What 50 open source projects taught us about security in the AI era

Why it matters

GitHub reports on the latest session of its Secure Open Source Fund, which paired open source maintainers with GitHub Security Lab experts, security tooling, funding, and a peer community. The context is AI's effect on open source: maintainers now review unfamiliar contributions, manage new attack surfaces, and handle vulnerabilities with limited time and resources. The program's central finding is that AI-assisted workflows help maintainers investigate, prioritize, and respond to vulnerabilities faster, while humans retain the judgment and accountability for what ships. OpenClaw, invited as GitHub's fastest-growing open source project, developed an incident response plan, expanded its use of GitHub security tooling, and audited its GitHub Actions workflows. The article also describes the program's structure: a three-week sprint followed by a year of engagement, with funding tied to verified security improvements and training covering open source security foundations, threat modeling, secure coding, and AI security. Applications for the next session remain open, and the article catalogs participating projects by ecosystem role.