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GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis
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Large Language Models (LLMs) face threats from jailbreak prompts. Existing methods for detecting jailbreak prompts are primarily online moderation APIs or finetuned LLMs. These strategies, however, often require extensive and resource-intensive data collection and training processes. In this study, we propose GradSafe, which effectively detects jailbreak prompts by scrutinizing the gradients of safety-critical parameters in LLMs. Our method is grounded in a pivotal observation: the gradients of an LLM's loss for jailbreak prompts paired with compliance response exhibit similar patterns on certain safety-critical parameters. In contrast, safe prompts lead to different gradient patterns. Building on this observation, GradSafe analyzes the gradients from prompts (paired with compliance responses) to accurately detect jailbreak prompts. We show that GradSafe, applied to Llama-2 without further training, outperforms Llama Guard, despite its extensive finetuning with a large dataset, in detecting jailbreak prompts. This superior performance is consistent across both zero-shot and adaptation scenarios, as evidenced by our evaluations on ToxicChat and XSTest. The source code is available at https://github.com/xyq7/GradSafe.
Forward citations
Cited by 8 Pith papers
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JailbreaksOverTime: Detecting Jailbreak Attacks Under Distribution Shift
Jailbreak detection models drift over time, and a weekly self-trained detector plus an unsupervised behavioral monitor can keep false negatives near 0.3 to 0.4 percent at a 0.1 to 1 percent false positive rate.
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SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses
A systemization of LLM jailbreak security that adds linked taxonomies, an evaluation platform, and JailbreakDB, while its main attack–defense comparison results remain deferred.
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The First Differentiable Transfer-Based Algorithm for Discrete MicroLED Repair
The submission's abstract announces a differentiable microLED repair planner, yet its full text is the LoD jailbreak-detection paper (arXiv 2508.09201v4), leaving the claimed result without any derivation.
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Advancing Embodied Agent Security: From Safety Benchmarks to Input Moderation
A masked-attention moderator isolates user instructions from functional prompts and detects malicious commands to embodied agents with 94.58% average accuracy.
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JBShield: Defending Large Language Models from Jailbreak Attacks through Activated Concept Analysis and Manipulation
JBShield detects jailbreaks by checking whether a prompt activates both a toxic concept and a jailbreak concept inside an LLM, then steers those concepts to produce a safe refusal.
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CCFC: Core & Core-Full-Core Dual-Track Defense for LLM Jailbreak Protection
A dual-track core-extraction defense reports near-zero jailbreak success on four attacks with preserved benign utility, but its safety-classifier component is undisclosed.
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Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A Cyber Defense Perspective
A structured survey of jailbreak prompts and layered defenses for large language models, with six illustrative case studies and no empirical evaluation.
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Attack and defense techniques in large language models: A survey and new perspectives
A survey taxonomizing LLM attacks into prompt, optimized, application-level, and model-theft categories, and defenses into prevention and detection categories, with no new empirical results.
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