Reassembling Distributed Risk: Trajectory-Conditioned Action Generation for Multi-Turn Agent Safety

• URL: http://arxiv.org/abs/2608.25711v1
• Date: Wed, 26 Aug 2026 12:28:35 GMT
• Title: Reassembling Distributed Risk: Trajectory-Conditioned Action Generation for Multi-Turn Agent Safety
• Abstract summary: Under multi-turn decomposition attacks, a harmful objective can be distributed across individually plausible requests and tool calls.<n>We propose emphReassembling Distributed Risk (ReDiR), a generation-time defense that conditions action generation on trajectory-level security evidence.<n>We evaluate ReDiR on two agent-safety benchmarks across three model families and eight held-out tool domains.
• Score: 12.022506016268112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tool-using LLM agents extend security risks beyond generated text to actions that affect external systems. Under multi-turn decomposition attacks, a harmful objective can be distributed across individually plausible requests and tool calls, becoming apparent only from the accumulated trajectory. Existing defenses either rely on auxiliary online reasoning to recover long-horizon security evidence or assess actions after generation, often incurring additional inference cost or depending on runtime-specific action representations. We propose \emph{Reassembling Distributed Risk} (ReDiR), a generation-time defense that conditions action generation on trajectory-level security evidence. Before each action, ReDiR compresses the current trajectory into a compact latent safety representation and injects it into the frozen base model. The representation is learned through same-model, cross-view supervision, where safe behavior from an explicit task view provides supervision for recovering distributed safety evidence from the original multi-turn trajectory. This design enables ReDiR to integrate cross-turn security information directly within the generation process without relying on a separate action-level safety module. We evaluate ReDiR on two agent-safety benchmarks across three model families and eight held-out tool domains. ReDiR reduces attack success rates to below 8\%, transfers to unseen tool domains, and preserves benign fidelity with low computational overhead.

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