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#supply-chain-security

8月13日周四

  1. GitHub Security Blog

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

    ai-securitymaintainersopen-sourcesupply-chain-security

    AI is changing the pace of open source development and the security challenges that come with it. Maintainers are reviewing unfamiliar contributions, managing new attack surfaces, and responding to vulnerabilities with limited time and resources. Session 4 of the GitHub Secure Open Source Fund tested a practical response. The Secure Fund invested more than $500,000 across 50 projects, pairing maintainers with GitHub Security Lab experts, GitHub security tools, AI-assisted workflows, and a peer community. One lesson emerged consistently: AI can help maintainers investigate, prioritize, and respond faster. Maintainers still provide the context, judgement, and accountability required to decide what ships. OpenClaw was invited to participate in Session 4 because it is GitHub’s fastest-growing open source project, and its maintainers wanted to strengthen its security posture. By the end of Session 4, OpenClaw developed an incident response plan, expanded its use of GitHub security tooling, audited its GitHub Actions workflows, and strengthened its processes for identifying and responding to security issues. The maintainers shared: OpenClaw’s experience reflects the broader story of Session 4. While the specific risks varied across the cohort, maintainers shared a consistent need: the knowledge, tools, and expert support to secure software as AI changed how they built it. Across the program, maintainers turned that support into concrete security improvements. Projects s

8月6日周四

  1. GitHub Security Blog

    How we took malware advisories beyond npm

    github-advisory-databasemalwarenpmsecuritysupply-chain-security

    A compromised package can steal credentials the moment you install it, and until recently, GitHub could only flag those in npm. Not anymore. This is the story of how the supply chain engineering team behind Dependabot expanded malware advisories to eight ecosystems by building on OpenSSF’s shared malicious packages data. Here’s where things stand: earlier this year, Dependabot started flagging malware in your npm dependencies. Great news if you write JavaScript. Now we’re bringing that same functionality to PyPI. We’ve enhanced the GitHub Advisory Database to ingest malware reports from OpenSSF’s malicious-packages repository, which means malware advisories and the Dependabot alerts they power cover all eight major package ecosystems: npm, PyPI, Maven, RubyGems, NuGet, Go, crates.io, and PHP Composer. I lead the Dependabot team in GitHub’s supply chain security organization, and in this post, I’ll show you how this pipeline works. From one ecosystem to eight The Advisory Database has imported vulnerability data from external sources for years. RubySec for gems, RustSec for crates, PyPA for Python. Each one is an importer that reads a public advisory repo and maps records into our database. Malware was the odd one out: it flowed through a separate, internal, npm-only path, built around GitHub’s own detection of malicious npm packages. Expanding the existing detection from one to eight supported ecosystems would have taken us year

7月29日周三

  1. GitHub Security Blog

    Tame Dependabot: Group your updates, slow the cadence, keep security fast

    dependabotengineeringgithub-actionssecuritysupply-chain-security

    If you maintain an active repository, you know the feeling. You open your notifications on a Monday morning and there they are: five, 10, sometimes a dozen Dependabot pull requests, each bumping a single dependency by a single patch version. Individually, every one of them is helpful. Collectively, they’re noise. And noise is how important updates get ignored. We looked at Microsoft’s GCToolkit, an open source Java library for analyzing garbage collection logs. As of July 2026, a git log of the repository showed that 92 of its 578 commits, roughly one in six, were Dependabot version bumps, with 61 in the previous 12 months alone, sometimes several in a single day. That’s a lot of review, merge, and CI cycles spent on routine maintenance. The good news: Dependabot already ships with the features to fix this. In a recent pull request, the project changed its dependabot.yml in three small but meaningful ways, turning a daily drip of single-dependency pull requests into a predictable, grouped, monthly batch per ecosystem. Here’s what changed, why it works, and how to apply the same pattern to your own repositories, following the GCToolkit example. The problem: Good defaults, wrong cadence Here’s what GCToolkit’s configuration looked like before: version: 2 updates: - package-ecosystem: github-actions directory: "/" schedule: interval: daily open-pull-requests-limit: 10 This is a common starting point, but the daily interval here was a deliberat

7月28日周二

  1. GitHub Security Blog

    Disrupting supply chain attacks on npm and GitHub Actions

    github-actionsmalwarenpmsecuritysupply-chain-security

    In the past year, there’s been a pattern of supply chain attacks that target weaknesses in package repositories and CI/CD systems to quickly spread malware to hundreds of open source projects. This malware seeks to exfiltrate credentials both to broadly spread the attack, as well as for later exploitation. We’ve written a few times about our plans for hardening the supply chain: Our plan for a more secure npm supply chain in September 2025, Strengthening supply chain security: Preparing for the next malware campaign in December 2025, and What’s coming to our GitHub Actions 2026 security roadmap in March 2026. In this post, we’re updating you on changes we’ve implemented that directly disrupt some of the most common and impactful supply chain attack techniques. Anatomy of supply chain attacks Supply chain attacks chain together several weaknesses, and there is no single security capability that can stop them. Addressing them takes a holistic approach, prioritizing the mitigations that break the most impactful links in the attack chain. Our teams have been studying these attacks to deploy several improvements that disrupt them and limit their impact. This is possible thanks to collaboration with the security research and developer communities. The attacks vary in how they spread across the software ecosystem. However, most of these attacks follow similar techniques to gain initial access to a project, escalate privileges, and distribute across user