Security researchers have uncovered a stealthy malware campaign that leverages fake OSINT GitHub repos and deceivingly useful developer tools to spread a previously undocumented JavaScript-based remote access trojan called PyStoreRAT. These malware distribution tactics rely on presenting seemingly legitimate code often themed as OSINT utilities, GPT wrappers, or DeFi bots to lure developers, analysts, and even cybersecurity professionals into executing malicious payloads.
Threat actors behind this campaign create or resurrect dormant GitHub accounts and publish polished, attractive repositories designed to gain traction. In many cases, these tools climb onto GitHub’s trending lists and spread via social media platforms like YouTube and X, creating an illusion of legitimacy before triggering the malware’s multi-stage execution chain.
Deceptive Loader Stubs Embedded in Code
The core component that drives this infection is a deceptively small sniper-like loader stub written in Python or JavaScript. At first glance, the code base appears innocuous — simple utility functions tied to OSINT collection or GPT utilities. However, once a developer or analyst executes the code, the stub silently downloads and launches a remote HTA file that acts as the first stage of PyStoreRAT’s execution.
This technique relies on minimal local footprint and heavy dependence on dynamic execution. Attackers make extensive use of ‘mshta.exe’ a Windows system tool launching it via cmd.exe when certain antivirus signatures like Falcon (CrowdStrike) or Reason (Cybereason/ReasonLabs) are absent. This evasion technique helps reduce detection and prolong dwell time on the compromised host.
Multi-Stage Payload Execution and Persistence
Once executed, PyStoreRAT switches into a multi-stage mode capable of a wide variety of actions:
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It may download and execute additional EXE payloads, including stealers or credential harvesters like Rhadamanthys.
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It can unpack ZIP archives containing malicious modules.
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It can invoke rundll32.exe to load DLL modules dynamically.
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The RAT may fetch raw JavaScript code and execute it in memory using eval(), making static detection very difficult.
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It supports PowerShell execution in memory a classic technique to evade endpoint controls.
Persistence on the host is achieved through a cleverly disguised scheduled task that mimics an NVIDIA App SelfUpdate process. This persistence mechanism helps embed the malware deeply into the host’s startup routine without arousing user suspicion.
Growing Trend of Open-Source Platform Abuse
Threat actors have long abused open-source repositories for malicious distribution, but this campaign illustrates how attackers now exploit not just trust in code but developers’ confidence in GitHub as a platform. Prior research into malicious GitHub behavior has documented extensive abuse of repositories to spread malware, credential theft, and supply-chain attacks, with attackers using AI tools and deceptive tactics to make repositories appear trustworthy while hiding harmful logic.
The Stargazers Ghost Network and similar schemes have previously deployed thousands of fake accounts and manipulated repository metrics to boost visibility for malicious projects a method that aligns with the techniques seen in the current PyStoreRAT campaign.
Why This Matters to Developers and Defenders
This abuse of trusted development platforms represents a significant escalation of supply-chain-adjacent malware tactics. Developers who rely on open-source tools must now treat external code with deeper skepticism — especially when an OSINT tool or utility library begins to act as a loader for an unseen payload.
From a defender’s perspective, these infections show that attackers can blend into legitimate development workflows, requiring more rigorous vetting of third-party dependencies and enhanced code review processes.
Defensive Measures and Threat Hunting Strategies
Security teams and SOCs should apply multiple defensive layers to address threats originating from fake OSINT GitHub repos:
Vet All Third-Party Code Before Use
Before integrating external tools into a project or running code locally, conduct automated and manual code reviews. Look for suspicious loaders, calls to external scripts, and references to system utilities like mshta.exe or eval() — each can be an early sign of malicious intent.
Integrate Repository Threat Intelligence
Use feeds that monitor malicious GitHub activities and false trending signals; encrypt telemetry to detect sudden spikes in anonymous repo activity that don’t match project history or contributor reputation.
Monitor Endpoint Behavior for Executable Downloads
Malware like PyStoreRAT often pulls code from external servers post-execution. Correlate endpoint detection logs with unusual downloads or unexpected scheduled tasks.
Enforce Execution Control Policies
Restrict execution of unapproved installers, HTA files, PowerShell scripts, and in-memory JavaScript execution on enterprise endpoints. Restrict mshta.exe and similar system tools unless explicitly required.
Zero-Trust and Least Privilege Practices
Adopt strict access policies for developers and CI/CD environments to limit the blast radius of infected workstations and protect build systems from downstream contamination.
FAQs
Q: What are “fake OSINT GitHub repos”?
A: These are malicious repositories hosted on GitHub that masquerade as legitimate open-source intelligence tools or utility projects. They contain minimal loader code that, when executed, pulls down and runs malware such as PyStoreRAT.
Q: What is PyStoreRAT?
A: PyStoreRAT is a modular remote access trojan (RAT) that can execute various payloads, including EXEs, DLLs, PowerShell scripts, and JavaScript, enabling adversaries to control systems, harvest information, and maintain persistence.
Q: How do attackers make these repos seem legitimate?
A: Attackers employ AI-generated documentation, polished visuals, inflated stars/forks, and deceptive project descriptions. They also revive dormant accounts or mimic trending technologies so unsuspecting developers trust and run the code.
Q: What defenses help against GitHub-based malware spread?
A: Effective defenses include rigorous code vetting, threat intelligence integration, execution policy enforcement, and endpoint monitoring for unusual activity such as unknown scheduled tasks or external code downloads.
