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arXiv cs.AI· Wei Zhu, Meiyun Zuo, Huiping Sun·· 2 天前AI 评分38

Blindfold:让 LLM 智能体无法察觉审计抛硬币结果

Blindfold: Keeping the Audit Coin Secret from LLM Agents

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研究提出审计式认证的有效性还依赖一项保密属性——审计抛硬币结果必须在动作提交前不干扰智能体视野,称为 pre-commit noninterference(PNI),其违反程度用两种硬币取值下智能体视野的全变差距离 D_A 衡量。

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Abstract:Audit-based certification bounds the violation rate of an LLM agent by auditing a random fraction $\varepsilon$ of actions and reweighting the audited labels; its validity is meant to rest only on the defender's audit coin. We show that it also rests on a confidentiality property: the coin must not interfere with the agent's view before the action commits, at a time the agent chooses. We call this pre-commit noninterference (PNI) and measure its violation by $D_{\mathrm{A}}$, the total-variation distance between the agent's views under the two coin values. PNI is necessary and sufficient for an unbiased certificate against adaptive agents, the worst-case bias is $(1-\varepsilon)D_{\mathrm{A}}$, and no procedure that sees only audited labels can pay less. Leakage composes across steps, a resource shared across episodes opens a channel of computable size, and raising the audit rate cannot restore validity. On a monitor we built, a silent audit changes no output but adds latency that tool timestamps expose, and the agent can enlarge it. A real LLM agent follows an injection that names a visible audit notice (generic, tool, and timing clauses have no effect); over that channel the plain certificate fails on measured labels and, by a post-hoc analysis, in a live run on a pre-registered schedule, while a silent audit restores it. A fresh coin per audited call closes the timing channel; padding only narrows it.
Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2610.37055 [cs.CR]
  (or arXiv:2610.37055v1 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2610.37055

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Huiping Sun [view email]
[v1] Tue, 27 Oct 2026 11:24:12 UTC (66 KB)

来源:arXiv cs.AI · arxiv.org