier models (GPT-5.2, Claude Opus 4.6, Cerebras Qwen3-235B), because the whole process is quite demanding. Small local models will be noticeably weaker. But we still gave it a try. The first model was Qwen3:0.6B and its first attempt didn’t produce any results. After the second attempt, it found recent vulnerabilities that Windows had.
We also tried it with Qwen3:4b, but it produced absolutely irrelevant data in its response.
We didn’t stop here and tried it again. The results were still irrelevant. Instead of making a report on Mikhail Karisalov (CEO of Sibur) it spoke about something else.
Using OpenPlanter with Ollama – Remote Servers
If you decide to rent a server with good hardware to test other models, don’t waste your time on it. We tried various models, but none of them worked well. OpenPlanter calls a model, the model replies and then it fails. The output can be seen on the screen.
Here is an example with Qwen3.6:27b. Qwen3.6:35b had the same issue.
We also tried Ornith:35B.
These models support thinking and tooling, but they can’t really do much in this case.
Terminal Interface
It’s also important to mention that there are two ways you can use OpenPlanter in the terminal. So far, you’ve seen only one. If you’re more comfortable with a chat interface, you can use the second option.
kali > openplanter-agent --provider openrouter --model openrouter/free
Here you run your prompts and tweak the tool using the available commands.
Summary
After testing the tool in various ways, we came to the conclusion that it works reliably only with OpenRouter. That’s what gave us the best results. The developers also push towards frontier models or OpenRouter. The whole process of investigation relies heavily on the Exa API. Using it with Ollama models hosted externally (VPS) will not work, as it fails silently even if you select a supported AI model.
The tool might confuse people, especially if their names are common and their social media profiles are empty. Everything it finds deserves validation. Occasionally, it may check the results, marking them HIGH, MEDIUM or LOW depending on its confidence. It doesn’t always do it, but this can be fixed if the prompt explicitly asks for it. Most importantly, OpenPlanter can still save you time.
Learn more with our AI for Cybersecurity training. During the training, we’ll show you different ways of using AI in cybersecurity, set up local models and solve tasks with it.
NASA, IBM Launch AI Foundation Model for Lunar Science
A 10-image mosaic captured by NASA’s Lunar Reconnaissance Orbiter’s Narrow Angle Camera between June 2012 and April 2016 showing the volcanic feature Mons Rümker and its surrounding mare plains.
NASA/GSFC/Arizona State University
NASA is bringing artificial intelligence to the study of the Moon, helping researchers transform how they analyze the Moon’s surface. In an ongoing collaboration with IBM Research and several academic institutions, NASA has launched the NASA-IBM Lunar Foundation Model, among the first open-source AI models built specifically for lunar science. The model, trained primarily on data from NASA’s Lunar Reconnaissance Orbiter (LRO), is hosted publicly on Hugging Face for anyone to use, with the complete codebase available on GitHub for testing and experimentation.
The NASA-IBM Lunar Foundation Model supports the next generation of lunar science by helping researchers quickly analyze vast quantities of data to better understand the Moon’s surface. Using the model as a mapping tool, researchers can rapidly develop actionable strategies for evaluating the Moon’s rugged surface, understanding its geological past, and planning future lunar research.
“NASA has spent decades building an extraordinary scientific record of the Moon, but collecting data is only part of the job,” said Kevin Murphy, chief science data officer and acting chief data and AI officer at NASA Headquarters in Washington. “We also have to make data easier for scientists to explore and use. The NASA-IBM Lunar Foundation Model shows what’s possible when we bring AI to NASA’s petabytes of scientific data. That’s a real opportunity we see with AI: turning large-scale data into new discoveries.”
Unlike traditional models that require building and training specialized algorithms from scratch for specific tasks, foundation models are pre-trained on vast, unlabeled datasets. The broad knowledge they acquire through pre-training allows them to generalize across multiple scientific domains through quick fine-tuning, making foundation models both versatile and efficient in accelerating scientific research.
The NASA-IBM Lunar Foundation Model shows what’s possible when we bring AI to NASA’s petabytes of scientific data.
Kevin Murphy
NASA Chief Science Data Officer and Acting Chief Data Officer/Chief AI Officer
Data collected by NASA’s LRO over the past 17 years was well-suited for training this foundation model because it covers most of the lunar surface in detail. The data produced from the LRO mission is larger than all other NASA planetary missions combined, capturing an almost seamless, high-resolution mosaic of the entire Moon. The NASA-IBM model was trained on roughly 2 million image tiles from this dataset, comprising more than 1 million high-resolution camera images at 1-meter resolution and nearly 964,000 multispectral images at 100-meter resolution. The model also was trained on high-resolution Moon imagery and terrain data from multiple other missions such as NASA’s GRAIL (Gravity Recovery and Interior Laboratory), NASA’s Lunar Prospector, and JAXA’s (Japan Aerospace Exploration Agency) Selenological and Engineering Explorer.
Because the foundation model is already pre-trained on this dataset, planetary scientists can adapt the model to many different lunar research tasks such as mapping craters, spotting young volcanic features, and estimating where ice may exist near the lunar poles by using only small amounts of labeled data. For researchers who study the Moon’s polar ice, the NASA-IBM model can help them estimate where ice patches are likely to be stable, on and below the surface. Dark areas like the Moon’s permanently shadowed regions remain cold enough to trap and preserve ice for up to billions of years. Studying these areas offers insight into the Moon’s history and presents an opportunity to map potentially usable resources for future space exploration.
The NASA-IBM model reproduces patterns of lunar ice prospectivity (scaled from blue to yellow), shown at four locations (left) near the Moon’s pole. Top row: reference ice prospectivity map of Mons Mouton near the lunar south pole; middle row: predictions from the ConvNeXt model; bottom row: predictions from the NASA-IBM model. The NASA-IBM model preserves many fine-scale prospectivity patterns in the reference data.
NASA/IBM Research
While the Moon is thought to no longer be volcanically active, it once experienced dynamic geological processes. For researchers studying lunar volcanism, the NASA-IBM model accelerates the identification of unusual looking volcanic features known as irregular mare patches. Because these structures appear relatively young, they challenge established timelines for lunar cooling, and mapping them could help scientists piece together a more accurate understanding of the Moon’s thermal evolution.
The model also can map surface features, such as craters, more efficiently than manual methods. Every crater is formed by an impact, making crater counts and measurements essential for dating the lunar surface and reconstructing solar system history. The foundation model helps speed up the process of identifying and measuring craters, allowing scientists to focus on interpreting findings and determining their implications for exploration.
These Lunar Reconnaissance Orbiter images show the Moon’s surface near Einstein crater before (left) and after (right) a SpaceX rocket body impact. The NASA-IBM Lunar Foundation Model detected existing craters (blue outlines) and highlighted the newly formed impact crater (red box). Because the post-impact image was excluded from pre-training, this test demonstrates how the model can be fine-tuned to recognize novel surface changes between observations. This approach can help scientists automatically detect natural impacts and surface changes across vast lunar datasets, though varying lighting conditions between orbits may influence smaller crater visibility.
