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Here, Mythos outshined all previous models, becoming “the first model to solve TLO from start to finish,” AISI said. While Anthropic’s new model only succeeded in 3 out of 10 attempts, even the average Mythos Preview run got through 22 of the 32 required infiltration steps, significantly higher than the 16-step average achieved by Claude 4.6.
Mythos Preview still has its limitations, though. AISI points out that the model still struggles with “Cooling Tower,” an even more difficult seven-step test designed to simulate an attempted disruption of the control software for a power plant. But AISI also writes that it expects “our evaluations would continue to improve with more inference compute” past the 100 million token budget imposed for its tests.
Small, weakly defended systems beware
Overall, Mythos’ performance on TLO suggests that the model “is at least capable of autonomously attacking small, weakly defended and vulnerable enterprise systems where access to a network has been gained,” AISI writes. That said, the group cautions that its simulated cyber ranges lack the kind of active defenders and defensive tooling often present in critical real-world systems. AISI’s TLO test is also designed to have specific vulnerabilities that might not exist in real-world systems and doesn’t penalize models for the kind of detection that might cause a real-world infiltration attempt to fail.
For those reasons, AISI says it can’t be sure whether “well-defended systems” would fall to an automated attack from Mythos Preview. But as future models match or outperform Mythos’ capabilities, AISI warns that those designing system protections should similarly utilize AI models to help harden their defenses.
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