Localization then Neutralization: Gradient-guided Token Suppression against Visual Prompt Injection Attack
arXiv:2605.25194v1 Announce Type: new Abstract: Adversarial images pose a severe security threat to multimodal large language models through prompt injection. Existing defenses largely lack a principled understanding of the underlying mechanisms and struggle to balance efficiency and defense utility. In this work, we show that successful adversarial attacks do not rely on the entire image uniformly but instead depend on a small subset of critical image tokens. Based on this insight, we propose Gradient Token Masking (GTM), which localizes these tokens via gradient analysis and neutralizes them
The proliferation of multimodal large language models makes them increasingly attractive targets for adversarial attacks, pushing the need for robust defense mechanisms.
This research provides a more principled understanding of prompt injection attacks, moving beyond ad-hoc defenses towards more systematic and efficient protective measures.
The ability to selectively neutralize critical attack tokens changes the landscape of AI security, offering a more efficient way to defend against visual prompt injections without sacrificing model utility.
- · Multimodal LLM developers
- · AI security researchers
- · Organizations deploying AI systems
- · Adversarial attackers
- · Developers of less robust AI defense mechanisms
More secure and reliable deployments of multimodal large language models will become possible.
This defense mechanism could inspire similar gradient-guided approaches to other forms of AI vulnerabilities.
Increased trust in AI systems could accelerate their adoption in sensitive applications, provided these defenses prove scalable and resilient.
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Read at arXiv cs.LG