Video MLLMs show higher jailbreak rates with multi-clip videos than images or static videos, with success increasing alongside clip count and contextual diversity.
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A hybrid first-order then zeroth-order optimization approach improves robustness of safety-aligned LLMs while preserving utility, with layer-wise sensitivity estimation for efficiency.
A unified adaptive attack exploits the common weakness across 15 defenses against malicious fine-tuning, showing they only obscure rather than remove harmful model capabilities.
A new 7x4 taxonomy organizes agentic AI security threats by architectural layer and persistence timescale, revealing under-explored upper layers and missing defenses after surveying 116 papers.
Refusal in language models is mediated by a single direction in residual stream activations that can be erased to disable safety or added to elicit refusal.
GPT-4 LLM agents autonomously exploit 87% of tested one-day vulnerabilities when given CVE descriptions, far outperforming other models and tools.
Optimizer choice during LLM fine-tuning produces up to 7x variation in emergent misalignment rates, with spectral regularization on LoRA adapters substantially mitigating misalignment for prone optimizers.
CANARY detects 1% fine-tuning contamination with AUROC 1.000 using SAE-filtered hidden states, 7.5x below output-level detection thresholds, with zero false positives on benign tuning.
CSULoRA decomposes LoRA updates into fully aligned, partially aligned, and off-subspace components and solves a closed-form penalized minimum-change problem to preserve safe parts while attenuating unsafe directions.
Early mixing of post-training data into pretraining improves retention of acquired capabilities after subsequent fine-tuning in language models.
A truly benign DPO attack using 10 harmless preference pairs jailbreaks frontier LLMs by suppressing refusal behavior, achieving up to 81.73% attack success rate on GPT-4.1-nano at low cost.
Benign fine-tuning of foundation models induces large, heterogeneous, and often contradictory changes in safety metrics across general and domain-specific benchmarks.
REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
Gradient-based selection that drops high-gradient samples during continual fine-tuning preserves safety alignment in LLMs better than standard fine-tuning while keeping task performance competitive.
ORPO is most effective at misaligning LLMs while DPO excels at realigning them, though it reduces utility, revealing an asymmetry between attack and defense methods.
Benign fine-tuning collapses safety geometry in guard models like Granite Guardian, dropping refusal to 0%, but Fisher-Weighted Safety Subspace Regularization restores it to 75% while improving robustness.
FRPO applies a max-min robust optimization over KL-bounded policy neighborhoods during RLHF to reduce catastrophic forgetting of safety and accuracy under subsequent SFT or RL fine-tuning.
Introduces NoisyToolBench benchmark and Ask-when-Needed framework to improve LLM tool-use performance when user instructions are unclear or incomplete.
The paper proposes Dual-Reference SFT (DR-SFT) to defend LLMs against harmful QA pairs embedded in benign training samples, where existing guardrails fail at the example level.
Trait-space drift monitoring detects emergent misalignment checkpoints in 7-9B LLMs with 2.2% FNR, 2.9% FPR and 0.99 AUROC, outperforming PCA and SAE baselines.
DataShield scores training samples by their contribution to increased LLM response compliance and filters high-risk ones using a compliance vector and layer-specific CAS metric.
LoRA fine-tuning produces feature dictionaries in language models that show weak alignment with pretrained SAE features and are better reconstructed by adapter-specific SAEs.
SPARD defends LLMs from harmful fine-tuning attacks via alternating safety projections and relevance-diversity DPP data selection, reporting lowest attack success rates on GSM8K and OpenBookQA while keeping task accuracy.
Abliteration and prefilling attacks raise harm success rates on safeguarded open-weight LLMs from below 10% to 16-96% across three benchmarks, and a new ART tuning method reduces those rates by 10-20%.
citing papers explorer
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Aligned but Fragile: Enhancing LLM Safety Robustness via Zeroth-Order Optimization
A hybrid first-order then zeroth-order optimization approach improves robustness of safety-aligned LLMs while preserving utility, with layer-wise sensitivity estimation for efficiency.
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Palette: A Modular, Controllable, and Efficient Framework for On-demand Authorized Safety Alignment Relaxation in LLMs
Palette identifies refusal directions via multi-objective search, internalizes them through lightweight adaptation, and supports on-demand multi-domain authorization via independent learning and parameter merging.