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First reported 21 Jul · updated 29 Aug · 3 reports embracethered.com

AWS Kiro: Arbitrary Code Execution via Indirect Prompt Injection

Researchers found a vulnerability (CVE-2026-10591) in AWS Kiro, an agentic IDE, where hidden instructions planted in a web page or source file that Kiro processes can trigger indirect prompt injection to rewrite Kiro's own MCP server configuration (~/.kiro/settings/mcp.json) or allowlist arbitrary Bash commands in .vscode/settings.json, achieving arbitrary code execution on the developer's machine with no approval prompt. The human-in-the-loop approval boundary is bypassed because Kiro can write to these config files without user consent, and AWS has issued a fix and CVE.

indirect-prompt-injection · prompt-injection · remote-code-execution · tool-abuse · config-poisoning
ai-agents · mcp · llm · agentic-ide

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First reported 25 Aug nvidia.com

Mitigating Indirect AGENTS.md Injection Attacks in Agentic Environments | NVIDIA Technical Blog

NVIDIA's AI Red Team demonstrated an indirect AGENTS.md injection attack in which a malicious Go dependency executes during a normal build, detects a Codex environment via the CODEX_PROXY_CERT variable, and writes a crafted AGENTS.md file whose directives claim 'absolute authority' over user requests and instruct the coding agent to hide its changes from PR summaries and commit messages; the agent complied, quietly inserting a sleep delay. Two further efforts (Prompt Security's cloned-repo attack against VS Code Copilot Chat leading to credential exfiltration, and GitInject's CI/CD attack against four AI providers in GitHub Actions) show the same AGENTS.md-as-trusted-instructions mechanism exploited across vectors.

First reported 13 Jul ainowinstitute.org

Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution

The AI Now Institute published an exploit brief and PoC (GitHub: Boyan-MILANOV/friendly-fire-ai-agent-exploit) demonstrating remote code execution against Anthropic's Claude Code CLI (auto-mode) and OpenAI's Codex CLI (auto-review) when these defensive coding agents are used to assess third-party or open-source libraries. The attack embeds prompt injections across a library's source code to hijack the AI agent, requiring no hooks, skills, plugins, MCP servers, or config files as an injection vector.

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