| Cogent Announces Mythos-Class Frontier AI for Cybersecurity Cogent Unveils VR-1, the First Mythos-Class Frontier AI Model Trained Specifically to Excel at Cybersecurity TasksIn benchmark testing, VR-1 proved twice as many enterprise attack paths as leading frontier models, at roughly a quarter of the cost. Cogent Security introduced Cogent VR-1, a frontier reasoning model built for cybersecurity and trained to operate inside live enterprise environments. On IntrusionBench, a new benchmark for real-world enterprise attack chains released alongside the model, VR-1 began with a single foothold and autonomously proved viable paths to its objectives, achieving 2x the performance of other frontier models, proving twice as many enterprise attack paths as leading frontier models, at roughly a quarter of the cost. Most frontier models can find a single bug in a single codebase. Real attacks rarely stay that contained. They travel through a chain of small, individually unremarkable weaknesses. A public service exposes a minor flaw. An identity carries more permission than it needs. Somewhere in the build pipeline, an artifact gets trusted without ever being checked. On their own, each looks like ordinary backlog noise. Strung together, they become a path to a crown jewel. Cogent trained VR-1 to work the way a skilled adversary does. Given a foothold inside an enterprise, it maps the surrounding environment and connects weaknesses that span different systems to prove which paths are actually reachable. It then checks whether a proposed fix closes the gap or simply relocates the risk. Reasoning that once took an expert security researcher weeks runs in hours. The release lands as autonomous AI attacks stop being theoretical. In recent months, fully autonomous AI attacks have targeted some of the world's largest governments and technology companies. Defenders need matched AI capabilities at this level to understand where AI-driven attacks could succeed