SAVeS: Steering Safety Judgments in Vision-Language Models via Semantic Cues | ScienceToStartup | ScienceToStartup

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References (28)

[1]

Vision Language Models Map Logos to Text via Semantic Entanglement in the Visual Projector

2025Sifan Li, Hongkai Chen et al.

[2]

Multimodal Safety Evaluation in Generative Agent Social Simulations

2025Alhim Vera, Karen Sanchez et al.

[3]

Visual Contextual Attack: Jailbreaking MLLMs with Image-Driven Context Injection

Founder's Pitch

"A framework and benchmark to steer and evaluate the safety judgments of vision-language models using semantic cues, revealing vulnerabilities in current systems."

Vision-Language Safety•Score: 7•View PDF ↗

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SAVeS: Steering Safety Judgments in Vision-Language Models via Semantic Cues

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References (28)

Founder's Pitch

"A framework and benchmark to steer and evaluate the safety judgments of vision-language models using semantic cues, revealing vulnerabilities in current systems."

Commercial Viability Breakdown

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SAVeS: Steering Safety Judgments in Vision-Language Models via Semantic Cues

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References (28)

Founder's Pitch

"A framework and benchmark to steer and evaluate the safety judgments of vision-language models using semantic cues, revealing vulnerabilities in current systems."

Commercial Viability Breakdown

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