ML Safety Frameworks Poised to Face Wave of Peer-Reviewed Attacks

As ML safety research becomes professionalized, researchers seeking career advancement are predicted to target open-source safety evaluation frameworks with novel attacks through late 2026. This creates a critical threat to tools like Robustness Gym and Adversarial Robustness Toolbox that form the foundation of AI safety assurance. Understanding this motivation-driven attack pattern helps organizations defend not just their models, but the infrastructure they use to evaluate safety itself.