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AgentBaiting: How Fake AI Skills Deliver Malware at Scale

Island security researchers uncovered the FakeGit campaign, which used roughly 7,600 malicious GitHub repositories—over 800 posing as AI Skills or MCP servers—to deliver SmartLoader malware that installs the StealC information stealer. The campaign introduces a technique called AgentBaiting, in which AI agents such as Claude Code, Gemini, and ChatGPT autonomously discover the attacker repositories, treat the malicious README as legitimate documentation, and pass installation instructions to users; the operation recorded over 14 million downloads.

supply-chain · tool-abuse · malware-delivery · agentbaiting
mcp · ai-agents · ai-skills · llm · copilot

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AWS Kiro: Arbitrary Code Execution via Indirect Prompt Injection

AWS Kiro, an agentic coding IDE, was vulnerable to arbitrary command execution via indirect prompt injection: hidden text on a web page (or a comment in a source file) processed by the agent could make Kiro use its no-approval fsWrite tool to rewrite ~/.kiro/settings/mcp.json (or allowlist all Bash commands in .vscode/settings.json), causing it to launch attacker-specified MCP servers/commands and achieve RCE on the developer's machine, bypassing the human 'allow' approval boundary. Discovered by Intezer with Kodem Security and independently by Embrace The Red (Johann Rehberger); AWS has patched the issue. Details →

Refused in Chat, Written in Code: Workflow-Level Jailbreak Construction in IDE Coding Agents

Alan Turing Institute researchers Abhishek Kumar and Carsten Maple demonstrated a "workflow-level jailbreak construction" against GitHub Copilot in VS Code, showing that harmful requests refused in direct chat succeed when decomposed across ordinary multi-turn IDE coding tasks. Across 204 prompts from Hammurabi's Code, HarmBench, and AdvBench, four closed-weight backends (Claude Sonnet 4.6, Claude Haiku 4.5, Gemini 3.1 Pro, Gemini 3.5 Flash) refused in 808/816 direct tries but produced unsafe outputs in all 816/816 runs when the harmful objective was embedded as an input to a coding workflow. Details →

We put the exploit in a picture. Your AI code reviewer never opens it.

Researchers from the University of Missouri-Kansas City's ASSET Research Group demonstrated 'Ghostcommit,' an attack that hides malicious prompt-injection instructions inside a PNG image so AI code reviewers (CodeRabbit, Cursor Bugbot) never see them. A benign-looking AGENTS.md convention file points to build-spec.png, whose rendered text instructs a coding agent to read the repo's .env byte-by-byte and emit the secrets as an integer tuple; the payload sits dormant until an unrelated agent session triggers exfiltration. A proof-of-concept is published on GitHub and the findings were disclosed to affected vendors. Details →

Agent Data Injection Attacks are Realistic Threats to AI Agents

A research paper by Woohyuk Choi and colleagues introduces agent data injection attacks (ADI), a new category of indirect prompt injection in which malicious data is disguised as trusted data (such as security-critical metadata or agent context data like tool call/response formats) rather than as instructions. The authors demonstrate ADI against real-world agents, achieving arbitrary click attacks on web agents (Claude in Chrome, Antigravity, Nanobrowser) and remote code execution plus supply-chain attacks on coding agents (Claude Code, Codex, Gemini CLI), showing it bypasses existing IPI defenses because agents fail to isolate trusted from untrusted data. Details →

The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities

A systematization-of-knowledge paper by Mohammadreza Rashidi organizes 39 works (2023-2026) on execution security for AI coding agents into 17 categories covering sandbox isolation, capability/access control, policy enforcement, TOCTOU races, and MCP threats, and verifies four disclosed, patched CVEs affecting production agent harnesses. It surfaces five cross-cutting gaps, including denylist policy failure rates of 69-98% and benign out-of-scope agent actions occurring at rates up to 17.1% under realistic prompting. Details →
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