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arXiv AI By Yixiang Luo, Ziheng Ma, Ke Chang, Jian Wang, Jiqiang Liu

When Safety Checks Become Privacy Risks: A Privacy-Preserving Paradigm for LLM Agent Safety Auditing

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The paper introduces a privacy‑preserving safety auditing paradigm for large language model agents that interact with online services via tool calls. It separates audit computation from plaintext access, using trusted representation construction, encrypted audit computation, and trusted decision interfaces to protect sensitive user data. The authors implement this paradigm in SealCall, which employs learned audit representations and CKKS‑based encrypted similarity computation, achieving a mean F1 score of 0.840 on 1,252 public binary candidates while maintaining plaintext audit decisions and completing encrypted scoring in 0.176 s.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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