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arXiv · 2502.01633

Adversarial Reasoning at Jailbreaking Time

Mahdi Sabbaghi·Paul Kassianik·George Pappas·Yaron Singer·Amin Karbasi·Hamed Hassani
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Abstract

As large language models (LLMs) are becoming more capable and widespread, the study of their failure cases is becoming increasingly important. Recent advances in standardizing, measuring, and scaling test-time compute suggest new methodologies for optimizing models to achieve high performance on hard tasks. In this paper, we apply these advances to the task of model jailbreaking: eliciting harmful responses from aligned LLMs. We develop an adversarial reasoning approach to automatic jailbreaking that leverages a loss signal to guide the test-time compute, achieving SOTA attack success rates against many aligned LLMs, even those that aim to trade inference-time compute for adversarial robustness. Our approach introduces a new paradigm in understanding LLM vulnerabilities, laying the foundation for the development of more robust and trustworthy AI systems.

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Mahdi Sabbaghi, Paul Kassianik, George Pappas, Yaron Singer, Amin Karbasi, Hamed Hassani. 2025-02-03. Adversarial Reasoning at Jailbreaking Time. https://arxiv.org/abs/2502.01633

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