Breaking Alignment: Universal Attacks on Multimodal LLMs
How a single optimized image can bypass safety guardrails across multiple models
This research demonstrates a concerning security vulnerability where adversarial images can override alignment safeguards in multimodal LLMs.
- Creates a universal attack method that works across different queries and models
- Forces models to respond affirmatively to harmful prompts using synthetic images
- Bypasses safety measures through vision encoder exploitation
- Reveals critical security implications for deployment of multimodal AI systems
This work highlights the urgent need for robust defenses against adversarial attacks before widespread deployment of multimodal LLMs in sensitive applications.