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Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents

ICML 2026 · Multi-agent systems and games › LLM agents · approach: Agent safety and attack benchmarks · 0 citations (OpenAlex)

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Introduces STING, an automated multi-turn red-teaming framework that decomposes harmful intents into persona-grounded phases to jailbreak tool-using LLM agents, and evaluates it across AgentHarm and six non-English languages with a time-to-first-jailbreak analysis framework and RMJD metric.

Problem

Existing agent-misuse benchmarks test single-prompt harmful instructions, leaving unmeasured how tool-using LLM agents come to assist illicit multi-turn tasks, and how language affects this.

Approach

A strategist agent builds a benign persona and phased attack plan; an attacker executes phases over multiple turns with refusal and phase-completion judges giving adaptive feedback; the red-teaming process is analyzed as a survival (time-to-first-jailbreak) problem with discovery curves, Cox hazard ratios, and RMJD.

Key results

• STING raises AgentHarm Score for Qwen3-Next from 35.1 (single-turn) to 72.7 at S_max=10, a 107.1% relative increase.
• X-Teaming performs worse than single-turn prompting in the agentic setting (e.g., 5.0 AHS for GPT-5.1 vs 24.3 single-turn).
• Across six non-English languages, lower-resource languages do not consistently raise jailbreak success; Hindi and Telugu show hazard ratios below 1 for Qwen3-Next.

Surprising findings

• Lower-resource languages (Urdu, Telugu) do not consistently increase jailbreak success, diverging from chatbot findings.
• For GPT-5.1, medium reasoning effort is safer than high reasoning effort.

Limitations

• Relies on AgentHarm's 44 base behaviors (176 prompt instances), a limited test set.
• Attack success and AHS are only moderately complementary; ASR depends on judge quality.
• Attacker and evaluators are open-weight models, and strategist quality is a bottleneck.

Experimental setup

Datasets: AgentHarm public test set

Benchmarks: AgentHarm

Metrics: AgentHarm Score attack success rate Restricted Mean Jailbreak Discovery hazard ratio

Baselines: single-turn AgentHarm prompting X-Teaming

Builds on: X-Teaming AgentHarm Crescendo

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