NASA/IBM Research
Overall, the model matched or exceeded the performance of several other strong baseline models across all evaluated tasks, achieving comparable results on crater mapping and segmentation of irregular mare patches, while demonstrating a clear advantage on estimating polar ice stability.
The NASA-IBM Lunar Foundation Model is part of the agency’s Office of the Chief Science Data Officer’s strategy for AI for science — a larger, ongoing collaboration between NASA and IBM aimed at using advanced AI to explore our planet and solar system. It joins a growing collection of AI models developed through this partnership, including:
The Prithvi Models: a family of models pre-trained on Earth observation data and designed to support applications such as disaster monitoring, flood mapping, crop yield prediction, and hurricane prediction.
The Surya Model: a heliophysics model trained on high-resolution solar observation data to predict space weather phenomena such as solar flares which can disrupt power grids and satellite operations.
Within NASA, the Impact AI team at the agency’s Marshall Space Flight Center in Huntsville, Alabama, collaborated with scientists in the agency’s Science Mission Directorate Planetary Science Division, NASA’s Goddard Space Flight Center in Greenbelt, Maryland, and NASA’s Ames Research Center in California’s Silicon Valley, to build the NASA-IBM model. The model is an example of open science in action, uniting experts from NASA, industry, and academia to turn raw data into a resource for lunar discovery. To support the global research community, the team released comprehensive machine learning-ready pre-training datasets and benchmark collections alongside the model, which is integrated into the open-source TerraTorch toolkit. Supported by a companion paper available on Hugging Face, this open release ensures reproducible research and equips scientists worldwide to build, compare, and refine AI models for the future of lunar exploration.
The science team, assembled by NASA Headquarters, included experts from the Universities Space Research Association in Huntsville, Alabama; the SETI Institute in Silicon Valley, California; the University of Maryland, Baltimore County in Catonsville, Maryland; Howard University in Washington, D.C.; NASA’s Science Mission Directorate Planetary Science Division; NASA Ames; and NASA Goddard.
For more information about NASA’s strategy of developing foundation models for science, visit:
At least four espionage groups, most with suspected links to China, are using a new exploit kit that chains two Chromium-based browser flaws and one Microsoft Windows bug to break into organizations' networks in the US and Southeast Asia. Mark Kelly, a threat researcher at email security shop Proofpoint, told The Register that the researchers don't know exactly who was targeted, nor how, and so far the damage appears limited. “In terms of organizations targeted, we saw fewer than 20 organizations globally targeted across the activity highlighted," he said. "However, the true number is almost certainly higher than this.” Proofpoint’s threat hunters spotted the new kit, which they named BlueMoon, and said its first observed use started on August 28. This is when a Beijing-backed crew they track as TA412, also known as Violet Typhoon and APT31, used BlueMoon to “repeatedly” target non-governmental organizations (NGOs), mining companies, and physical commodity trading firms in the US. TA412 is a cyberespionage group linked by US authorities to China's Ministry of State Security (MSS), and American prosecutors previously charged seven alleged members with conspiracy to commit computer intrusions and wire fraud, alleging they broke into computer networks, email accounts, and cloud storage belonging to numerous critical infrastructure organizations, companies, and individuals. Just days after Proofpoint documented the late-August activity, “several other espionage-motivated clusters began using BlueMoon, the majority of which have a suspected China nexus,” Kelly and fellow researchers Greg Lesnewich, Konstantin Klinger, Saher Naumaan, Julia Paluch, David Galazin, and Stuart Del Caliz said on Wednesday, noting that there may be other, non-China-nexus attackers using the exploit kit as well. “BlueMoon was developed and deployed rapidly, and shared across multiple threat actors within days,” Kelly told The Register. “This may reflect a reduced cost and barrier to entry for this class of capability, which has historically been rare and high value, as AI agents increasingly enable threat actor exploit development. That is particularly true for open-source codebases such as Chromium, where publicly accessible upstream patches create a ‘patch-gap’ window for rapid reverse engineering and exploit development ahead of downstream stable releases.” A Google spokesperson declined to comment beyond what Proofpoint wrote. Microsoft patched the Windows bug (CVE-2026-85880) on Tuesday, and a spokesperson reiterated that customers who applied that patch are protected. BlueMoon attack chain The kit chains together three vulnerabilities. The first is a V8 type confusion (CVE-2026-85046) flaw that allows remote code execution and affects all Chromium-based browsers, including Google Chrome and Microsoft Edge. Google patched this bug in Chrome on September 3, and at the time warned that it “is aware that an exploit for CVE-2026-85046 exists in the wild.” Microsoft published a security advisory saying it fixed the flaw in Edge Stable version 152.0.4191.62 on September 2. The second is a Chrome V8 sandbox escape. This one also affected all Chromium-based browsers. It does not have a CVE because Google doesn’t issue them for sandbox escapes. Finally, the third bug is a privilege escalation vulnerability in Windows Advanced Local Procedure Call (CVE-2026-85880) that Microsoft patched on Tuesday, as noted above. Redmond also warned that this flaw had been exploited as a zero-day prior to the security update. The Proofpoint researchers also note that both V8 vulnerabilities are what’s called "patch-gap" zero-days at the time of the observed activity. This means they were known and fixed in upstream Chromium source code – a change containing the fix for CVE-2026-85046 was committed on August 7. But they remained unpatched in the latest stable releases of Chrome and Chromium-based browsers available to the public for weeks. “It is likely that the exploit kit developer used these publicly available Chromium patches to weaponize the browser exploit chain,” the researchers note. From phishing to browser surveillance The attacks start with a phishing email that tricks victims into clicking on an actor-controlled URL. This triggers the two V8 bugs to allow remote code execution and escape the browser sandbox. The attack chain then exploits the Windows bug to download multiple payloads including browser-surveillance malware, credential-stealing backdoors, and others, depending on the group using the exploit kit. TA412’s first campaign, which began on August 28, used a range of lures. Some of the emails purported to come from university students interested in internships at the targeted organizations, and some were more target-specific exchanges, intended to build trust with the individual before ultimately sending a malicious link via email. In these instances, the exploit chain “ultimately downloaded and ran a loader executable on the infected host, which then installed a malicious browser extension disguised as Google Gemini on the victim's Chromium-based browser,” the team wrote. This browser extension, which Proofpoint tracks as GemStone, allowed the Beijing spies to issue commands through a command-and-control (C&C) channel, steal cookies and other sensitive data, take screenshots, and inject a keylogger into a browser tab. The malware also contains a keyword monitor, which injects an attacker-specified keyword list into the top frame of each page, scans the HTML body for these keywords, and triggers a screenshot if it finds any. A few days later, beginning on September 2, a second China-aligned spy crew that Proofpoint tracks under the temporary group designator UNK_LateNight used BlueMoon to target multiple US aerospace companies. The phishing emails used request-for-quotation lures specific to defense industry organizations, and included links to attacker-controlled domains spoofing a variety of US aerospace companies. These websites also served the BlueMoon exploit kit and ultimately loaded a backdoor called ShadowPad, which has been shared among multiple China-aligned groups since 2019. Around this same time, on September 2, another suspected espionage group that Proofpoint tracks as UNK_DoubleCheck targeted a Vietnamese manufacturing firm with messages sent from a compromised Southeast Asian government email address. The fourth campaign began a day later, and involved suspected China-linked spy crew UNK_QuietRacket using BlueMoon to target government, consulting, and financial-sector organizations in Indonesia and Singapore. These phishing emails used lures related to Indonesian conferences, such as the Indo Startup Expo and Forum 2026 and the World Conference on Creative Economy (WCCE 2026). Proofpoint warns that BlueMoon will likely be used by both cyberspies and financially motivated attackers. “The broader dynamic revealed by this activity - rapid exploit development that leverages the open source patch-gap – is likely to recur beyond BlueMoon as this development model becomes accessible,” the team wrote. ®
Artistic concept of lunar surface technologies and infrastructure capabilities, including in-situ resource utilization oxygen production systems, surface power systems, in-space manufacturing tools, and advanced nanomaterials production.
