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Unsafer in Many Turns: Benchmarking and Defending Multi-Turn Safety Risks in Tool-Using Agents

ICML 2026 · Multi-agent systems and games › LLM agents · approach: Agent safety and attack benchmarks · 1 citations (OpenAlex)

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Introduces a multi-turn attack taxonomy and the MT-AgentRisk benchmark for tool-using agent safety, plus ToolShield, a training-free self-exploration defense that distills safety experiences from simulated test executions.

Problem

Existing agent safety benchmarks test either single-turn tool-using harms or multi-turn text-only harms, leaving harmful intent distributed across turns and tools unevaluated; defenses also often require retraining or external guardrails that do not improve the agent's own awareness.

Approach

A principled taxonomy (Addition vs. Decomposition formats, Mapping/Wrapping and Composition/Identity methods, Data vs. Environment targets) converts single-turn harmful tasks into multi-turn tool-use attack sequences; ToolShield has the agent synthesize test cases for a new tool, execute them in a sandbox, and distill the outcomes into an experience list injected into its context at deployment.

Key results

• ASR increases on average by 16% from single-turn to multi-turn settings across models (+27.1% for Claude-4.5-Sonnet, +8.8% for Deepseek-v3.2).
• ToolShield reduces multi-turn ASR by about 30% on average; Claude-4.5-Sonnet multi-turn ASR drops from 72% to 22%.
• ToolShield does not reject any of 170 benign tasks (0% rejection for Claude-4.5-Sonnet with and without defense).

Surprising findings

• Stronger capability does not imply better safety: Gemini-3-Flash and GPT-5.2 are close on a capability index but differ markedly in multi-turn ASR (79% vs. 51%), and DeepSeek-v3.2 has the weakest safety despite strong capability.
• LlamaFirewall reduces ASR by only about 3% on the multi-turn subset.

Limitations

• Black-box evaluation only, with no analysis of internal model states.
• Benchmark covers only five tools, which may not capture all real-world tool-use scenarios.
• Defense depends on the agent's own capability, so weaker agents generate lower-quality experiences.

Experimental setup

Datasets: OpenAgentSafety SafeArena P2SQL MCPMark

Benchmarks: MT-AgentRisk

Metrics: Attack Success Rate Rejection Rate false positive rejection on benign tasks cost per experience

Baselines: LlamaFirewall LLM Guardrail ToolShield w/o simulation Taxonomy Defense

Builds on: OpenHands LlamaFirewall OpenAgentSafety SafeArena MHJ

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