OverAct: Measuring and Mitigating Proactive Over-Authorization in LLM Tool-Calling Agents
OverAct finds LLM tool agents routinely access more private data than requests authorize.
OverAct is a judge-free benchmark of proactive over-authorization in structured tool-calling agents across eight privacy-sensitive domains. All seven models from four families significantly exceeded authorized scope; request specificity best predicted severity, while tool-pool size had a sublinear effect and temperature little effect. SelfAudit, a zero-shot filter that drops calls lacking a request-grounded justification, cut privacy-oriented excess by 43% without oracle knowledge.
- Benchmark covers eight privacy-sensitive tool-calling domains
- Seven models all exceeded the user's authorized data scope
- Specificity predicts severity more than temperature or pool size
- SelfAudit cuts excess privacy access by 43% before execution
From the original article
1 min read at arxiv.orgLLM agents with tool-calling capabilities can access external services and private user data, but they may retrieve more information than a user's request explicitly requires. We study this behavior in structured tool-calling agents and term it proactive over-authorization. This setting differs from filesystem-level coding agents because the main risk is unnecessary access to private data.
By Taolin Zhang · arxiv.org
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