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Erlang/OTP is one of the world's largest open-source codebases, powering infrastructure used by billions of people every day across fintech, digital health, telecoms and defence. 🌍 Keeping something that critical secure isn't simple. Human code reviews, daily and weekly automated testing, and proprietary vulnerability scanners were all in place. Yet edge cases and important vulnerabilities were still slipping through undetected. With this in mind, one of our Senior Software Developers set out to solve it differently, using agentic AI. Check out how that experiment played out, and what it found.

Erlang/OTP is one of the world's largest open-source codebases, and keeping it secure takes more than standard code review and testing. 🛠️ One of our Senior Software Developers at Erlang Solutions and member of the Ericsson Erlang/OTP core development team, built a custom agentic AI scanner using Claude Code to dig deeper. It doesn't just flag issues, it verifies them too, running each finding through a Docker container to check severity and confidence. Crucially, the tool is designed to be sceptical of itself. It errs on the side of caution, and a senior engineer reviews every result before anything is confirmed. Judgement stays human, even when the discovery is automated. 🤖

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Scanning the Erlang/OTP codebase with agentic AI turned up around 20 previously unknown, potentially severe security vulnerabilities. 📊 Alongside that, roughly 400 critical or high severity findings, and close to 4,000 in total, many of them low-severity issues that had simply never been noticed before. As our Senior Software Developer at Erlang Solutions, put it: agentic AI helped surface issues at a scale manual review couldn't reach, but experienced engineering judgement remained essential to separate genuine findings from false positives.

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