When Safety Checks Become Privacy Risks: A Privacy-Preserving Paradigm for LLM Agent Safety Auditing
Safety checks that inspect LLM agent tool calls before execution can themselves expose private data — this paper formalises that paradox and proposes a privacy-preserving auditing paradigm.
Summary written by editorial AI · Source link below
arXiv:2610.41401v1 Announce Type: new Abstract: Large language model agents interact with online services through tool calls that may access private data or perform sensitive actions. Specialized providers can supply and maintain pre-execution safety auditing capabilities for agent operators. However, sending task descriptions, call arguments, and interaction histories to an external auditor may expose sensitive user information. In this work, we propose a paradigm for privacy-preserving safety
Editorial Analysis
Enterprises deploying LLM agents with safety-auditing layers may inadvertently create a new data-leakage vector through the very controls meant to prevent harm.
Review whether your LLM agent safety-checking layer exposes sensitive data to third-party auditing services and evaluate privacy-preserving alternatives.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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