PrivacyPeek: Auditing What LLM-Based Agents Acquire, Not Just What They Say
LLM-based agents are rapidly advancing, autonomously invoking external tools to complete multi-step tasks for users. However, agents often acquire more sensitive information than the task requires. Existing privacy benchmarks audit what the agent's response or outgoing actions disclose, but overlook the acquisition stage where data first enters the agent's context. The over-acquired information is then one careless action or one attack away from an outright leak. To assess its prevalence, we introduce \emph{PrivacyPeek}, a benchmark for evaluating acquisition-stage privacy leakage of LLM-based agents, with $1{,}182$ cases across $7$ acquisition behaviours and $16$ application domains. Spe...
| Authors | Mingxuan Zhang, Jiahui Han, Dadi Guo et al. |
|---|---|
| Primary category | cs.CR |
| Published | 29 May 2026 |
| Last updated at source | 6 August 2026 |
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