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🧠 AI🔴 BearishImportance 7/10Actionable
Credential Leakage in LLM Agent Skills: A Large-Scale Empirical Study
arXiv – CS AI|Zhihao Chen, Ying Zhang, Yi Liu, Gelei Deng, Yuekang Li, Yanjun Zhang, Jianting Ning, Leo Yu Zhang, Lei Ma, Zhiqiang Li|
🤖AI Summary
A large-scale study of 17,022 third-party LLM agent skills found 520 vulnerable skills with credential leakage issues, identifying 10 distinct leakage patterns. The research reveals that 76.3% of vulnerabilities require joint analysis of code and natural language, with debug logging being the primary attack vector causing 73.5% of credential leaks.
Key Takeaways
- →520 out of 17,022 analyzed LLM agent skills contained credential leakage vulnerabilities across 1,708 identified issues.
- →Debug logging through print and console.log statements caused 73.5% of credential leaks due to stdout exposure to LLMs.
- →76.3% of leakage vulnerabilities require cross-modal analysis of both code and natural language components.
- →89.6% of leaked credentials were exploitable without requiring special privileges, making them highly dangerous.
- →After disclosure, all malicious skills were removed and 91.6% of hardcoded credentials were successfully fixed.
#llm-security#credential-leakage#ai-agents#vulnerability-research#prompt-injection#third-party-skills#debug-logging#security-disclosure
Read Original →via arXiv – CS AI
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