Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing

• URL: http://arxiv.org/abs/2605.10582v1
• Date: Mon, 11 May 2026 13:54:26 GMT
• Title: Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing
• Abstract summary: This paper proposes a guaranteed defense method for large language models (LLMs) to safeguard against jailbreaking attacks.<n>We integrate a two-stage prompt processing scheme-first disrupting the input prompt, then rectifying it-into the conventional smoothing defense framework.<n>Our approach can defend against both token-level and prompt-level jailbreaking attacks, under both established and adaptive attacking scenarios.
• Score: 14.717187950347986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a guaranteed defense method for large language models (LLMs) to safeguard against jailbreaking attacks. Drawing inspiration from the denoised-smoothing approach in the adversarial defense domain, we propose a novel smoothing-based defense method, termed Disrupt-and-Rectify Smoothing (DR-Smoothing). Specifically, we integrate a two-stage prompt processing scheme-first disrupting the input prompt, then rectifying it-into the conventional smoothing defense framework. This disrupt-and-rectify approach improves upon previous disrupt-only approaches by restoring out-of-distribution disrupted prompts to an in-distribution form, thereby reducing the risk of unpredictable LLM behavior. In addition, this two-stage scheme offers a distinct advantage in striking a balance between harmlessness and helpfulness in jailbreaking defense. Notably, we present a theoretical analysis for generic smoothing framework, offering a tight bound for the defense success probability and the requirements on the disruption strength. Our approach can defend against both token-level and prompt-level jailbreaking attacks, under both established and adaptive attacking scenarios. Extensive experiments demonstrate that our approach surpasses current state-of-the-art defense methods in terms of both harmlessness and helpfulness.

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