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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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NVD - CVE-2026-42271

CVE-2026-42271 is an OS command injection flaw (CWE-78, CVSS 8.8) in LiteLLM, a proxy server / AI Gateway for LLM APIs. Two endpoints used to preview an MCP server before saving accept a full server configuration including command execution parameters, letting an authenticated user — even one with low-privilege internal-user keys — send a crafted config to run arbitrary commands on the proxy host with the proxy process's privileges. Red Hat rates it Important, it affects OpenShift AI and Ansible Automation Platform, is fixed in LiteLLM v1.83.7-stable, and CISA lists it with known public exploits. Details →

(A)I Sees What You Don't: Exploiting New Attack Surfaces in Third-Party Mobile Agents

Researchers from Simon Fraser University, CUHK, Shandong University, and QAX's Xingtu Lab published an arXiv paper (arXiv:2607.00333) demonstrating seven concrete attacks against five open-source mobile AI agent frameworks (AppAgent, AppAgentX, Mobile-Agent-v3, Open-AutoGLM, and MobA). A malicious Android app without privileged permissions can slip invisible on-screen text that the VLM-driven agent reads and acts on, exploiting a 'Screen Perception' surface (human-vs-machine vision gap) and a 'Misused Channel' surface to hijack agent actions and even achieve arbitrary command execution on the host PC driving the agent. All five frameworks fell to at least six of the seven attacks; no CVE was assigned and authors report no evidence of in-the-wild use. Details →

A Security Analysis of the OpenClaw AI Agent Framework

Researchers detailed three now-patched high-severity flaws (GHSA-hjr6-g723-hmfm, GHSA-9969-8g9h-rxwm, GHSA-575v-8hfq-m3mc; CVE-2026-46817, CVE-2026-55200) in the OpenClaw personal AI assistant framework that compose into a complete unauthenticated remote code execution path — from an LLM tool call triggered via a WhatsApp message to command execution on the host. The chain abuses OS command injection and an incomplete disallowed-input filter in the exec allowlist, whose closed-world lexical parsing is defeated by shell line continuation, busybox multiplexing, and GNU option abbreviation; a companion arXiv analysis taxonomizes 470 advisories and shows a malicious plugin skill bypassing the exec pipeline entirely. Details →
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