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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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Breaking Claude Code Opus 5 Auto Mode

Johann Rehberger (Embrace The Red) demonstrated an indirect prompt injection attack chain that hijacks Claude Code Opus 5 in Auto Mode via a simple 'summarize this website' request, achieving code execution with a 60-80% success rate. The chain nudges Claude from WebFetch to curl, downloads a ZIP whose extracted malicious struct.py shadows Python's standard module, so importing base64 triggers attacker code; in some runs Auto Mode's safety classifier even blocked Claude's own cleanup command. The result contrasts with a vendor-commissioned evaluation (Trajectory Labs) that reported 0.00% attack success for Opus 5 in Auto Mode. 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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