How Much Can Tool Calls Reveal About You? Privacy Leakage from Tool-Call Traces
Researchers show that LLM agent tool-call traces via protocols like MCP can leak user behavioral signals—a finding with direct GDPR and EU AI Act implications for enterprises operating AI agents at scale.
Summary written by editorial AI · Source link below
arXiv:2610.41885v1 Announce Type: new Abstract: Large language model (LLM)-based agents interact with external tools through protocols such as the Model Context Protocol (MCP), producing tool-call traces that may expose behavioral signals about users. In this work, we systematically study the risk of user-profile inference from MCP tool-call traces, where observers infer user attributes from protocol-valid tool interactions without access to user conversations. We introduce PrivacyTrace, a benc
Editorial Analysis
As enterprises adopt MCP-style agent protocols, unexamined tool-call traces could create a new category of personal-data exposure under GDPR and the EU AI Act.
Conduct a data-protection impact assessment on LLM agent tool-call logging to identify and mitigate behavioral-signal leakage.
AI agent tool-call logs may inadvertently expose user data, creating regulatory risk under GDPR that warrants proactive review.
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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