Papers by Peichun Hua
Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring (2026.acl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) are vulnerable to a growing array of multimodal jailbreak attacks, necessitating a generalizable defense that is efficient for practical deployment. |
| Approach: | They propose a framework that uses a lightweight projection to separate benign and malicious inputs in safety-critical layers. |
| Outcome: | The proposed framework enables a simple yet powerful contrastive score that differentiates true malicious intent from mere distribution shift. |