Introducing Cisco’s Integrated AI Security and Safety Framework - Cisco Blogs
<a href="https://news.google.com/rss/articles/CBMiWEFVX3lxTFBKak5uejQ5elcxcWNEaGU5dHRTanFodGpFTk5ZWFpsWjlqSjBFUGJ6Q3RRNzdQYUVrTlREaHpmRkNjZUhrTV9rY2NkZHRjZ3EzTmJkSmU2RXk?oc=5" target="_blank">Introducing Cisco’s Integrated AI Security and Safety Framework</a> <font color="#6f6f6f">Cisco Blogs</font>
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Anthropic Dials Back AI Safety Commitments - WSJ
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Dummy-Aware Weighted Attack (DAWA): Breaking the Safe Sink in Dummy Class Defenses
arXiv:2603.29182v1 Announce Type: new Abstract: Adversarial robustness evaluation faces a critical challenge as new defense paradigms emerge that can exploit limitations in existing assessment methods. This paper reveals that Dummy Classes-based defenses, which introduce an additional "dummy" class as a safety sink for adversarial examples, achieve significantly overestimated robustness under conventional evaluation strategies like AutoAttack. The fundamental limitation stems from these attacks' singular focus on misleading the true class label, which aligns perfectly with the defense mechanism--successful attacks are simply captured by the dummy class. To address this gap, we propose Dummy-Aware Weighted Attack (DAWA), a novel evaluation method that simultaneously targets both the true la
Efficient Bilevel Optimization with KFAC-Based Hypergradients
arXiv:2603.29108v1 Announce Type: new Abstract: Bilevel optimization (BO) is widely applicable to many machine learning problems. Scaling BO, however, requires repeatedly computing hypergradients, which involves solving inverse Hessian-vector products (IHVPs). In practice, these operations are often approximated using crude surrogates such as one-step gradient unrolling or identity/short Neumann expansions, which discard curvature information. We build on implicit function theorem-based algorithms and propose to incorporate Kronecker-factored approximate curvature (KFAC), yielding curvature-aware hypergradients with a better performance efficiency trade-off than Conjugate Gradient (CG) or Neumann methods and consistently outperforming unrolling. We evaluate this approach across diverse tas
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Anthropic Dials Back AI Safety Commitments - WSJ
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A Year’s Worth Of Analyses And Insights About The Avid Pursuit Of AGI And AI Superintelligence - Forbes
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