arXiv:2601.14300v3 Announce Type: replace Abstract: Hard-label black-box attacks, relying solely on top-1 predictions, represent one of the most challenging yet practically threat models. Despite recent progress, existing approaches face two key limitations: (1) they overlook the critical role of initialization, focusing primarily on optimization strategies; and (2) they rely heavily on empirical heuristics without theoretical guarantees. To bridge this gap, we establish a unified theoretical framework showing that existing sign-flipping hard-label attacks can be understood as approximating th
The continuous evolution of AI models and their deployment in real-world applications is driving an immediate need for robust security and adversarial robustness, making advancements in attack methodologies crucial for defense.
This research provides a theoretically grounded method for hard-label adversarial attacks, enabling better understanding and development of defenses against sophisticated black-box threats in AI systems.
The ability to perform low-cost, theoretically-backed hard-label attacks shifts the paradigm for evaluating AI model security, forcing developers to consider more robust defenses against less visible threats.
- · AI security researchers
- · Organizations developing robust AI defenses
- · Ethical hackers and red teams
- · Developers of insecure AI systems
- · Organizations with inadequate AI security protocols
- · AI applications vulnerable to black-box attacks
Improved understanding and greater emphasis on developing adversarial attack resilience in AI systems.
Increased investment in explainable AI and inherently robust model architectures to counter sophisticated black-box attacks.
Potential for regulatory pressure demanding certified adversarial robustness for critical AI deployments.
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Read at arXiv cs.LG