SPA: Securing Persistent LLM Agents Across Queries with Plan-First Information-Flow Control
SPA introduces plan-first information-flow control for persistent LLM agents, preventing cross-query state contamination that existing per-tool defences miss—critical as enterprises deploy stateful agents over sensitive resources.
Summary written by editorial AI · Source link below
arXiv:2608.27234v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate over untrusted webpages, documents, tools, and persistent states while exercising authority over security-sensitive resources. Existing defenses typically protect either planning or individual tool interactions, but persistent agents face a broader threat: attacker-controlled data can alter control flow, enter security-sensitive tool arguments, or compromise later queries. We present SPA, a pl
Editorial Analysis
Persistent LLM agents that retain state across queries create a new data-leakage vector; plan-level information-flow control is essential before granting agents access to sensitive enterprise resources.
Mandate information-flow boundaries at the plan level for any persistent LLM agent with access to sensitive data or privileged tools.
Persistent AI agents that remember past interactions can leak or misuse sensitive data across sessions; a new defence framework addresses this risk architecturally.
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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