Papers with safety evaluation

2 papers
Dynamic Evaluation with Cognitive Reasoning for Multi-turn Safety of Large Language Models (2025.acl-long)

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Challenge: Existing safety evaluation methods rely on static assessments that use fixed harmful prompts or predefined prefixes as jailbreak templates.
Approach: They propose a dynamic evaluation framework for multi-turn safety assessment of LLMs based on cognitive theories to simulate real chatting process and scenario simulation and strategy decision to guide dynamic generation.
Outcome: The proposed framework has been applied to evaluate the safety of widely used LLMs.
Experience-Driven Multi-Agent Optimization for Black-Box Jailbreak Attacks on Large Language Models (2026.findings-acl)

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Challenge: Existing methods for jailbreak have poor transferability and high sensitivity to preprocessing . EMJO provides an effective and scalable paradigm for systematic jailbreak optimization .
Approach: They propose a model that couples agents into a closed-loop "probe–evaluate–revise” process . they propose EMJO, which can be query-efficient and transferable, under black-box access.
Outcome: a new approach outperforms existing jailbreak baselines on diverse LLMs . it achieves up to 11% improvement in attack success rate while reducing query cost .

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