Papers by Qibing Ren

3 papers
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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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.

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