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

Summary

The paper proposes a privacy-preserving paradigm for pre-execution safety auditing of LLM agent tool calls, separating delegated audit computation from plaintext access and execution authority. Its instantiation, SealCall, combines learned audit representations, CKKS-based encrypted similarity computation, and execution control bound to audited tool calls. On 1,252 public binary candidates it reaches a mean F1 of 0.840 under grouped nested evaluation, and one configuration completes each encrypted scoring operation in 0.176 s.

Topics

• Training data and privacy