Author name: ITMAITY

When Christian Grobmeier went to help his son with a Minecraft problem, he found the game displaying a warning: “We are suffering from a security hole from Log4J, please be careful and update immediately.” I stared at the screen and told my son, ‘I’m sorry, it’s my fault.’ Christian Grobmeier, Log4j maintainer This is the untold story of how one maintainer and the Log4j team navigated a crisis that exposed critical gaps in our digital infrastructure and demonstrated the importance of open source security and sustainability. Now, initiatives like the GitHub Secure Open Source Fund are working to make sure it never happens again. It all started a few hours earlier on a cold November day, when Christian, who is a maintainer of the open source project Log4j, planned to spend time playing games with his son. Instead, he found himself staring at his phone, watching notifications pile up in his inbox—10, then 20 emails flooding in. When he saw the words “remote code execution,” his first thought was: “Maybe I’m on the wrong mailing list.” He wasn’t. And within hours, Christian would be at the center of what became known as Log4Shell: the most severe vulnerability in internet history, affecting billions of devices from Fortune 500 companies to Minecraft servers worldwide. “I told my son, I will play with you in like five minutes,” Christian recalls. “But he didn’t see me for the next couple of days.” Watch the full interview with Christian Grobmeier and Gregg Cochran, staff program manager at GitHub, above. 👆 The ubiquity that made Log4Shell a perfect storm Log4j is foundational software. This 20+ year-old Java logging library quietly powers system events in applications worldwide, like user logins and calculation results. But this small piece of software had quietly become a dependency in thousands of projects across the Java ecosystem. Log4j is such a small, tiny library. But everybody can use it in their software. Christian Grobmeier That ubiquity made Log4Shell devastating. Financial services companies relied on it for compliance auditing. E-commerce systems used it to track security incidents. Insurance companies needed it to monitor their software behavior. In a 2022 Tidelift survey, 49% of open source developers reported that their organization relies on Java—and most of them were using Log4j without even knowing it. When Christian realized the scope of the vulnerability, the weight hit him immediately: “Literally all Java applications in the world could be affected. Even 10% would be a major problem. This would be catastrophic.” A vulnerability that scored a perfect 10 Log4Shell reveals how a seemingly innocent feature became an attack vector. Log4j used Java’s Naming and Directory Interface (JNDI) to provide flexibility, allowing developers to load software components from remote servers. But the library didn’t validate whether JNDI lookup strings were coming from trusted sources. “How can a string break the internet?” Christian asks.  The exploitation was frighteningly simple. An attacker could input a malicious JNDI string into any application field that gets logged—a username field, a search box, even a Minecraft chat message—and execute remote code on the target system. jndi:<protocol>://<server-name>:<port>/<path-to-object> “You don’t even need to have special knowledge,” Christian notes. “You just run around and push the string wherever you want it.” The Common Vulnerability Scoring System (CVSS) gave Log4Shell a perfect 10: the highest possible score. “The first time I heard about this score, I thought, maybe it’s not so bad,” Christian remembers. “And then after a couple of days, I thought, yeah, maybe we should extend this to a score of 15 or 20.“ The human cost of maintaining critical infrastructure The personal toll on maintainers during the Log4Shell crisis reveals the hidden human cost of our software supply chain. Christian and his team, mostly volunteers, suddenly found themselves responsible for patching a vulnerability affecting half the internet. The pressure was immense and deeply personal. Some of us stopped sleeping. We all felt that either we fix it right now in the next few days, or we close this project. Christian Grobmeier Fixing the initial vulnerability led to the discovery of additional issues, creating what Christian describes as “a bag of water with a hole. When you patch the hole, you see another one.” Meanwhile, the community response was mixed. “On the one hand, you have people who really hate you, and on the other hand, you have people who are really behind you,” Christian explains.  Perhaps most telling: Nobody stops in to check on you. They check on the project. There’s also nobody standing up and saying, ‘hey, thank you for the good work you’re doing to remediate this issue.’ Christian Grobmeier How the GitHub Secure Open Source Fund is strengthening security The Log4Shell incident highlighted a critical gap in open source security: Maintainers often lack the training and resources to build security into their projects from the ground up. This realization sparked initiatives like the GitHub Secure Open Source Fund, which provides both funding and security training to critical open source projects. The fund has been effective and efficient as a form of proactive protection, pooled resources, and shared responsibility. Think of it as “insurance” for the open source supply chain—helping make the digital ecosystem safer and reducing risks that could impact billions of users. Christian participated in the GitHub Secure Open Source Fund security training program, and the impact was transformative. The training didn’t just provide technical knowledge—it shifted his perspective. Christian explains, “With this training, developers are no longer the weakest link. Instead, they’re the first line of defense.” This change in mindset is crucial. As Christian puts it: Ignorance is by far the worst and most critical security hole. It will basically break all software. Christian Grobmeier When asked if the GitHub Secure Open Source Fund training could have prevented Log4Shell, Christian is direct: “If this training had existed five years ago, maybe Log4Shell wouldn’t be here today.” Technical lessons: Building security by default The Log4Shell incident taught the industry several critical lessons about secure development practices: 1. Validate

We started building our accessibility governance program in 2022 when we adopted accessibility as a GitHub Engineering Fundamental, along with availability and security. Since then, we’ve continued to optimize and scale accessibility governance processes.  This post explains how GitHub Copilot empowered an accessibility program manager to build a working prototype of a critical accessibility governance process improvement and enlist engineering resources to move it from prototype to production in record time. Context The primary construct of our accessibility governance program is services: A service may represent an entire website, an entire application, or a collection of pages, features, or backend functionality.  The accessibility compliance of each service is tracked and visible to program leads. The compliance status for each service is updated weekly and service owners are notified of changes. The challenge When new services are added or the compliance status of existing services is changed, service owners are notified. However, the problem is that we did not have an effective mechanism to prompt service owners to plan, schedule, and complete the work required to reach compliance. This shortcoming had the following consequences: Service owners sometimes delayed accessibility remediation work, extending potential negative impacts for users with disabilities. Leadership could not quantify and manage accessibility compliance risk due to the lack of target dates for remediation. The solution  We used GitHub Copilot to automate a workflow that increases accountability for service owners, increases visibility  for leadership, and reduces barriers for users with disabilities. Our workflow now: Uses GitHub Actions to auto-create GitHub Issues that track accessibility remediation in service repositories when a service falls out of compliance. Includes all relevant information and guidance for service owners within remediation issues. Cross-references remediation issues with a GitHub Projects board that provides a global view for program managers and leadership. Syncs assignees between remediation issues and the project board.  Mentions stakeholders for transparency without repo spam. Auto-closes remediation issues when services are acceptable. How Copilot changed the game The traditional approach to building an internal automation like this would have meant drafting detailed requirements, prioritizing them into a team backlog, waiting for engineering capacity, and cycling through multiple sprint iterations before we saw working end‑to‑end value. That could take weeks, if not longer. Instead, we spent five to six hours in direct conversation with Copilot, rapidly prototyping and testing ideas. Our working loop was intentionally lightweight. For each iteration, we roughly followed this pattern: Framed a single rule in plain language (e.g., detecting sustained non-compliance and ensuring an issue existed or was updated with current context). Asked Copilot to scaffold or adjust code (e.g., new helper, data parsing tweak, API refinement) instead of writing everything from scratch. Used a small synthetic fixture of accessibility compliance snapshots (e.g., initial drop, continued drop, recovery) to exercise the logic locally. Reviewed the output (e.g., issue body, labels, assignees) and refined prompts to tighten naming, thresholds, or branching. Added guardrails: idempotency (i.e., skip if a valid issue was already open), simple dampening to avoid flip‑flop closures, and defensive handling for incomplete data. Logged high‑level decisions (e.g., “updated existing issue” vs “no action – compliant”) to quickly verify intent. Re-ran the fixture (plus a variation) to confirm no regressions, then commit and move to the next rule. Because each iteration was scoped to a single behavior, Copilot’s suggestions stayed relevant and we avoided big refactors. When new edge cases emerged, like transient score dips or duplicate issue creation due to renamed services, we added another short loop instead of scheduling a meeting. This rapid cadence enabled us to converge on something production‑ready without a formal project plan. From prototype to production We first built a quick prototype to reliably detect a non‑compliant service, create or update a remediation issue, and keep ownership visible. We also wanted to prove we could achieve this without any human triage. The initial goal was a controlled rollout to a small set of services in a staging environment with historically known volatility. This way we could watch behavior under real conditions before rebuilding the workflow for broad deployment in a production environment.  