Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs

ACL ARR 2025 May Submission141 Authors

Abstract:

With the development of Large Language Models (LLMs), numerous efforts have revealed their vulnerabilities to jailbreak attacks.
Although these studies have driven the progress in LLMs' safety alignment, it remains unclear whether LLMs have internalized authentic knowledge to deal with real-world crimes, or are merely forced to simulate toxic language patterns.
This ambiguity raises concerns that jailbreak success is often attributable to a hallucination loop between jailbroken LLM and judger LLM.
By decoupling the use of jailbreak techniques, we construct knowledge-intensive Q\&A to investigate the misuse threats of LLMs in terms of dangerous knowledge possession, harmful task planning utility, and harmfulness judgment robustness.
Experiments reveal a mismatch between jailbreak success rates and harmful knowledge possession in LLMs, and existing LLM-as-a-judge frameworks tend to anchor harmfulness judgments on toxic language patterns.
Our study reveals a gap between existing LLM safety assessments and real-world threat potential.

Paper Type: Long

Research Area: Ethics, Bias, and Fairness

Research Area Keywords: AI safety

Contribution Types: Model analysis & interpretability, Data resources, Data analysis

Languages Studied: English,Chinese

Keywords: Large Language Model, LLM safety, Jailbreak attack, safety alignment mechanism

Submission Number: 141