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

arXiv: https://arxiv.org/abs/2508.16347 · HTML: https://mlsanity.com/paper/2508.16347/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM safety and security / [Jailbreaking](https://mlsanity.com/safety/jailbreaking/)

## Builds on (most cited first, 22 of 22)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://mlsanity.com/paper/2310.03693/) (2023)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models](https://mlsanity.com/paper/2310.04451/) (2023)
- [JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models](https://mlsanity.com/paper/2404.01318/) (2024)
- [A StrongREJECT for Empty Jailbreaks](https://mlsanity.com/paper/2402.10260/) (2024)
- [LAB-Bench: Measuring Capabilities of Language Models for Biology Research](https://mlsanity.com/paper/2407.10362/) (2024)
- [CMB: A Comprehensive Medical Benchmark in Chinese](https://mlsanity.com/paper/2308.08833/) (2023)
- [DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers](https://mlsanity.com/paper/2402.16914/) (2024)
- [LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts](https://mlsanity.com/paper/2410.10700/) (2024)
- [FlipAttack: Jailbreak LLMs via Flipping](https://mlsanity.com/paper/2410.02832/) (2024)
- [CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models](https://mlsanity.com/paper/2402.16717/) (2024)
- [Intention Analysis Makes LLMs A Good Jailbreak Defender](https://mlsanity.com/paper/2401.06561/) (2024)
- [A Survey on Post-training of Large Language Models](https://mlsanity.com/paper/2503.06072/) (2025)
- [Are large language models superhuman chemists?](https://mlsanity.com/paper/2404.01475/) (2024)
- [JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models](https://mlsanity.com/paper/2406.09321/) (2024)
- [Know Thy Judge: On the Robustness Meta-Evaluation of LLM Safety Judges](https://mlsanity.com/paper/2503.04474/) (2025)
- [Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks](https://mlsanity.com/paper/2407.00869/) (2024)
- [from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors](https://mlsanity.com/paper/2503.00038/) (2025)
- [A Taxonomy for Data Contamination in Large Language Models](https://mlsanity.com/paper/2407.08716/) (2024)
- [The Digital Cybersecurity Expert: How Far Have We Come?](https://mlsanity.com/paper/2504.11783/) (2025)

## Built on by (most cited first, 11 of 11)

- [SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement](https://mlsanity.com/paper/2604.04989/) (2026)
- [A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness](https://mlsanity.com/paper/2603.06594/) (2026)
- [Gated Differentiable Working Memory for Long-Context Language Modeling](https://mlsanity.com/paper/2601.12906/) (2026)
- [Scaling Patterns in Adversarial Alignment: Evidence from Multi-LLM Jailbreak Experiments](https://mlsanity.com/paper/2511.13788/) (2025)
- [Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking](https://mlsanity.com/paper/2602.24009/) (2026)
- [Do Prompts Guarantee Safety? Mitigating Toxicity from LLM Generations through Subspace Intervention](https://mlsanity.com/paper/2602.06623/) (2026)
- [Jailbroken Frontier Models Retain Their Capabilities](https://mlsanity.com/paper/2605.00267/) (2026)
- [SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring](https://mlsanity.com/paper/2607.18665/) (2026)
- [HarmProfile: Characterizing Harmful Distributions in Frontier LLMs](https://mlsanity.com/paper/2608.14577/) (2026)
- [AI Security Policy Should Assess Systems, Not Only Models](https://mlsanity.com/paper/2605.09504/) (2026)
- [VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation](https://mlsanity.com/paper/2609.35028/) (2026)

## Abstract (arXiv)

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.