When Should Agent Trust Be Conditional? Characterizing and Attacking Skill-Conditional Reputation in Agent Swarms
Quick Answer
This study introduces skill-conditional trust R(i | k) for heterogeneous LLM agents, revealing that conditional trust is beneficial under high agent diversity and sparse evidence.
Quick Take
However, attackers can exploit this system, leading to significant routing errors, with a potential regret increase from 0 to 0.94, despite a zero-cost trust rating of +0.19 being contaminated to -0.06.
Key Points
- Conditional trust is advantageous in high heterogeneity and sparse evidence scenarios.
- Attackers can hijack the conditional router, causing routing regret to spike.
- The study uses a benchmark of 14 heterogeneous AppWorld agents.
- Cross-skill evidence borrowing can lead to trust contamination.
- A zero-evidence gate limits but does not eliminate the attack risk.
Paper Resources
Source Excerpt
arXiv:2606. 14200v1 Announce Type: new Abstract: Open platforms increasingly route tasks among heterogeneous agents--differing in base model, scaffold, and tool stack--whose competence varies sharply by skill: an agent excellent at one skill may be useless at another. The standard reputation approach summarizes each agent by a single global trust score, but that scalar is the wrong object here, because routing every task to the globally most-trusted agent leaves the value of specialization unclaimed. …
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