Snyk's VulnBench benchmark found that large language models (LLMs) are not fully repeatable in security reviews: across 300 scans of 10 JavaScript projects with 6 configurations repeated 5 times, 84.8…
Snyk's benchmark of ~150 vulnerable code samples in JavaScript, Java, and Python found that out-of-the-box frontier models cluster at 72–75% secure-and-functional fix rates, but Snyk Intelligence (the…
AI code review has become production-standard in 2026, with tools now outperforming tired human reviewers on many categories of findings. The guide explains how AI code review works, its capabilities …
Aikido's 2026 State of AI in Security & Development report finds nearly 70% of organizations uncovered flaws tied to AI-generated code, with 1 in 5 reporting serious incidents linked to AI code. Engin…
Snyk released VulnBench JS 1.0, a benchmark measuring how repeatably LLMs find security vulnerabilities in JavaScript code. Across 300 scans, reference-matched findings were stable, but 80 of 161 uniq…
Generative AI is transforming .NET development by automating boilerplate code, bug prevention, documentation, unit testing, and log analysis. Tools like GitHub Copilot, Tabnine, Microsoft IntelliCode,…
A new benchmark, Snyk VulnBench JavaScript 1.0, reveals that large language models (LLMs) produce inconsistent vulnerability findings across repeated scans of the same JavaScript code, with 80 of 161 …
In a 2025 benchmark comparing three industry static analysis tools against three frontier LLMs on sixty-three real vulnerabilities in ten C# projects, the LLMs achieved F1 scores between 0.75 and 0.80…