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Structurally Close, Temporally Distant: Measuring Security Exposure in Long-Horizon LLM Agents

arXiv · AI, language, vision and robotics · article · Sep 5, 2026 · UTC

Long-horizon LLM agents interact with untrusted content, persistent memory, external state, and sensitive tools. Existing analyses often characterize attacks by the number of execution steps between malicious input and a downstream action. We show that temporal remoteness can overstate security separation in stateful agents. We introduce a provenance-aware execution graph linking agent events through deterministic state, identifier, and tool provenance, and define \emph{influence distance} $\DI$ as the shortest structural path from an untrusted source to a sensitive action. We compare it with

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