Jailbreaking is (Mostly) Simpler Than You Think
On the map: LLM safety and security → Jailbreaking
Abstract (arXiv)
We introduce the Context Compliance Attack (CCA), a novel, optimization-free method for bypassing AI safety mechanisms. Unlike current approaches -- which rely on complex prompt engineering and computationally intensive optimization -- CCA exploits a fundamental architectural vulnerability inherent in many deployed AI systems. By subtly manipulating conversation history, CCA convinces the model to comply with a fabricated dialogue context, thereby triggering restricted behavior. Our evaluation across a diverse set of open-source and proprietary models demonstrates that this simple attack can circumvent state-of-the-art safety protocols. We discuss the implications of these findings and propose practical mitigation strategies to fortify AI systems against such elementary yet effective adversarial tactics.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Jailbreaking, one of 12 directions in LLM safety and security. Builds on 7 of its 9 references that are in the map. First posted on arXiv 7 Mar 2025.
Builds on
7 of 9 references.
Closest ancestors · 7
Same direction, newest first
- 2024Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack2 Apr
- 2023Jailbreaking Black Box Large Language Models in Twenty Queries12 Oct
- 2023Multilingual Jailbreak Challenges in Large Language Models10 Oct
- 2023AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models3 Oct
- 2023GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts19 Sep
- 2023Universal and Transferable Adversarial Attacks on Aligned Language Models27 Jul
- 2023Jailbroken: How Does LLM Safety Training Fail?5 Jul
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Prefill-level Jailbreak: A Black-Box Risk Analysis of Large Language Modelscited by 9
- 2025LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systemscited by 1
- 2026Exploring Approaches for Detecting Memorization of Recommender System Data in Large Language Modelscited by 0
- 2026Exploring Approaches for Detecting Memorization of Recommender System Data in Large Language Models5 Jan 2026
- 2025LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems12 Sep 2025
- 2025Prefill-level Jailbreak: A Black-Box Risk Analysis of Large Language Models28 Apr 2025