MTK detects jailbreaks by monitoring the evolution of prompt neighborhood structures on the data manifold through LLM layers, reporting 95% TPR at 5% FPR on benign and 2% on pseudo-malicious prompts plus 85% TPR under adaptive attacks.
arXiv preprint arXiv:2410.02298 , year=
4 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Ellipsoid Control is a white-list test-time jailbreak defense that fits an anisotropic ellipsoid from benign activations to constrain projected gradient descent updates, aiming to improve the safety-utility tradeoff over black-list RepE methods.
FineSteer decomposes inference-time steering into Subspace-guided Conditional Steering and Mixture-of-Steering-Experts to deliver stronger control over LLM behaviors with less utility loss than prior methods.
Multimodal LLMs suffer Safety Geometry Collapse from modality-induced drift that reduces refusal separability; ReGap corrects drift at inference time using self-rectification signals to restore safety without retraining.
citing papers explorer
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Defending Jailbreak Attacks on Large Language Models via Manifold Trajectory Kinetics
MTK detects jailbreaks by monitoring the evolution of prompt neighborhood structures on the data manifold through LLM layers, reporting 95% TPR at 5% FPR on benign and 2% on pseudo-malicious prompts plus 85% TPR under adaptive attacks.
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Ellipsoid Control: A White-list Jailbreak Defense via Benign Latent Modeling
Ellipsoid Control is a white-list test-time jailbreak defense that fits an anisotropic ellipsoid from benign activations to constrain projected gradient descent updates, aiming to improve the safety-utility tradeoff over black-list RepE methods.
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FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models
FineSteer decomposes inference-time steering into Subspace-guided Conditional Steering and Mixture-of-Steering-Experts to deliver stronger control over LLM behaviors with less utility loss than prior methods.
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Safety Geometry Collapse in Multimodal LLMs and Adaptive Drift Correction
Multimodal LLMs suffer Safety Geometry Collapse from modality-induced drift that reduces refusal separability; ReGap corrects drift at inference time using self-rectification signals to restore safety without retraining.