Credit: NASA
NASA is seeking proposals to advance the technology and infrastructure needed to explore the Moon and establish a Moon Base in the lunar South Pole region.
Announced on Tuesday, Sept. 8, the solicitation targets capability gaps, including power generation, oxygen extraction, and producing materials on the Moon required for construction and operations. These technologies are essential to making humanity’s next great leap in lunar exploration.
“NASA is accelerating the development of key technologies and closing critical gaps needed for long-term human exploration at the Moon,” said Greg Stover, director of NASA’s Advanced Research and Technology Division. “Partnering with industry will strengthen the U.S. industrial base as we mature the capabilities and infrastructure needed for a sustainable lunar presence.”
The NextSTEP-3 Broad Agency Announcement Appendix A: Lunar Enabling Infrastructure Accelerator solicitation aims to mature and demonstrate capabilities in five areas:
Vertical solar array technology that can provide consistent power generation, management, distribution, and energy storage.
In-situ resource utilization oxygen from regolith production to extract usable oxygen molecularly bonded to rock and dust covering the Moon’s surface.
Radioisotope Stirling generator, a type of nuclear energy technology that uses heat from fissile materials to produce electric power for operating spacecraft systems in the darkest, dustiest, and most remote places.
In-space advanced manufacturing to reduce reliance on resupply missions from Earth and to optimize mission flexibility and resilience on the Moon.
Innovative nanomaterials production to advance the commercial availability and quality of nanomaterials that can be used in lunar exploration.
The solicitation intends to cultivate U.S.-led capabilities while maintaining full and open competition among private industry, academic institutions, and not‑for‑profit entities, as well as international partners participating through U.S.-led teams.
NASA may apply insights gained from the resulting contracts of this solicitation, such as technical data, and demonstration results, to shape future acquisition strategies.
Data theft and extortion crews are stealing companies’ proprietary AI data and threatening to leak it if the victim organizations don’t pay a ransom, according to Google’s threat hunters. In one case that Google’s Mandiant incident response team investigated, the crooks broke into a healthcare company and exfiltrated corporate data and drug research, including AI research and a proprietary AI model. The criminals then threatened to publish the data unless the company met their extortion demand. In another breach at a company that specializes in AI media generation, attackers stole sensitive AI data including source code, prompts, skills, model scripts, and secrets before demanding a payment and threatening to dump the AI assets publicly if the ransom wasn’t paid. Google detailed these two intrusions for the first time in its most recent AI Threat Tracker, published Tuesday and shared in advance with The Register. “But it's certainly not limited to that,” John Hultquist, chief analyst at Google Threat Intelligence Group, said in an interview with The Register. Mandiant responded to several of these data-theft-and-extortion operations during the second quarter of 2026, he said. The intrusions affected companies in the technology, healthcare, pharmaceutical, and media and entertainment sectors in North America and Europe. “It’s become a really valuable target where organizations are spending a lot of money and investment, and they don't necessarily want their IP exposed to the open world, so they're willing to pay in an extortion scheme,” Hultquist said. “Criminals attacking AI systems is an area that's not received as much attention as it probably should, and as we incorporate these systems, it’s going to come with brand-new risks,” Hultquist added. “There are certainly threat actors who are ahead of others when it comes to that problem – TeamPCP has been extremely successful.” Since March, TeamPCP has pulled off several very large scale open source supply chain attacks targeting ecosystems including PyPI, npm, and Docker Hub. After compromising these open source packages and registries, TeamPCP, which Google tracks as UNC6780, typically deploys stealers to scoop up cloud and AI system credentials. “Evidence indicates that UNC6780 created a malicious GitHub Actions workflow for the company’s proprietary AI repository, and that the extortion actor exfiltrated a copy of this AI repository,” the report says. “Beyond these demonstrated tactics, UNC6780 has also implemented more than half a dozen different methods to target or exploit AI tools and open source software development practices.” While Google’s earlier AI tracker, published in February, documented attackers experimenting with agentic AI to support certain pieces of the attack chain, in the past quarter they’ve gone on to integrate agentic capabilities into multiple stages of an attack lifecycle, according to the researchers. In one example, Mandiant observed miscreants who compromised an organization’s cloud infrastructure in an autonomous, multi-agent credential-harvesting attack that took less than six hours. During that time, the agents autonomously scanned for vulnerabilities, performed real-time troubleshooting, and executed IP rotation logic without manual intervention. “Like scanning – but with a brain,” Hultquist said. In another case detailed in the report, Google Threat Intelligence observed a China-linked espionage group using Gemini to design a dynamic, automated penetration-testing framework that could reason through actions, execute tasks, and change course as needed in unpredictable environments. Google disabled the assets associated with this particular crew. “That’s where we are headed,” Hultquist said. “We're kind of in this interim place where threat actors are inserting agentic AI into certain parts of their operations, but we've not gotten to the place where they are able to sort of remove themselves entirely. We're right on the precipice of that.” ®
Bluetooth is often seen as something short range and therefore harmless. Many people think that because it only works over a limited distance, it must also be secure by design. But that’s not true. Bluetooth is convenient, but convenience often comes at the cost of security and privacy. A big number of vulnerabilities show that Bluetooth devices can expose much more information than many realize. At a technical level, they constantly announce their presence to the surrounding environment. Even when you are not actively using them, they still send small pieces of data. Over time these pieces form patterns that show detailed information about people’s lives.
Hackers can take control of devices, pair with them without permission and even use them as remote listening tools. In other cases, simply listening is enough.
WhisperPair Vulnerability
In January 2026, researchers from KU Leuven disclosed a critical Bluetooth vulnerability known as WhisperPair (CVE-2025-36911). This vulnerability affects hundreds of millions of Bluetooth audio devices, including headphones and headsets that rely on modern pairing mechanisms. The attack takes advantage of a feature called Fast Pair in Android. Fast Pair was designed to simplify the user experience. With a single tap users can connect their Bluetooth accessories and synchronize them with their account. It’s convenient and widely adopted.
