Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction
arXiv:2606.10571v1 Announce Type: cross Abstract: Adversarial examples reveal vulnerabilities in Vision-Language Pre-training (VLP) models and provide insights for improving robustness. A key property is cross-model transferability, which enables transfer-based black-box attacks. However, existing attacks often rely heavily on the surrogate model, causing cross-model performance drops. One reason is that adversarial optimization may follow surrogate model responses more than input semantics, making the update direction effective on the surrogate but less transferable to unseen targets. We refe
As Vision-Language Pre-training (VLP) models become more prevalent and powerful, research into their vulnerabilities, particularly adversarial attacks, is intensifying to improve their robustness and security.
Improving adversarial transferability challenges the 'black-box' security assumption of VLP models, forcing developers to build more robust and trustworthy AI systems, which is critical for their deployment in sensitive applications.
The ability to generate more effective transfer-based black-box attacks means that VLP models previously thought secure due to their proprietary nature may be more easily compromised by adversarial examples.
- · Cybersecurity researchers
- · AI robustness platforms
- · Organizations developing secure AI systems
- · Developers of unhardened VLP models
- · Organizations relying solely on model secrecy for security
- · AI systems vulnerable to adversarial manipulation
Security vulnerabilities in VLP models are more easily exploited via improved transferability of adversarial attacks.
Increased focus and investment will be directed towards developing advanced adversarial training techniques and defenses for VLP models.
The enhanced understanding of VLP model vulnerabilities could lead to new regulatory frameworks for AI safety and security, particularly in critical infrastructure and defense applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI