AISLE replicates Anthropic Mythos flagship vulnerability detection with a 3.6B open model at $0.11/M tokens. Tests reveal AI security capability has a jagged frontier: small models outperform large ones in some subtasks, and the real moat is system design, not model size.
AISLE tests show DeepSeek R1 matches Anthropic's Mythos in critical bug detection, with cheaper models also finding the same vulnerabilities. The experiment uncovered long-standing flaws in FreeBSD and OpenBSD. Results indicate AI security depends more on workflow integration than model cost, with implications for crypto markets under MiCA.
AISLE found that DeepSeek R1 matches Anthropic’s Mythos in critical bug detection, while several cheaper models also performed strongly. The results suggest AI security may depend more on workflow integration and system design than model price alone.
AISLE's test shows DeepSeek R1 equals Anthropic's Mythos in critical bug detection at 1/10th cost. Finding challenges the 'expensive model wins' consensus, impacting crypto risk management and MiCA compliance.
AISLE tests show DeepSeek R1 matches Claude Mythos in detecting critical bugs at one-tenth the cost. The result reveals AI security performance depends more on workflow integration than model price, with significant implications for crypto market security audits and MiCA regulatory compliance.