However, some devices don’t properly ignore pairing requests when they aren’t in pairing mode. A hacker can exploit this by sending crafted pairing initiation packets to a vulnerable device. Even if the device isn’t actively trying to connect, it may still respond. Once the hacker receives that response, they can establish a normal Bluetooth connection.
Source: WhisperPair
From that point on, the hacker gains control over the accessory.
Source: WhisperPair
Then they can activate the microphone to record conversations. The attack works from up to 14 meters away, which is plenty for offices, cafes or public transport.
Source: WhisperPair
This can be combined with device tracking. Some Bluetooth accessories integrate with Google’s Find Hub network, which allows lost devices to be located using nearby Android devices. If a vulnerable accessory has never been paired with an Android device before, a hacker can register it under their own Google account. In doing so, they become the “owner” of the device in the tracking system.
An attacker tracks the victim’s location through the Find Hub network. Source: WhisperPair
The victim may eventually receive a notification about unwanted tracking, but the alert can appear misleading. If the user’s own device is responsible for tracking, that will cause confusion and reduce the likelihood that the threat is taken seriously. Meanwhile, the hacker continues to track the device over time. It affects multiple vendors, chipsets and product lines. As a result, exploitation is likely to continue well beyond 2026.
Bluehood Scanner
Sometimes, attacks are completely passive. In February 2026, a developer released a Bluetooth scanner called Bluehood. It looks like a monitoring tool and shows how much information can be extracted from the environment without ever connecting to a device.
Bluetooth is almost always enabled. Phones, laptops, smartwatches, headphones, cars and even medical devices continuously broadcast signals. Bluehood listens to that data and builds patterns over time. By passively listening to this traffic over days or weeks, hackers can reconstruct behavior.
For example, you can find out when delivery vehicles arrive and whether the same driver appears regularly. You can see daily routines by tracking when certain devices appear and disappear. You can also correlate devices that are always seen together, such as a phone and a smartwatch, which likely belong to the same person. You can even determine approximate schedules when someone leaves for work or returns home.
You don’t need to buy hardware for that. In many cases, a laptop will do the job. If you want, you can get a Raspberry Pi with a Bluetooth adapter.
Some devices are designed to always keep Bluetooth active. Hearing aids, for instance, rely on Bluetooth Low Energy for configuration and diagnostics. Pacemakers may also broadcast BLE signals for similar reasons. These aren’t devices that users can simply turn off.
Many cars use Bluetooth for diagnostics, driver assistance and connectivity features. Consumer devices add even more noise to the environment. Smartwatches, pet trackers and fitness equipment all give off signals. Together, they create a dense network of signals that can be analyzed.
Bluehood works only in passive mode. It doesn’t try to connect to devices. It identifies them based on manufacturer data and BLE service UUIDs, then tracks when they appear and disappear. The tool also includes a web dashboard. It generates hourly and daily heatmaps, tracks dwell time and has filters. New devices often use randomized MAC addresses for privacy and Bluehood can detect and filter these.
Installation
You can install the tool quickly using Docker.
kali > git clone https://github.com/dannymcc/bluehood.git
kali > cd bluehood
kali > docker compose up -d
Alternatively, you can install it using package managers and Python tools.
kali > sudo apt install bluez python3-pip
kali > pip install -e .
kali > sudo bluehood
After the installation you can start the scanner.
# Start with web dashboard (default port 8080)
kali > bluehood
# Specify a different port
kali > bluehood --port 9000
# Use a specific Bluetooth adapter
kali > bluehood --adapter hci1
# List available adapters
kali > bluehood --list-adapters
# Disable web dashboard (scanning only)
kali > bluehood --no-web
Keep in mind that if you installed the app with Docker Compose, it should be accessible at http://localhost:8080.
Collected data is stored in SQLite, and the tool can optionally send notifications through ntfy.sh when devices arrive or leave a location.
Summary
Bluetooth security is often underestimated because the technology feels invisible and low risk. That’s not the case though. There are active and passive techniques that can be used for tracking. Big cities often have listeners scattered around public places and stations, working like Bluehood. Active techniques like WhisperPair can lead to full device compromise with tracking and audio surveillance.
If you enjoy experimenting with frequencies and trying new things, we have our SDR for Hackers training. With Master OTW, you’ll learn how to use your computer and inexpensive SDR hardware to explore and hack a wide range of radio signals.
The Roscosmos Progress 92 cargo spacecraft is photographed in March 2026 from the International Space Station as it flies into an orbital sunset 267 miles above Earth’s surface.
Credit: NASA
NASA will provide live coverage of the launch and docking of a Roscosmos cargo spacecraft carrying about three tons of food, fuel, and supplies for the crew aboard the International Space Station.
The unpiloted Progress 96 resupply spacecraft is scheduled to launch at 12:15 p.m. EDT (9:15 p.m. Baikonur time), Wednesday, Sept. 9, on a Soyuz rocket from the Baikonur Cosmodrome in Kazakhstan. NASA’s live launch coverage will begin at 12 p.m.
After a two-day trip to the space station, Progress will dock autonomously to the Poisk module’s space-facing port at 2:37 p.m., Friday, Sept. 11. NASA’s arrival coverage will begin at 1:45 p.m.
NASA will stream these events live through a variety of platforms. Learn where to watch online:
The spacecraft will remain docked to the orbiting laboratory for about five months before departing to re-enter Earth’s atmosphere, where it will harmlessly burn up over the Pacific Ocean.
Before Progress 96 arrives, the Progress 94 spacecraft will undock from the space station at approximately 11:18 a.m., Monday, Sept. 7, for its departure and planned destructive re-entry. NASA will not stream coverage of Progress 94 undocking.
For more than 25 years, people have lived and worked continuously aboard the International Space Station, advancing scientific knowledge and making research breakthroughs not possible on Earth. The space station helps NASA understand and overcome the challenges of human spaceflight, expand commercial opportunities in low Earth orbit, and build on the foundation for long-duration missions to the Moon, as part of the Artemis program, and to Mars.
Learn more about the International Space Station, its research, and crew, at:
A Federal Reserve research paper has compared wholesale CBDC settlement with tokenized commercial bank deposits, adding another official research layer to the debate over how future digital money systems might operate.
The paper does not mean the Fed is launching a CBDC. It does not amount to an endorsement of crypto assets. It is research, and that distinction matters.
Still, the topic is important because banks, regulators, and payment networks are studying how tokenized deposits, wholesale central bank money, and blockchain-style settlement systems could change financial market plumbing.
For more details, visit the official Federalreserve platform.
TL;DR
Federal Reserve research examined wholesale CBDC settlement and tokenized deposits.
The paper is research, not a launch plan or policy announcement.
The debate centers on liquidity, settlement efficiency, and future payment infrastructure.
Why Wholesale CBDCs Are Different
Most public CBDC debates center on retail use.
That means a central bank digital currency held or used by the general public. Wholesale CBDCs are different. They are designed for financial institutions, settlement systems, banks, and market infrastructure.
