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This article centers on an AI-native tool called Aisle that successfully identified significant security vulnerabilities in the Curl library, outperforming other major AI models like Mythos and Codex which found none. The piece highlights a shift toward using systematically deployed, smaller language models for practical codebase scanning rather than relying solely on expensive frontier models or manual snippets. This represents a tangible application of machine learning in software supply chain security and vulnerability management.

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This AI tool found 6 Curl vulnerabilities Mythos and Codex missed – even Greg KH is impressed

ZDNetSeptember 4, 20265 min read

Aisle found six Curl flaws after Mythos and Codex reported none.

Aisle finds bugs that AI coding programs can’t. The best Linux maintainers are impressed. You should consider Aisle to help find bugs. You may not have heard of Aisle, an AI-native vulnerability-management startup, but some of the best open-source maintainers know it well and really like it. Why? Because Aisle finds real bugs that other, far better-known AI coding programs, such as Anthropic’s Mythos and OpenAI’s Codex, don’t. For example, Aisle recently said its security analysis system uncovered six previously unknown vulnerabilities in Curl that the open-source project’s maintainers accepted and assigned Common…

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