arXiv:2512.21815v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks. Entropy, as a measure of model uncertainty, is highly correlated with VLM reliability. While prior entropy-based attacks maximize uncertainty at all decoding steps, implicitly assuming that every token equally contributes to model instability, we reveal that a small fraction (around 20%) of high-entropy tokens, in the evaluated representative open-source VLMs with diverse architectures, concentrates a disproportionate share of adve
This research provides a more granular understanding of failure modes in Vision-Language Models, building upon existing vulnerabilities and adversarial attack research.
Sophisticated readers must understand that AI systems, particularly VLMs, have specific, high-leverage vulnerabilities that can be exploited, impacting reliability and security in critical applications.
The focus for improving VLM robustness shifts towards identifying and shoring up specific 'high-entropy tokens' rather than broadly addressing all decoding steps, potentially accelerating defenses.
- · AI security researchers
- · Developers of robust VLM architectures
- · Organizations deploying secure AI systems
- · Adversarial attackers relying on general uncertainty maximization
- · Unsecured VLM applications
- · Organizations with high VLM deployment without robust security protocols
VLMs become more resilient to certain classes of adversarial attacks as defenses become more targeted and effective.
Increased trust and broader deployment of VLMs in sensitive domains due to enhanced security and reliability.
A new arms race between highly targeted adversarial attacks and sophisticated defense mechanisms, pushing the boundaries of AI security research.
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Read at arXiv cs.LG