#MLSecOps
#Offensive_security
"Multi-Faceted Attack: Exposing Cross-Model Vulnerabilities in Defense-Equipped Vision-Language Models", Nov. 2025.
// Multi-Faceted Attack (MFA) - framework that systematically uncovers general safety vulnerabilities in leading defense-equipped VLMs, including GPT-4o, Gemini-Pro, and LlaMA 4, etc. Central to MFA is the Attention-Transfer Attack, which conceals harmful instructions inside a meta task with competing objectives. We offer a theoretical perspective grounded in reward-hacking to explain why such an attack can succeed
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