Hispasec•July 21, 2026•🇪🇸Translated from Spanish

FakeGit Campaign Floods GitHub with 7,600 Fake Repositories to Distribute SmartLoader Malware

The FakeGit campaign has turned GitHub into a massive storefront of deceptive repositories aimed at distributing SmartLoader, a loader designed to install follow-on malware such as the StealC infostealer. The operation, active at least until July 2026, has created or repurposed nearly 7,600 repositories and employs a sophisticated mix of cloned legitimate projects, realistic documentation, and fake developer profiles to appear trustworthy.

Visitors are guided to download ZIP archives from GitHub Releases, a channel often perceived as more reliable. Approximately 200 of these repositories have recorded more than 14 million downloads, demonstrating the campaign’s significant reach. In roughly 800 cases the repositories masquerade as AI-related utilities, specifically presented as Skills or MCP (Model Context Protocol) servers.

This approach enables the AgentBaiting technique, in which attackers do not need to send direct links; instead, they rely on AI agents searching for free skills or MCP servers and subsequently following installation instructions that execute malicious code. Internal tests have shown that systems including Anthropic Claude Code, Google Gemini, and OpenAI ChatGPT can surface malicious repositories during discovery.

The infection chain begins with the downloaded ZIP, which triggers a staged payload executed through LuaJIT and culminating in an obfuscated Lua script that installs SmartLoader. The loader maintains persistence on Windows via scheduled tasks and resolves its command-and-control infrastructure dynamically using a blockchain-based dead drop referencing a smart contract on Polygon.

Exposure is further amplified by more than 600 listings appearing on public directories such as LobeHub, Glama, MCP.so, and MCP Market. Organizations are recommended to avoid executing unverified ZIP files or scripts, maintain internal catalogs of approved AI components, apply application control policies, and monitor for LuaJIT execution and anomalous network activity including direct IP connections or Polygon RPC endpoints.

Related articles

Habr•Supply Chain & Open Source

Security Researcher Builds SAST Scanner for AI-Generated Code and Audits 3,800 Public Repositories

A developer released AigisSAST, a lightweight open-source static analysis tool written in pure Python with no external dependencies, specifically tuned to detect common mistakes made by AI coding assistants. The scanner was run across roughly 3,800 repositories ranging from small pet projects to popular open-source platforms. It identified thousands of potential secrets and misconfigurations, yet manual review reduced the number of genuine leaks to approximately 30 cases, mostly Telegram bot tokens, database credentials, and committed .env files. The project also examined 471 production-grade Telegram bots handling payments and VPN services, uncovering 31 repositories that exposed real credentials either in current code or in Git history. AigisSAST includes 21 detection rules, 193 regression tests, automatic remediation via the fix command, and seamless integration with GitHub Actions. The author deliberately avoided validating any discovered keys to stay within ethical research boundaries.

Habr•Supply Chain & Open Source

Vendor Responsibility in Open Source: Licensing Obligations Exposed by Sonatype Nexus Changes

The article examines how vendors building products on copyleft open source projects like Nexus Repository OSS inherit significant legal and security responsibilities under licenses such as EPL 1.0. Sonatype's February 2025 shift from regular OSS binary releases to a limited Community Edition forces downstream vendors to handle their own builds, patch porting, and compliance disclosures. This change highlights the second part of copyleft licenses that outlines obligations for distributors, including revealing modifications and assuming liability for the final product. Security implications arise because critical vulnerabilities in the upstream project must now be tracked and patched by the vendor, with delays creating measurable supply chain risks. The piece provides a practical checklist for buyers to assess licensing hygiene, SBOM availability, and vulnerability response times in any open source-based solution.

AntiMalware•Supply Chain & Open Source

PhantomSub Campaign Deploys 101 Malicious npm Packages to Hijack WhatsApp Accounts for Unauthorized Channel Subscriptions

Researchers at OX Security uncovered 101 malicious npm packages tied to the PhantomSub campaign that abuse connected WhatsApp accounts to subscribe users to promotional channels without consent. The packages disguise themselves as modified versions of the open-source Baileys library used for WhatsApp automation. Attackers rely on authenticated sessions rather than simple package installation, allowing them to control subscriptions through lists stored on GitHub, in plaintext, or as encoded identifiers. The packages have accumulated roughly 490,000 downloads, including 116,000 in the past 30 days, though the exact number of compromised accounts remains unknown. As of 28 September, npm had removed only 16 of the identified packages. The operation ultimately benefits channels selling bots, game resources, accounts, and promotion services by inflating subscriber counts while disabling notifications to hide the activity.

Habr•Supply Chain & Open Source

AI Model Hallucinations Fuel Slopsquatting Attacks on PyPI and npm Registries

Researchers identified 139 package names consistently hallucinated by five different AI models across Python and JavaScript ecosystems. Seven of these names are already registered on PyPI and npm, including one previously used to distribute malware. The attack vector, termed slopsquatting, allows attackers to register AI-suggested package names and execute code with developer privileges during installation. One package, metro-evaluator, contained malicious code removed by npm in December 2025, while another empty package css-color-stop began receiving downloads after the list was published. Real projects such as odf and lusid now occupy names that AI models recommend, causing developers to install unrelated software. Studies show hallucination rates between 4.62% and 21.7% depending on the model, with commercial models performing better than open-source ones. The findings highlight risks when AI coding agents execute dependency installation commands without human verification.