SkillFuzz: Fuzzing Skill Composition for Implicit Intents Discovery in Open Skill Marketplaces
arXiv:2607.02345v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents increasingly automate software engineering tasks through reusable skills, natural-language instruction documents that guide planning and execution. Open skill marketplaces enable users to assemble agents by co-activating community-contributed skills, but marketplace operators typically audit skills in isolation. As a result, individually benign skills may interact to redirect an agent toward unintended objectives, which we term implicit intents. Detecting such intents is challenging because the effect eme
The rapid proliferation of LLM-based agents and open skill marketplaces necessitates methods for ensuring the safety and intended behavior of composite AI systems, as agents move from theoretical to practical deployment.
This development addresses a critical vulnerability in autonomous AI agents, where interactions between individually benign skills can lead to unintended and potentially harmful outcomes, impacting trust and adoption.
The ability to proactively identify implicit intents within composite AI skill sets allows marketplace operators and developers to build more secure and predictable agentic systems.
- · AI Agent Developers
- · Skill Marketplace Operators
- · Cybersecurity Firms
- · Enterprise AI Adopters
- · Malicious Skill Developers
- · Unaudited AI Skill Marketplaces
Improved safety and reliability of AI agents due to better detection of emergent, unintended behaviors.
Increased trust in open AI skill marketplaces, fostering greater innovation and deployment of agentic systems across industries.
The development of a new sub-field within AI security focused on 'skill composition auditing' and 'implicit intent discovery' for autonomous agents.
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