That difference changes the political and technical debate.
A wholesale CBDC could be used to settle transactions between regulated institutions without becoming a consumer payment tool. It may affect interbank settlement, securities settlement, liquidity management, and collateral movement.
That is why wholesale CBDC research often receives attention even from institutions that are skeptical of retail CBDCs.
Tokenized Deposits Offer Another Path
Tokenized deposits are commercial bank money represented on digital rails.
Instead of issuing central bank money directly to a broader set of users, banks could issue deposit tokens that remain liabilities of commercial banks. Those tokens could then move across controlled digital infrastructure.
This model appeals to parts of the banking sector because it preserves a familiar role for commercial banks.
It may also reduce some concerns associated with retail CBDCs, while still allowing faster settlement and programmable financial workflows.
The question is whether tokenized deposits can deliver the same efficiency and trust benefits as wholesale central bank settlement.
Settlement Efficiency Is The Core Debate
Modern financial markets rely on settlement systems that can be slow, layered, and operationally complex.
If tokenized cash instruments can reduce friction, they could improve how institutions move money, settle securities, manage collateral, or transfer liquidity across market infrastructures.
But efficiency is not the only test.
Systems must also handle legal finality, resilience, privacy, compliance, cyber risk, operational controls, and central bank oversight.
That is why official research papers tend to be careful. They examine models and trade-offs rather than making sweeping claims.
Not A Crypto Endorsement
Crypto markets often react strongly to CBDC or tokenization headlines.
But this paper should not be framed as the Fed endorsing cryptocurrencies. Wholesale CBDCs and tokenized bank deposits are institutional money systems, not speculative tokens.
They may use some similar design ideas, but their purpose is different.
The value for crypto readers is that central banks are still studying the same underlying shift: financial assets and money may move onto more programmable settlement rails.
The Bigger Picture
The future of digital money may not be one system.
It could involve wholesale CBDCs, tokenized deposits, stablecoins, tokenized money-market funds, and traditional payment networks operating side by side. Each will serve different users and carry different risks.
The Fed research paper adds to that conversation.
It shows that tokenized settlement is no longer only a crypto-industry idea. It is being examined inside mainstream monetary and financial infrastructure debates.
That makes the paper important, even without a launch plan attached.
This article draws on Federal Reserve research into wholesale CBDCs and tokenized deposits.
This article was written by the News Desk and edited by Samuel Rae.
This report is based on information released by Federalreserve. at Federalreserve
While monitoring Mirage Kitten activity, we uncovered a previously undocumented malware family that we dubbed NodeRabbit. We identified the first sample on a system in Afghanistan. Further threat hunting revealed two additional, more advanced, variants: one on a system in Egypt and another on a system in Ethiopia.
NodeRabbit is a cross-platform remote access trojan (RAT) built with Node.js. It targets Windows, Linux, and macOS. Its operators deliver it through spear-phishing messages on LinkedIn and other job search platforms that contain trojanized coding challenge archives.
During the same investigation, we discovered another previously undocumented malware family that we dubbed PollCat. Like NodeRabbit, PollCat is a cross-platform RAT, but it is written in obfuscated JavaScript also distributed through trojanized coding challenge archives.
Mirage Kitten has historically relied on native malware written in languages such as C, C++, and Go, often deploying it through DLL search-order hijacking. NodeRabbit and PollCat represent the first publicly documented use of Node.js- and JavaScript-based malware by this APT group.
Kaspersky’s products detect this threat as Trojan.JS.MirageKitten.*
Background
During recent threat research, we detected suspicious activity on a system in Afghanistan. We traced it to an archive containing a software development project that the user may have received during a job application process. The archive purported to contain a coding challenge for candidates applying for an engineering role.
The archive, Front-Technical-Challenge.zip (MD5: 1EA83E4E4592B01E4ACAB63EB867BEE5), was hosted in an Amazon S3 bucket at: https://oracle-challenge.s3[.]us-east-1.amazonaws[.]com/Front-Technical-Challenge.zip
It contained TaskFlow, an app for software engineering assessment built with Express, React, and Vite. The accompanying README instructed the candidate to review the application and fix defects in its frontend. It also claimed that server.js was bug-free and should not be modified, conveniently directing attention away from the only application source file the attackers had altered.
README file for a trojanized coding challenge app
The README also imposed a three-hour time limit and prohibited the use of AI assistants. Notably, an AI code-review assistant tasked with auditing the project would likely have flagged the suspicious first-line import of an unknown npm package and warned the targeted developer that the project was trojanized.
Rules and time limit included in the trojanized coding challenge app README file
The first line of server.js imported a trojanized npm package named colorized_terminal, version 2.1.0. The attackers bundled the package directly in the challenge task archive’s node_modules directory rather than publishing it to the npm registry. When imported, the package silently launched an implant from node_modules/.cache/.320697f1/index.js as a detached background process.
Retrospective threat hunting across our telemetry revealed the broader scope of the campaign. We identified three NodeRabbit variants with a shared code lineage; each was recovered from a system in a different country. The operators delivered the variants through similarly themed coding challenges and used two trojanized packages, colorized_terminal and pretty-log, both pinned to version 2.1.0.
The campaign also delivered PollCat, a second RAT with a substantially different structure, through a separate coding challenge lure. We’ll analyze PollCat later in this research.
Initial access
The infection chain begins with fake recruiter accounts contacting prospective targets on a job search platform. According to a publicly cited source, a threat actor posing as a talent acquisition specialist at a major technology company contacted a software engineer and advertised a job opening, inviting the target to complete a technical assessment.
The target received a link to a coding challenge hosted on Amazon S3 and was pressured to download and run the project immediately. This public post matches the delivery chain we reconstructed from our telemetry: recruiter outreach on a job search platform, a coding challenge presented as a technical assessment, and a trojanized project archive hosted on legitimate cloud infrastructure.
NodeRabbit RAT: the first variant
We discovered the first NodeRabbit variant on a system in Afghanistan. The malware was concealed within the TaskFlow assessment at node_modules/.cache/.320697f1/index.js and executed by the trojanized colorized_terminal package.
Once running, NodeRabbit generates a unique agent identifier from available host information. It calculates the SHA-256 hash of the hostname, username, operating system version, architecture, and MAC address, then truncates the result to its first 32 hexadecimal characters.
NodeRabbit binds a TCP listener to 127.0.0.1:48739. This listener acts as a single-instance mechanism. If the malware cannot bind to the port, it assumes that another instance is already running and terminates silently.
NodeRabbit uses a persistence mechanism for each operating system:
Operating system
Persistence mechanism
Windows
Copies itself to %APPDATA%\Microsoft\EdgeUpdate\msedge_update.js; clones the local node.exe to nodew.exe in the same folder and patches its PE subsystem from Console to Windows GUI to suppress the console window; creates HKCU\Software\Microsoft\Windows\CurrentVersion\Run\MicrosoftEdgeUpdate registry key executing nodew.exe msedge_update.js
Linux
Copies itself to ~/.config/microsoft-edge-update/msedge_update.js and creates an @reboot cron entry that invokes the script using the current Node.js executable.
macOS
Copies itself to ~/.config/microsoft-edge-update, creates ~/Library/LaunchAgents/com.microsoft.edgeupdate.plist configuration file pointing at the copy’s location with RunAtLoad and KeepAlive parameters, and attempts to load it.
