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ClawSandbox – 7/9 attacks succeeded against an AI agent w/ shell access

by ariansyah·Mar 4, 2026·3 points·4 comments
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AI Analysis

●●●BangerBig BrainSolve My ProblemZero to One

First systematic attack framework proving 7/9 exploits work on AI agents with shell access.

Strengths
  • •Generalizable methodology: tests apply to AutoGPT, LangChain, Cursor, Devin—any agent with code execution, not just OpenClaw.
  • •Real attack surface: prompt injection, memory poisoning, privilege escalation, data exfiltration—maps to actual LLM Top 10 vulnerabilities.
  • •Published results with rigor: 7/9 succeeded against Gemini 2.5 Flash + OpenClaw; actionable findings instead of speculation.
Weaknesses
  • •GitHub page truncated—missing implementation details, Docker setup clarity, and how to 'bring your own agent' reproducibly.
  • •Narrow immediate applicability: useful for agent builders and security researchers, but risk surface shrinks as agents get better isolation.
Category
Security
Target Audience

AI/ML engineers, security researchers, developers building autonomous agents with code execution

Similar To

OWASP LLM Top 10 · Prompt Injection benchmarks (HuggingFace, Anthropic's red-teaming) · Container escape test suites

Similar Projects

Security○Pass

Synesthetic Computation

The author walks the reader through a full exploit chain that starts with a UX/trust-boundary trick and ends in RCE by causing a client to connect to an attacker gateway, leak a token, and reconfigure the agent’s execution environment. It's a sharp systems narrative that will change how you think about agents crossing chat, browser, and local tooling — excellent reading for defenders and attacker-minded engineers, but it's an investigative article rather than a ship-or-tool.

WizardryRabbit Hole
agamrafaeli
104mo ago