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New SEMA framework enhances multi-turn jailbreak attacks on LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SEMA, a novel framework designed to improve multi-turn jailbreak attacks against large language models. SEMA utilizes a two-stage process: prefilling self-tuning to generate structured adversarial prompts and reinforcement learning with an intent-drift-aware reward to maintain harmful objectives. This approach achieves state-of-the-art attack success rates, outperforming existing methods by a significant margin on benchmarks like AdvBench. AI

IMPACT This research provides a more robust method for red-teaming LLMs, potentially accelerating the identification and mitigation of safety vulnerabilities.

RANK_REASON The cluster contains an academic paper detailing a new method for LLM safety research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SEMA framework enhances multi-turn jailbreak attacks on LLMs

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The cluster contains an academic paper detailing a new method for LLM safety research. [lever_c_demoted from research: ic=1 ai=1.0]
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safety, paper
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mingqian Feng, Xiaodong Liu, Weiwei Yang, Jialin Song, Xuekai Zhu, Chenliang Xu, Jianfeng Gao ·

    SEMA: Simple yet Effective Learning for Multi-Turn Jailbreak Attacks

    arXiv:2602.06854v2 Announce Type: replace Abstract: Multi-turn jailbreaks capture the real threat model for safety-aligned chatbots, where single-turn attacks are merely a special case. Yet existing approaches break under exploration complexity and intent drift. We propose SEMA, …

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