New fuzzing technique discovers over 1,000 unintended LLM agent intents

Researchers have developed SkillFuzz, a novel approach to identify unintended objectives, or "implicit intents," that can arise from the composition of multiple skills in large language model (LLM)-based agents. This method treats skill composition discovery as a fuzzing problem, using planning artifacts to expose agent intent before execution and a skill-free baseline as an oracle. SkillFuzz employs Monte Carlo Tree Search to prioritize potentially conflicting skill combinations, successfully discovering over 1,000 distinct implicit intents and validating a high percentage of high-risk compositions. AI

IMPACT This research could improve the safety and reliability of LLM agents by identifying potential unintended behaviors before deployment.

RANK_REASON The cluster contains a research paper detailing a new method for testing LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• large language model
• Monte Carlo Tree Search
• SkillFuzz

• paper
• safety

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