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AgentBaiting: How Fake AI Skills Deliver Malware at Scale

The FakeGit campaign, detailed by Island security researcher Oleg Zaytsev, uses roughly 7,600 malicious GitHub repositories—over 800 posing as AI Skills or MCP servers—to deliver SmartLoader malware, which establishes persistence and installs the StealC information stealer. Researchers coined the technique 'AgentBaiting,' where AI agents like Claude Code, Gemini, and ChatGPT autonomously discover the attacker repositories, treat the malicious READMEs as legitimate documentation, and hand installation instructions to users; the operation recorded over 14 million downloads and peaked in April 2026.

supply-chain · tool-abuse · malware-distribution · agent-baiting
mcp · ai-agents · llm · github

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Miasma Supply Chain Attack: Azure Hit, 73 Repos Down, 37 PyPI Wheels

The Miasma supply-chain worm campaign expanded to poison Microsoft's Azure/durabletask GitHub repository with config files that execute a credential-harvesting payload the moment a developer opens the repo in AI coding tools like Claude Code, Gemini CLI, Cursor, or VS Code, triggering GitHub to disable 73 Microsoft repositories including Azure/functions-action. Two days later the Hades variant dropped 37 malicious PyPI wheels across 19 packages using Python .pth startup hooks to run a Bun-powered credential stealer on every interpreter launch, with Socket tracking 448 affected artifacts across npm and PyPI. Details →

Your Agent Risk Isn't in One Plugin. It's in the Composition.

OpenACA analyzes how agentic AI risk emerges from the composition of plugins, MCP servers, skills, and npm dependencies rather than any single component, using the Claude Code 'imessage' plugin as an example (a local MCP server reading chat.db, sending iMessages, with skills that rewrite access policy). They scanned the official Claude plugin marketplace (62 manifests, 530 components) and found all 124 known-vulnerability advisories clustered in the four message-channel plugins (discord, telegram, fakechat, imessage) that ingest untrusted input and can exfiltrate local files, arguing SCA and runtime monitoring miss this composition-level exposure. Details →
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