ContextLeak: Exfiltrating LLM Agent Context via Malicious Tools
ContextLeak demonstrates how a single malicious tool in an LLM agent's toolkit can silently exfiltrate user prompts, execution traces, and tool inventories — a supply-chain risk enterprises adopting agentic AI must address now.
Summary written by editorial AI · Source link below
arXiv:2608.27800v1 Announce Type: new Abstract: Exfiltrating an LLM agent's runtime context -- such as the user prompt, execution trajectory, and tool list -- poses severe security and privacy risks to users. Such attacks can be carried out via malicious tools and typically require three conditions: (1) the agent selects the malicious tool for task execution, (2) the agent passes its runtime context as input arguments to the tool, and (3) the tool's implementation transmits these inputs to an a
Editorial Analysis
Agentic AI adoption is accelerating, but tool-supply-chain attacks can expose confidential prompts and business logic. Enterprises need tool-integrity controls before scaling LLM agent deployments.
Establish a tool-vetting and allow-listing policy for all integrations in LLM agent ecosystems.
Malicious tools in AI agent pipelines pose a new data-exfiltration risk that warrants supply-chain controls before scaling agentic AI.
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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