Extracting Knowledge from Tools in LLM Agents
Research exposes how adversaries can extract proprietary knowledge from tool-augmented LLM agents, adding a data-leakage vector enterprises must address when deploying agentic AI.
Summary written by editorial AI · Source link below
arXiv:2608.30288v1 Announce Type: new Abstract: LLM agents commonly use knowledge-based tools and access their underlying files, databases, and search indexes through tool invocation. This integration improves agents' ability to provide domain-specific services but also introduces the risk of tool-mediated knowledge extraction: source content exposed to an agent for legitimate responses may be progressively recovered from its outputs, enabling reconstruction of the knowledge source behind a tar
Editorial Analysis
Enterprises connecting LLM agents to internal knowledge bases risk unintended data exposure through carefully crafted queries exploiting tool interfaces.
Implement output-sanitisation and rate-limiting on all tool APIs exposed to LLM agents handling sensitive enterprise data.
LLM agents connected to internal knowledge stores can leak proprietary data through adversarial queries — access controls must be enforced at the tool layer.
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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