arXiv:2503.01734v3 Announce Type: replace-cross Abstract: Attacks on machine learning models have been extensively studied through stateless optimization. In this paper, we demonstrate how a reinforcement learning (RL) agent can learn a new class of attack algorithms that generate adversarial samples. Unlike traditional adversarial machine learning (AML) methods that craft adversarial samples independently, our RL-based approach retains and exploits past attack experience to improve the effectiveness and efficiency of future attacks. We formulate adversarial sample generation as a Markov Decis
The increasing sophistication of AI models and their widespread deployment necessitates more advanced methods for identifying and mitigating vulnerabilities.
This paper introduces a novel, more effective method for adversarial attacks using reinforcement learning, threatening the security and reliability of AI systems across various applications.
Adversarial attacks are no longer 'stateless' but can learn and adapt, making current defense mechanisms potentially obsolete and demanding a new generation of robust AI security.
- · Cybersecurity researchers
- · AI security solution providers
- · Organizations prioritizing AI model robustness
- · Organizations relying solely on traditional AI defenses
- · Deployed AI systems with inadequate security
- · Sectors with high-stakes AI applications (e.g., autonomous vehicles, finance)
New vulnerabilities in deployed AI models will emerge, leading to increased security incidents and a need for immediate patches.
Significant investment will shift towards developing adaptive, RL-based defensive mechanisms and formal verification for AI systems.
Regulatory bodies may introduce stricter compliance requirements for AI systems, mandating demonstrable robustness against sophisticated adversarial attacks.
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