Towards Realistic Guarantees: A Probabilistic Certificate for SmoothLLM

• URL: http://arxiv.org/abs/2511.18721v1
• Date: Mon, 24 Nov 2025 03:25:16 GMT
• Title: Towards Realistic Guarantees: A Probabilistic Certificate for SmoothLLM
• Abstract summary: SmoothLLM provides a certification guarantee against jailbreaking attacks.<n>It relies on a strict k-unstable' assumption that rarely holds in practice.<n>We introduce a more realistic probabilistic framework, (k, $varepsilon$)-unstable,' to certify defenses against diverse jailbreaking attacks.
• Score: 0.7212939068975618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The SmoothLLM defense provides a certification guarantee against jailbreaking attacks, but it relies on a strict `k-unstable' assumption that rarely holds in practice. This strong assumption can limit the trustworthiness of the provided safety certificate. In this work, we address this limitation by introducing a more realistic probabilistic framework, `(k, $\varepsilon$)-unstable,' to certify defenses against diverse jailbreaking attacks, from gradient-based (GCG) to semantic (PAIR). We derive a new, data-informed lower bound on SmoothLLM's defense probability by incorporating empirical models of attack success, providing a more trustworthy and practical safety certificate. By introducing the notion of (k, $\varepsilon$)-unstable, our framework provides practitioners with actionable safety guarantees, enabling them to set certification thresholds that better reflect the real-world behavior of LLMs. Ultimately, this work contributes a practical and theoretically-grounded mechanism to make LLMs more resistant to the exploitation of their safety alignments, a critical challenge in secure AI deployment.

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