Context Inference Attacks Without Jailbreaks
Researchers show that agentic AI systems can leak sensitive context data — healthcare records, financial documents — without any jailbreak, challenging the assumption that alignment alone protects inference-time confidentiality.
Summary written by editorial AI · Source link below
arXiv:2609.01663v1 Announce Type: new Abstract: Agentic AI systems are increasingly deployed to process sensitive data at inference time, such as healthcare records or financial documents assembled into a hidden \emph{context} before the system answers. Prior work has studied privacy risks primarily through \emph{jailbreaking} attacks that induce models to directly disclose sensitive content, but has largely overlooked the agentic setting where the context is assembled by the agent's own tool c
Editorial Analysis
Enterprises feeding confidential data into agentic LLM pipelines face a newly demonstrated exfiltration path that bypasses safety alignment, creating direct GDPR and trade-secret exposure.
Conduct a red-team exercise specifically targeting context-inference extraction on any agentic AI system processing sensitive enterprise data.
Sensitive data routed through AI agents can be extracted by adversaries without defeating safety guardrails — a material data-protection risk.
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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