Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models
Kai Hu, Abhinav Aggarwal Facebook, Mehran Khodabandeh Meta, David Zhang, Eric Hsin Facebook, Li Chen Meta, Ankit Jain Meta, Matt Fredrikson Gray Swan AI and Carnegie Mellon University 等 9 位作者
This paper presents a novel Automated Red Teaming (ART) framework that shifts from example-based to policy-based evaluation, addressing critical limitations in scalability and validity. We define harmful content through abstract safety policies rather than specific static examples. We also introduce multiple evaluation objectives: risk coverage, semantic diversity, and fidelity, and discover Pareto trade-offs between them. We propose Jailbreak-Zero, a black-box method capable of both zero-shot generation and fine-tuned exploitation of a victim’s vulnerabilities to achieve Pareto optimality. Unlike prior approaches, it does not require expert-designed strategies/prompts, but still achieves superior, human-readable attacks against open-source and proprietary models (attack success rates of 99.5% against GPT-4o and 96.0% against Claude 3.5), even for unseen safety policies. It retains efficacy even after victim models undergo safety alignment, and exposes controls to navigate Pareto trade-offs without retraining. Lastly, we show that Jailbreak-Zero is the best-performing ART method at a given compute budget. Code is available at: https://github.com/hukkai/jailbreak-zero/ .
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表