SecureWebArena is a new benchmark suite for holistic security evaluation of LVLM-based web agents using diverse simulated environments, attack taxonomies, and multi-layered failure analysis across reasoning, behavior, and outcomes.
Safebench: A safety evaluation framework for multimodal large language models
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
ADvLM is the first visual adversarial attack framework for VLMs in autonomous driving, using semantic-invariant induction via LLM-generated prompt libraries and scenario-associated attention-based enhancement to achieve SOTA attack effectiveness across benchmarks and real-world tests.
ROBOSHACKLES is a new safety dataset for embodied foundation models created through a pipeline of hazard-aware editing and video synthesis from real observations, with all six tested models generating unsafe actions at a 100% rate.
PRISM decomposes harmful instructions into benign visual gadgets and directs LVLMs via prompts to compose them through reasoning into harmful outputs, achieving ASR over 0.90 on SafeBench.
citing papers explorer
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SecureWebArena: A Holistic Security Evaluation Benchmark for LVLM-based Web Agents
SecureWebArena is a new benchmark suite for holistic security evaluation of LVLM-based web agents using diverse simulated environments, attack taxonomies, and multi-layered failure analysis across reasoning, behavior, and outcomes.
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Visual Adversarial Attack on Vision-Language Models for Autonomous Driving
ADvLM is the first visual adversarial attack framework for VLMs in autonomous driving, using semantic-invariant induction via LLM-generated prompt libraries and scenario-associated attention-based enhancement to achieve SOTA attack effectiveness across benchmarks and real-world tests.
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ROBOSHACKLES: A Safety Dataset for Human-Injury Prevention in Embodied Foundation Models
ROBOSHACKLES is a new safety dataset for embodied foundation models created through a pipeline of hazard-aware editing and video synthesis from real observations, with all six tested models generating unsafe actions at a 100% rate.
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PRISM: Programmatic Reasoning with Image Sequence Manipulation for LVLM Jailbreaking
PRISM decomposes harmful instructions into benign visual gadgets and directs LVLMs via prompts to compose them through reasoning into harmful outputs, achieving ASR over 0.90 on SafeBench.