The malware communicates with its command-and-control servers through three API endpoints, choosing from the following Azure-hosted C2 infrastructure addresses. On failure, it switches to the next C2 address:
NodeRabbit serializes each C2 request object as JSON and wraps it with AES-256-GCM. The AES key is the SHA-256 digest of an ASCII seed embedded into the agent. Every request uses a fresh 12-byte IV and a 16-byte authentication tag:
The malware sends encrypted requests using the following structure:
C2 responses are structured the same way and may contain a command to execute. We observed the first NodeRabbit variant supporting 11 commands:
Command
Functionality
sys:info
Return hostname, domain user information, username, and process ID.
proc:list
List running processes.
proc:start
Execute an arbitrary shell command.
fs:list
List a directory.
fs:read
Read a file in chunks and return Base64 data.
fs:write
Decode Base64 and write it at a chosen file offset.
fs:delete
Delete a file or recursively delete a directory.
fs:mkdir
Create directories recursively.
net:config
Enumerate adapters, MAC addresses, IP addresses, and DNS settings.
agent:sleep
Change the beacon interval.
script:exec
Write a base64 Node.js script to a randomly named .tmp file, execute it and delete it.
NodeRabbit RAT: the second variant
Retrospective threat hunting following the discovery in Afghanistan led us to a second infection on a system in Egypt. This sample is a more advanced NodeRabbit variant, launched through the trojanized pretty-log package instead of colorized_terminal.
Before running its core functionality, the malware checks whether the host resembles an analysis environment. It terminates if it detects limited system memory, a low CPU count, short system uptime, analyst-associated usernames or hostnames, or common analysis tools running on the system.
Before terminating, the malware generates benign HEAD requests to www.google.com, www.microsoft.com, and www.cloudflare.com, then exits without ever contacting its C2 infrastructure. Most likely, it attempts to look less suspicious by showing some benign activity before exiting.
Variant 2 implements partial corporate proxy support: it checks HTTP(S) proxy environment variables, Windows Internet Settings, including an explicit PAC URL, and WinHTTP configuration; tunnels its HTTPS C2 through HTTP CONNECT. It first tries to establish an unauthenticated connection. If it fails, it retries using URL-embedded basic credentials. Finally, it delegates Windows NTLM/Negotiate challenges to curl.exe --proxy-anyauth --proxy-user. It caches the proxy-discovery result, including when no proxy is found, for five minutes. If the polling loop detects a network-interface or IP-address change, it clears the cache and runs proxy discovery again on the next checkin.
To make sure a single instance is running, Variant 2 uses a host-specific port derived from the agent identifier instead of the fixed TCP port used by the first variant. It interprets the first four hexadecimal characters of the identifier as an integer and applies the following calculation: 41984 + (value mod 5000).
The resulting listener port falls between 41984 and 46983. Unlike the shared port used by Variant 1, this port varies depending on the infected host.
For persistence, Variant 2 masquerades as Intel Driver & Support Assistant. The exact persistence mechanism, once again, depends on the operating system.
Operating system
Persistence mechanism
Windows
Copies itself to %LOCALAPPDATA%\Intel\DSA\idriver_support.js. It then copies the local node.exe binary to IntelDSA.exe and changes its PE subsystem from Console to Windows GUI, suppressing the console window. Finally, it creates a scheduled task named IntelDriverSupportUpdate, which runs daily at 10AM and executes IntelDSA.exe with the dropped script.
Linux
Copies itself to ~/.config/intel-dsa/idriver_support.js and creates an @reboot cron entry.
macOS
Copies itself to ~/Library/Application Support/Intel DSA/idriver_support.js and creates the LaunchAgent com.intel.dsa.helper with RunAtLoad and KeepAlive enabled.
NodeRabbit RAT: the third variant
Further threat hunting identified a third NodeRabbit variant on a system in Ethiopia. Like the second variant, it is launched through the trojanized pretty-log package. It retains much of the previous variant’s functionality but introduces significant changes to its command-and-control configuration, command set, and persistence mechanisms.
The third variant communicates with its C2 infrastructure through a different set of API endpoints:
Method
Endpoint
Purpose
POST
/sdk/v2/ready
Register agent and host info
POST
/sdk/v2/config
Poll for commands
POST
/sdk/v2/events
Submit results
We observed the malware using a C2 chain composed of Azure- and Cloudflare-hosted domains.
For persistence, Variant 3 implements the following mechanisms depending on the operating system in use:
Operating system
Persistence mechanism
Windows
Attempts to copy the payload to ProgramData or LocalAppData, create a build-specific daily 10AM task, and start the copied payload. To choose the exact directory, it tries to list C:\Windows\System32\config. If successful, it selects ProgramData with /ru SYSTEM /rl highest; in case of a failure, it selects LocalAppData without explicit /ru or /rl settings.
macOS
Copies the payload to ~/Library/Application Support, creates and loads a RunAtLoad/KeepAlive LaunchAgent and starts the copied payload.
Linux
Copies the payload to ~/.local/share, attempts to add an @reboot cron entry, and starts the copied payload. If crontab -l fails, persistence is skipped.
WSL
Uses the payload copied for persistence on the main Linux system, as described above. Writes launcher.vbs under the Windows user profile, and creates a daily 10AM Windows task that relaunches it through wscript.exe and wsl.exe.
A new command, agent:servers, replaces the active in-memory C2 server list and can write the updated list to .sv.json. The third variant retains the original 11 commands and adds 12 new ones, bringing the total to 23.
New commands
Functionality
fs:drives
Enumerate accessible Windows drive letters or WSL-mounted drives
proc:exec
Execute a process
proc:kill
Kill process by PID or image name
agent:servers
Replace the active C2 and attempt to keep the new configuration
agent:getchain
Return the current C2
outlook:emails
Harvest account addresses from Outlook OST and PST artifacts
persist:check
Check selected VS Code, scheduled-task, and Run-key persistence indicators
persist:vscode
Attempt to install a fake VS Code extension and Windows Run value
persist:vscode:remove
Remove the fake extension
persist:projects:scan
Search recent and common development locations for Git repositories
persist:project:inject
Inject a launcher into a repository’s Git hooks
persist:project:remove
Remove the marked Git-hook launcher
Beyond the persistence mechanisms described above, Variant 3 introduces two additional persistence mechanisms that relaunch the malware through common developer workflows.
1. Malicious VS Code extension
The persist:vscode command first copies the payload to its build-specific install path. If a compatible extension directory exists, it creates a fake extension displayed as GitHub Copilot Helper, with the description AI coding assistant helper service and the activation event on StartupFinished.
