arXiv:2606.10091v1 Announce Type: cross Abstract: Machine learning (ML) models are susceptible to various security, privacy, and fairness risks. Adversaries with different characteristics (i.e., objectives, knowledge, and capabilities) can collude by executing one attack to amplify others. Existing work lacks a systematic framework to explore collusion among adversaries, and to study the implications of the adversaries' characteristics. We present a framework covering collusion (a) between train- and inference-time adversaries, and (b) among inference-time adversaries. Our framework accounts f
The increasing sophistication and integration of AI models in critical infrastructure and decision-making processes necessitates a deeper understanding of their vulnerabilities to coordinated attacks.
A systematic framework for understanding colluding adversaries in ML pipelines is crucial for developing robust security measures and ensuring the trustworthiness of AI systems.
The focus expands from individual attack vectors to the more complex and dangerous realm of coordinated adversarial strategies, requiring a re-evaluation of current ML security paradigms.
- · AI security researchers
- · Cybersecurity firms
- · Regulators
- · Organizations relying on insecure ML systems
- · AI developers ignoring security risks
- · Users of compromised AI applications
New security standards and best practices for ML model development and deployment will emerge.
An entire industry dedicated to auditing and securing complex AI pipelines against multi-vector attacks will grow.
The development of 'adversary-aware' AI systems that dynamically adapt to detect and neutralize colluding threats could become a major research area.
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