Researchers have developed a new tool called AgentSeer to evaluate the vulnerabilities of large language models (LLMs) when they are deployed in agentic systems. This tool decomposes agent executions into action-component graphs, revealing a significant gap between model-level and agent-level risks. The study found that agentic-only vulnerabilities, particularly those involving tool-calling, are more prevalent than traditional model-level risks, with tool-calling increasing jailbreak success rates by 24-60%. The research also demonstrated that iterative attacks are more effective in agentic contexts than direct attacks, and proposed an action-based prompt improvement method to mitigate these vulnerabilities. AI
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IMPACT Introduces a novel framework for assessing agentic LLM vulnerabilities, potentially improving safety evaluations for deployed AI systems.
RANK_REASON Academic paper introducing a new evaluation methodology and tool for LLM safety.