MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
Safety evaluation and red-teaming of large language models remain predominantly text-centric, and existing frameworks lack the infrastructure to systematically test whether alignment generalizes to audio, image, and video inputs. We present MUSE (Multimodal Unified Safety Evaluation), an open-source, run-centric platform that integrates automatic cross-modal payload generation, three multi-turn attack algorithms (Crescendo, PAIR, Violent Durian), provider-agnostic model routing, and an LLM judge with a five-level safety taxonomy into a single browser-based system. A dual-metric framework distinguishes hard Attack Success Rate (Compliance only) from soft ASR (including Partial Compliance), capturing partial information leakage that binary metrics miss. To probe whether alignment generalizes across modality boundaries, we introduce Inter-Turn Modality Switching (ITMS), which augments multi-turn attacks with per-turn modality rotation. Experiments across six multimodal LLMs from four providers show that multi-turn strategies can achieve up to 90-100% ASR against models with near-perfect single-turn refusal. ITMS does not uniformly raise final ASR on already-saturated baselines, but accelerates convergence by destabilizing early-turn defenses, and ablation reveals that the direction of modality effects is model-family-specific rather than universal, underscoring the need for provider-aware cross-modal safety testing.
69a907778b5b27c7ea9fa6a4
Zhongxi Wang
69a907778b5b27c7ea9fa6a5
Yueqian Lin
claimed_verified
2026-03-05T10:02:14.032Z
64b5198c25882acb62fb77ef
https://cdn-avatars.huggingface.co/v1/production/uploads/64b5198c25882acb62fb77ef/HX9pfMEPQlfjvSAgSLplY.png
Yueqian Lin
linyueqian
user
linyueqian
69a907778b5b27c7ea9fa6a6
Jingyang Zhang
69a907778b5b27c7ea9fa6a7
Hai Helen Li
69a907778b5b27c7ea9fa6a8
Yiran Chen
MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
Safety evaluation and red-teaming of large language models remain predominantly text-centric, and existing frameworks lack the infrastructure to systematically test whether alignment generalizes to audio, image, and video inputs. We present MUSE (Multimodal Unified Safety Evaluation), an open-source, run-centric platform that integrates automatic cross-modal payload generation, three multi-turn attack algorithms (Crescendo, PAIR, Violent Durian), provider-agnostic model routing, and an LLM judge with a five-level safety taxonomy into a single browser-based system. A dual-metric framework distinguishes hard Attack Success Rate (Compliance only) from soft ASR (including Partial Compliance), capturing partial information leakage that binary metrics miss. To probe whether alignment generalizes across modality boundaries, we introduce Inter-Turn Modality Switching (ITMS), which augments multi-turn attacks with per-turn modality rotation. Experiments across six multimodal LLMs from four providers show that multi-turn strategies can achieve up to 90-100% ASR against models with near-perfect single-turn refusal. ITMS does not uniformly raise final ASR on already-saturated baselines, but accelerates convergence by destabilizing early-turn defenses, and ablation reveals that the direction of modality effects is model-family-specific rather than universal, underscoring the need for provider-aware cross-modal safety testing.
MUSE is an open-source platform for evaluating multimodal safety in large language models, incorporating automated cross-modal attack generation and a dual-metric framework to assess alignment across different input modalities.
multimodal unified safety evaluation cross-modal payload generation multi-turn attack algorithms Crescendo PAIR Violent Durian LLM judge safety taxonomy Attack Success Rate soft ASR Inter-Turn Modality Switching multimodal LLMs provider-agnostic model routing