🔒 LLM Safety

🎞️ ECCV2026 · 7 paper notes

📌 Same area in other venues: 📷 CVPR2026 (12) · 🔬 ICLR2026 (185) · 💬 ACL2026 (115) · 🤖 AAAI2026 (41) · 🧠 NeurIPS2025 (81) · 📹 ICCV2025 (10)

🔥 Top topics: Multimodal/VLM ×5 · Adversarial Robustness ×2

Attention Misses Visual Risk: Risk-Adaptive Steering for Multimodal Safety Alignment

MoRAS improves multimodal risk assessment with a short image description, then uses the estimated risk to steer early response activations for the original query toward refusals, reducing LLaVA-1.5-7B's MM-Safety attack success rate from 40.1% to 2.6% while retaining its reported general-task aggregate scores.

BBQ-V: Benchmarking Visual Stereotype Bias in Large Multimodal Models

BBQ-V evaluates unsupported group-based inferences using real multi-person images and under-informative open-ended questions: its 4,497 images support 14,144 image–question pairs, and an input ablation shows that seeing more does not necessarily produce more cautious judgments.

Learning to Compose: Revisiting Proxy Task Design for Zero-Shot Composed Image Retrieval

FoCo (Focus-then-Complete) models vision-language composition in Zero-Shot Composed Image Retrieval (ZS-CIR) as a learnable two-stage process of "focusing then completing." By jointly training two proxy tasks—text-anchored visual aggregation and context-conditioned semantic completion—along with a cross-instance contrastive loss to prevent shortcut learning, it comprehensively outperforms existing methods across four ZS-CIR benchmarks without relying on LLMs during inference.

Neural Gate: Mitigating Privacy Risks in LVLMs via Neuron-Level Gradient Gating

This paper proposes Neural Gate, a neuron-level gradient gating method for mitigating privacy risks in LVLMs. The core mechanism is to first measure the consistency of each neuron's contribution to privacy targets on privacy subject features using a learnable vector, classifying neurons into three categories: strongly active, weakly active, and inactive. Then, during model editing, parameter updates are applied only to the gradients of strongly active privacy neurons, while gradients of other neurons are truncated. On MiniGPT and LLaVA, Neural Gate achieves a rejection rate of over 94% on sensitive queries with almost no loss in general task performance, while displaying robust generalization on out-of-distribution privacy categories.

ReShift: Aha-Moment-Driven Reasoning-Level Backdoor Attacks on Vision-Language Models

ReShift proposes a reasoning-level backdoor attack method targeting Vision-Language Models (VLMs). By leveraging the reinforcement learning-induced cognitive behavior of the "aha moment," it guides a controllable shift in the reasoning trajectory. This redirects the predicted results to a preset target answer while maintaining logical consistency, making it significantly harder to detect than traditional output-level backdoor attacks.

SlowBA: An efficiency backdoor attack towards VLM-based GUI agents

SlowBA proposes the first efficiency backdoor attack against VLM-based GUI agents. Through a two-stage Reward-level Backdoor Injection (RBI), the agent is trained to generate ultra-long responses during SFT, and a trigger-aware reward function is then used in RL to distinguish between triggered and clean inputs. Consequently, the agent exhibits extremely high response latency when encountering pop-up triggers, while the task accuracy remains largely unaffected.

Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning

This paper proposes a new paradigm of "benign memory forgetting" and the S-MLLMUn Bench evaluation benchmark, and designs the SMFA (Sculpted Memory Forgetting Adapter) framework. By utilizing parameter masks guided by retaining anchors, it precisely erases privacy-sensitive knowledge in MLLMs without compromising the models' foundational visual understanding capabilities.