mlsanityData to 7 Oct 2026

Jailbreaking is (Mostly) Simpler Than You Think

arXiv 2503.05264 ↗First posted 7 Mar 2025cs.CR cs.AIcited by 3

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

  1. 2024Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack2 Apr
  2. 2023Jailbreaking Black Box Large Language Models in Twenty Queries12 Oct
  3. 2023Multilingual Jailbreak Challenges in Large Language Models10 Oct
  4. 2023AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models3 Oct
  5. 2023GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts19 Sep
  6. 2023Universal and Transferable Adversarial Attacks on Aligned Language Models27 Jul
  7. 2023Jailbroken: How Does LLM Safety Training Fail?5 Jul

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.