The extension’s extension.js file attempts to start the installed payload as a detached Node.js process. To look less suspicious to the user, it uses a trusted publisher name borrowed from local extension metadata or a trustedPublishers value found in state.vscdb. However, no signature or trusted status is copied.
Separately, the handler tries to disable Workspace Trust if the VS Code User directory exists. On Windows, it attempts to establish persistence using a current-user Run registry key value even if the extension directory is missing.
2. Git hook injection
Git-hook persistence works in two steps. First, persist:projects:scan checks recent VS Code workspace paths directly. Under common locations such as ~/projects and ~/source, it checks only the first 60 immediate children, not the root itself, and returns no more than 20 repositories.
For a selected repository, persist:project:inject appends a marked launcher to .git/hooks/post-merge and .git/hooks/post-checkout by default. The marker is # shepherd-persist; the line following the marker attempts to start the installed payload with Node in the background. A later Git operation must trigger one of those hooks, and the referenced Node executable and payload must still exist.
PollCat RAT
While tracking NodeRabbit infections, we discovered another malicious tool we dubbed PollCat, which is also distributed under the guise of a programming challenge. The sample we obtained resides inside RankChallenge-react, a React code-fixing challenge presented as a time-limited developer assessment. Running the project invokes npm i && node index.js, which starts the local application and attempts to open the challenge in the user’s browser.
Although the visible exercise is not a security CTF, the project uses CTF terminology in several places. The root package is named ctf-server, the backend prints CTF server running, the frontend uses several ctf-* storage keys, and the tutorial refers to path/to/ctf. These repeated labels, together with instructions that do not fully match the delivered application, are consistent with an AI-assisted or template-generated project. One possible explanation is that the attacker prompted an AI coding assistant to create a CTF-style React platform and later inserted the malicious components.
README instructions and challenge overview included in the trojanized React coding project
The PDF tutorial contained in the same archive as the project tells the target to click Continue, enter a six-digit OTP code, and complete the challenge within a one-hour session. It states that codes are supplied by the recruiter, are single-use, and expire quickly; the visible login page also claims that codes rotate every 30 seconds. In the delivery scenario described by the investigation, the threat actor posing as a recruiter could provide the code directly to the targeted developer. This gives the operator control over access to the lure, while the expiring code and countdown create a sense of urgency, pressuring the target to run the project and complete the assessment quickly, potentially accelerating the infection process.
One-hour session window enforced by the trojanized coding challenge
The bundled .env file contains the JWT signing secret, OTP service URL, and OTP client ID.
Configuration embedded in .env file of the trojanized coding project, including the OTP service URL and client identifier
The application forwards submitted codes to an attacker-managed domain registered in late June-2026: https://lifespotify[.]com/api/users/b879746e-fed9-4211-a6da-4d8223681267/otp/validate.
That said, PollCat starts independently of the OTP authentication process. During application startup, app.js loads requireAuth.js, which imports and immediately starts the malicious requireObjects.js component. PollCat can therefore begin C2 registration and command polling while the application is still loading, before the user enters an access code.
A failed OTP validation prevents the user from accessing the protected challenge features, but PollCat continues running in the background. A successful OTP validation issues a JWT and creates another worker that starts an additional PollCat instance. The first authenticated request also triggers the persistence attempt.
Persistence starts when the first request carrying a valid JWT reaches the protected middleware. PollCat then uses one of the following methods:
Operation system
Persistence mechanism
Windows
Writes package.json and requireObject.js to %APPDATA%\Microsoft\Network, runs npm install, and creates a daily task named NetSync_<username> and scheduled for 09AM that runs the worker with Node.js.
Linux
Writes the worker to ~/.node_packages, runs npm i, and appends both a daily 09AM cron line and an @reboot line.
macOS
Uses the same ~/.node_packages copy and cron path, then creates and loads ~/Library/LaunchAgents/com.harsh.requireobject.plist with RunAtLoad and a daily 09AM trigger.
Once active, PollCat identifies the host as 129--<hostname> and iterates over the following C2s until registration succeeds:
After registration, PollCat sends host information to /gate/hello, polls /gate/fetch for commands, and returns results through /gate/submit. All endpoints in use are presented in the table below.
Method
Endpoint
Purpose
POST
/beacon
Register the client and obtain a socketId and optional timing values.
POST
/gate/hello
Submit host, user, domain, OS information, and its current privilege level.
GET
/gate/fetch?token=<socketId>
Poll for commands.
POST
/gate/submit
Submit a Base64-encoded command-result structure.
GET
/vault/<uuid>
Retrieve a hosted file and write it to the victim machine.
PUT
/vault/push/
Upload a local file or file chunk to the C2.
POST
/gate/track
Report chunk-upload progress.
By default, PollCat RAT polls every two minutes with up to five seconds of jitter. Commands and results are stored as little-endian binary records and carried as Base64 text.
PollCat RAT declares 22 commands, but three of them have no implementation:
Command
Functionality
0x02 (DIR)
List a directory.
0x03 (MV)
Move a file or directory.
0x04 (RUN)
Execute a shell command.
0x05 (TASKLIST)
List running processes.
0x06 (DEL)
Delete a file or directory.
0x07 (UPLOAD)
Download a file from the C2 to the victim’s machine.
0x08 (DOWNLOAD)
Upload a local file to the C2.
0X09 (DRIVES)
List drives, volumes, or mount points.
0X0A (TERMINATE)
Terminate a process by PID.
0X0B (RUNDLL)
Load a DLL and call an exported function on Windows.
0X0C (MKDIR)
Create a directory.
0X0D (ZIP)
Create or extract a ZIP archive.
0X0E (CHUNKED_DOWNLOAD)
Upload a local file in chunks.
0X0F (RUN_HIDDEN)
Start a hidden background process.
0X20 (EVAL_JS)
Execute JavaScript supplied by the C2.
0X30 (SYSTEM_CHECK)
Collect process and software inventory.
0XA1 (WS_DOWNLOAD)
Defined but not implemented.
0xB0 (REQUEST_ELEVATION)
Defined but not implemented.
0XB1 (PERSIST)
Defined but not implemented.
0xF0 (SET_SLEEP_TIME)
Change the polling interval.
0XF1 (SET_IDLE_TIME)
Store an idle-time value.
0xF2 (SET_JITTER_TIME)
Change polling jitter.
The command names UPLOAD, DOWNLOAD, and CHUNKED_DOWNLOAD are written from the C2’s perspective. UPLOAD sends a C2-hosted file to the victim’s machine, while the two download commands transfer victim files back to the C2.
