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Safety Alignment

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Showing 126–150 of 288 papers

TitleStatusHype
Unleashing the Unseen: Harnessing Benign Datasets for Jailbreaking Large Language ModelsCode0
LLM Safety Alignment is Divergence Estimation in DisguiseCode0
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks SafetyCode0
BitBypass: A New Direction in Jailbreaking Aligned Large Language Models with Bitstream CamouflageCode0
Can a large language model be a gaslighter?Code0
DiaBlo: Diagonal Blocks Are Sufficient For FinetuningCode0
Monitoring Decomposition Attacks in LLMs with Lightweight Sequential MonitorsCode0
Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety AlignmentCode0
StructTransform: A Scalable Attack Surface for Safety-Aligned Large Language ModelsCode0
Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks—0
Toxic Subword Pruning for Dialogue Response Generation on Large Language Models—0
Trojan Horse Prompting: Jailbreaking Conversational Multimodal Models by Forging Assistant Message—0
Trustworthy AI: Safety, Bias, and Privacy -- A Survey—0
Jailbreak-AudioBench: In-Depth Evaluation and Analysis of Jailbreak Threats for Large Audio Language Models—0
TuneShield: Mitigating Toxicity in Conversational AI while Fine-tuning on Untrusted Data—0
Understanding and Mitigating Overrefusal in LLMs from an Unveiling Perspective of Safety Decision Boundary—0
Understanding and Rectifying Safety Perception Distortion in VLMs—0
Unfair Alignment: Examining Safety Alignment Across Vision Encoder Layers in Vision-Language Models—0
Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models—0
Utilizing Jailbreak Probability to Attack and Safeguard Multimodal LLMs—0
VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization—0
VLM-Guard: Safeguarding Vision-Language Models via Fulfilling Safety Alignment Gap—0
Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning—0
Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing—0
What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift—0
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