Our planned rollout path was incremental:  Prototype using a personal access token in a staging environment. Observe a handful of test weekly cycles in staging with mock service repositories and adjust thresholds or labels. Refactor the code and migrate to a GitHub App for proper security and scoped permissions. Deploy to production and roll out to all services that we track for accessibility compliance. Formalize governance reporting once noise was minimized. To validate, we recorded a concise end‑to‑end demo showing an input change triggering automatic issue creation, cross‑linking, assignee sync, and subsequent update on a repeated failure. That artifact gave stakeholders the ability to evaluate the full experience asynchronously. The reaction was decisive. Seeing live issues appear with clean structure and traceability accelerated approval to move beyond the prototype stage. We secured engineering partnership to productionize the flow, established a sandbox environment for hardening, and began implementing the GitHub App version with appropriate security and scale considerations. The real impact The impact came from two layers: the automation we introduced and the way Copilot changed who could build and iterate on it. Automation outcomes: Remediation issues now appear (or update) promptly instead of waiting on manual triage, allowing service owners to immediately understand the policy requirements to resolve those issues and to hold themselves accountable when exceptions are requested. Ownership, status, and cross-links live in one place, giving leadership a trustworthy snapshot without ad‑hoc spreadsheets or pings. This also strengthens the partnership between accessibility program owners and engineering teams. Duplicate or stale outreach dropped because idempotent logic and dampening prevent noisy close and reopen churn. Governance effort shifted from clerical tracking to higher‑value analysis of systemic accessibility patterns, and enabled tighter governance controls. Copilot-enabled delivery outcomes: A domain expert built the prototype, allowing engineers to stay focused on their critical roadmap work. Reduced context-switching for engineering. Partnership time was spent on security, scale, and production hardening instead of basic scaffolding.

In September, we experienced three incidents that resulted in degraded performance across GitHub services. September 15 17:55 UTC (lasting 25 minutes) On September 15, 2025, between 17:55 and 18:20 UTC, Copilot experienced degraded availability for the majority of the features. This was due a partial deployment of a feature flag to a global rate limiter. The flag triggered behavior that unintentionally limited 100% of requests, returning 403 errors. The issue was resolved by reverting the feature flag which resulted in immediate recovery. The root cause of the incident was from an undetected edge case in our rate limiting logic. The flag was meant to scale down rate limiting for a subset of users, but unintentionally put our rate limiting configuration into an invalid state. The issue has been resolved, and we are enhancing system resilience by adding traffic anomaly monitors for early issue detection and increasing coverage of rate limit scaling tests to strengthen pre-production validation. September 24 14:02 UTC (lasting 50 minutes) On September 23, 2025, between 15:29 UTC and 17:38 UTC, and also on September 24, 2025, between 14:02 UTC and 15:12 UTC, email deliveries were delayed, resulting in significant delays for most types of email notifications. While the overall incident impact from the two incidents totaled ~130 minutes, the peak delays experienced by customers was ~50 minutes. This occurred due to an unusually high volume of traffic, which caused resource contention on some of our outbound email servers. We have updated the configuration to better allocate capacity when there is a high volume of traffic and are also updating our monitors to improve our detection capabilities. September 29 16:26 UTC (lasting 67 minutes) On September 29, 2025, between 16:26 UTC and 17:33 UTC, the Copilot API experienced a partial degradation, causing intermittent erroneous 404 responses for an average of 0.2% of GitHub MCP server requests, peaking at times around 2% of requests. The issue stemmed from an upgrade of an internal dependency, which exposed a misconfiguration in the service. We resolved the incident by rolling back the upgrade to address the misconfiguration. We fixed the configuration issue and will improve documentation and rollout process to prevent similar issues. Please follow our status page for real-time updates on status changes and post-incident recaps. To learn more about what we’re working on, check out the GitHub Engineering Blog. The post GitHub Availability Report: September 2025 appeared first on The GitHub Blog. ​ Company news, News & insights, GitHub Availability Report The GitHub Blog