Papers by Qibing Ren
Between a Rock and a Hard Place: The Tension Between Ethical Reasoning and Safety Alignment in LLMs (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) safety alignment predominantly operates on a binary assumption that requests are either safe or unsafe. |
| Approach: | They propose a methodology that embeds harmful requests within ethical framings to exploit this vulnerability. |
| Outcome: | The proposed framework achieves high success rates by exploiting model's own ethical reasoning to frame harmful actions as morally necessary compromises. |
LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts (2025.acl-long)
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Qibing Ren, Hao Li, Dongrui Liu, Zhanxu Xie, Xiaoya Lu, Yu Qiao, Lei Sha, Junchi Yan, Lizhuang Ma, Jing Shao
| Challenge: | Current safety training focuses on teaching models to reject harmful queries, but recent research shows that adversarial attacks or jailbreak methods bypass these safety mechanisms. |
| Approach: | They propose to use a new attack method to craft multi-turn toxic prompts that gradually lead LLMs to reveal unsafe content. |
| Outcome: | The proposed method outperforms existing methods in diversity, effectiveness, and efficiency across aligned LLMs. |
CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion (2024.findings-acl)
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| Challenge: | Large Language Models exhibit remarkable generative capabilities but can be misused for harmful purposes. |
| Approach: | They propose a framework that transforms natural language inputs into code inputs. |
| Outcome: | The proposed framework bypasses the safety guardrails of all models more than 80% of the time. |