EVAL_JS runs JavaScript supplied by the C2 and gives that code access to Node.js modules, files, processes, networking, and child-process functions. SYSTEM_CHECK collects the names of running processes and lists files and folders from:
%SystemDrive%\Program Files
%SystemDrive%\Program Files (x86)
%LOCALAPPDATA%
%LOCALAPPDATA%\Programs
%APPDATA%
%USERPROFILE%
%APPDATA%\Microsoft\Outlook
%LOCALAPPDATA%\Microsoft\Olk\Attachments
%USERPROFILE%\Documents
It also searches for folders matching 24 hardcoded strings corresponding to security software vendor names: ‘Google’, ‘Microsoft’, ‘Palo Alto Networks’, ‘Cisco’, ‘VMware’, ‘Fortinet’, ‘Citrix’, ‘CheckPoint’, ‘Juniper Networks’, ‘LogMeIn’, ‘Sophos’, ‘Symantec’, ‘Trend Micro’, ‘McAfee’, ‘Kaspersky Lab’, ‘ESET’, ‘Bitdefender’, ‘Avast Software’, ‘CrowdStrike’, ‘SentinelOne’, ‘Malwarebytes’, ‘BraveSoftware’, ‘Tencent’, and ‘Naver’.
When PollCat finds a matching folder, it lists that folder’s root contents. It does not recursively scan the entire product directory. The detailed inventory, including process names, directory listings, and collected paths, is sent as JSON to POST /api/system-details/result.
Infrastructure
Mirage Kitten continues to rely on Azure Websites and Cloudflare-backed domains to hinder infrastructure discovery and tracking. More importantly, the use of Microsoft Azure subdomains for C2 helps the traffic blend into legitimate organizational network activity. In some cases that we encountered during our research, the actors even incorporated the targeted organization’s name into the Azure subdomain, making C2 communications appear more like normal business traffic originating from an employee machine during regular business days.
Based on our analysis of Mirage Kitten’s infrastructure, we identified certain patterns across several command-and-control channels, including msmanagementgrp[.]com and visitfinancedentists[.]com
Further investigation based on these patterns led to the discovery of approximately 11 additional infrastructure assets attributed to the same group.
Domain
Creation date
Registrar
healthful-hub[.]com
2026-07-03
NameCheap, Inc.
neumedicahealthcare[.]com
2026-07-03
NameCheap, Inc.
optimumhealthcredit[.]com
2026-07-03
NameCheap, Inc.
healthfullyrecipes[.]com
2026-06-30
NameCheap, Inc.
refreshhealthandwellness[.]com
2026-06-09
NameCheap, Inc.
healthvitalitycare[.]com
2026-05-18
NameCheap, Inc.
aceofspadesmanagement[.]com
2026-05-18
NameCheap, Inc.
glmediaagency[.]com
2026-05-18
NameCheap, Inc.
digimediaskill[.]com
2026-05-18
NameCheap, Inc.
healthyweightplan[.]com
2026-05-18
NameCheap, Inc.
mens-health-online[.]com
2026-05-15
NameCheap, Inc.
Victims
Based on our telemetry, we identified victims in fintech, aviation and aerospace sectors across the Middle East and Africa – specifically, in Egypt, Ethiopia and Afghanistan.
We also observed submissions of ZIP archives with trojanized projects containing NodeRabbit and PollCat to an online multi-scanner originating from several countries, including India, Türkiye, Israel, Iraq, Germany, and Ireland.
Attribution
We attribute this activity to Mirage Kitten with a high degree of confidence based on the following observations:
Structural similarities with the Retrograde/MiniFast native DLL backdoor (MD5:810F8E3B88EB05F710C09552941D6F56)
Initial C2 handshake and session establishment logic. Both PollCat and Retrograde/MiniFast follow a similar C2 handshake flow. Each builds a JSON request body containing host information and sends it via an HTTP POST request. Notably, both treat HTTP 400 as a successful handshake response rather than an error, parsing the response body to extract a socketId, which is then stored and used as the session token for subsequent C2 communication.
Similar C2 handshake and socketId session establishment logic in MiniFast/Retrograde and PollCat
Host registration. Both PollCat and Retrograde/MiniFast register the infected host with the C2 server by sending a structurally similar JSON request body containing the session token and host information.
Command fetching similarities. The similarities extend to command retrieval. Both PollCat and Retrograde/MiniFast periodically poll the C2 server using an HTTP GET request containing the previously assigned socketId as a token. Retrograde/MiniFast uses GET /agent/poll?token=<socketId>, while PollCat follows the same pattern with GET /gate/fetch?token=<socketId>, demonstrating a closely aligned C2 communication structure.
Beacon timing similarities. PollCat and the Retrograde/MiniFast share identical beacon timing defaults: a polling interval of 120,000 ms (0x1D4C0), a jitter of 5,000 ms (0x1388), and a retry timeout of 60,000 ms (0xEA60). This further highlights the structural similarities between the two C2 communication implementations.
Command set similarities. PollCat and Retrograde/MiniFast share several commands and command IDs. Notably, PollCat declares REQUEST_ELEVATION (0xB0) and PERSIST (0xB1) but does not implement them. In MiniFast, both are functional: 0xB0 performs UAC elevation, while 0xB1 creates the WindowsSecurityUpdate scheduled task for persistence.
Command set similarities between MiniFast/Retrograde and PollCat, including shared command identifiers
Proxy authentication similarities. NodeRabbit delegates corporate-proxy NTLM/Negotiate authentication to curl.exe --proxy-anyauth --proxy-user, using the victim’s logon session. Retrograde/MiniFast native DLL implements the same approach natively through WinHttpQueryAuthSchemes and WinHttpSetCredentials with NULL credentials. This shared proxy-aware C2 design suggests the same development approach across both malware families.
Speaking of victimology, the attacks are consistent with Mirage Kitten’s known geographic targeting, with the group maintaining a strong focus on entities across Africa and the Middle East, this time with a particular focus on the aviation and FinTech sectors.
As for the operational infrastructure, Mirage Kitten has historically hosted its initial ZIP lures on legitimate third-party services. Previously, it used onlyoffice.com for this purpose. In this activity, the group shifted to Amazon S3 buckets.
Finally, the combination of Azure Websites and Cloudflare‑backed domains has been a hallmark of Mirage Kitten’s TTPs, which we have observed across NodeRabbit and PollCat.
Conclusions
Mirage Kitten’s latest activity marks a notable evolution in the group’s tooling: NodeRabbit and PollCat are the group’s first Node.js/JavaScript-based implants, departing from its usual native malware deployed through DLL search-order hijacking. The shift to cross-platform scripting gives the operators a single codebase that runs on Windows, Linux, and macOS, with payloads that blend naturally into developer workstations.
The delivery mechanism, however, remains consistent with Mirage Kitten’s historical tradecraft: the use of recruiter personas on LinkedIn to target critical sectors across the Middle East and Africa for cyberespionage purposes. We continue to track the group’s activity and will report on new developments in future publications.
In this Sept. 7, 2016, photo, Dr. David Klein, chief of adolescent medicine at Fort Belvoir Community Hospital and an Air Force Major, listens to the heartbeat of Jenn Brewer, 13, at her monthly doctors appointment visit for monitoring of her treatment at the Pediatric Clinic at Fort Belvoir Community Hospital in Fort Belvoir, Va. Brewer is transitioning from male to female. Starting Oct. 3, the military’s health insurance will cover transgender-related services that include hormone therapy and supportive counseling. (AP Photo/Jacquelyn Martin)
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