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Anthropic Tests GRAM Access Control for Dual-Use Knowledge

Anthropic, in collaboration with AE Studio, introduced Gradient-Routed Auxiliary Modules (GRAM) on July 8, 2026, a pretraining method designed to restrict access to dual-use knowledge in AI models by routing sensitive categories like virology and cybersecurity into removable modules. The research, which has not yet been applied to Claude production models, aims to improve safety by embedding access control at the architectural level rather than relying solely on refusal layers.

read1 min views1 publishedJul 9, 2026
Anthropic Tests GRAM Access Control for Dual-Use Knowledge
Image: Letsdatascience (auto-discovered)

Anthropic's GRAM research matters because it targets a hard safety engineering question: how to limit access to dual-use model knowledge without training and operating many separate frontier models. In a July 8, 2026 post with AE Studio collaborators, Anthropic described Gradient-Routed Auxiliary Modules, a pretraining method that routes categories such as virology, cybersecurity, nuclear physics, and specialized code into removable modules. The lab emphasized that the work is preliminary and has not been applied to Claude production models. For practitioners, the useful signal is that access control may move below refusal layers, into model architecture and training data flows.

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