fidzu : php

Drupal's volunteer Security Team has protected millions of sites for more than 20 years and its process is world-class. Bandwidth among the security engineers has always been the limiting constraint. This spring that constraint met a new kind of pressure: AI-assisted analysis is finding latent vulnerabilities at an accelerating pace.

The Drupal AI Security Initiative adds funded security capacity in response. It is funded through Alpha-Omega's Security-Engineer-in-Residence (SEIR) program, coordinated by the Drupal Association, and works alongside the volunteer Security Team, which continues its normal process throughout.

This post introduces the initiative and reports on our first six weeks. The short version: the funded fractional team model is working and has already evolved our understanding of where we want to focus next.

What changed: the economics of discovery

Drupal's attack surface is what it has always been. What has changed is the cost of finding bugs. AI-assisted analysis makes discovery dramatically cheaper. AI can produce security issue reports at a volume and can discover exploit details at a speed that any volunteer effort struggles to absorb. Our advisory data shows the rate of discovery accelerating (our next post will work through what the data suggests in detail).

Meet the Drupal AI Security Initiative Team

The initiative builds on the lessons of the Drupal 8 Accelerate Initiative, which showed that throughput efficiency depends on funding the whole contribution workflow, not just one part of it.

The Drupal security team needs fixes, not just findings of potential issues. As fixes are developed, they are collaboratively reviewed. An engineer cannot mark their own fix complete. Funding one full-time engineer would likely produce findings faster than volunteers could review them, and they would queue. So we're using the grant to fund a fractional team that covers the full path from discovery to merge on both the project and infrastructure side for Drupal:

• Drew Weber (@mcdruid) is the Fixer. He applies AI-security expertise directly to Drupal's code: scanning, writing patches, building experimental tooling, and then submitting contribution-ready work across Drupal core and the contributed-project ecosystem.

• Greg Knaddison (@greggles) and Michael Hess (@mlhess) are Reviewers: They triage submissions, review patches, advance issues, and provide the RTBC status a fixer cannot grant themselves. Both come from the existing Security Team, and the grant helps subsidize the work they would otherwise do on volunteer time.

• Neil Drumm (@drumm) handles infrastructure, focusing on Drupal.org itself. The package distribution, build pipelines, and update mechanisms are a high-consequence, specialized surface on their own.

• Tiffany Farriss (@farriss) and Tim Lehnen (@hestenet) provide program support and coordination for the Drupal Association.

Our current grant has two three-month phases: Clarity (understand the problem) and Attention (fix issues and harden the process).

Six weeks in: what we've done

We're using the funding and AI tooling to find, validate, triage, and resolve vulnerabilities faster than before, including proactively, across core, contrib, and our own infrastructure. In six weeks, the team has made contributions to more than 10 published advisories and CVEs and filed more than 30 issues. This work includes SA-CORE-2026-005, a critical PHP object-injection issue reachable via JSON:API that arrived as an external report and was coordinated to a fast release, alongside triage and remediation across dozens of findings and hundreds of inbound requests. The team also worked on rapid response/urgent issues off-hours; in one case, AI-assisted review helped find and fix a significant issue in Drupal.org code.

We're also building reusable tooling and automation prototypes that increase throughput and make our security archive searchable and actionable. That includes five Claude skills and a set of opengrep static-analysis rules, each targeting a vulnerability class, and local, open-weight tooling that processes about 40,000 historical security-mailbox emails to assign metadata like CWE mapping and flag duplicates (keeping sensitive data local). One key project outcome will be delivery of working tools the Security Team can continue to use after the initiative ends.

Drupal's grant is one of several parallel Alpha-Omega grants across open source ecosystems. Being part of this cohort has allowed us to compare notes and share tooling, successes and failures with other open source projects. So far we've collaborated most directly with Volker Dusch, who leads the equivalent effort at the PHP Foundation, and with colleagues at the Open Source Technology Improvement Fund (OSTIF), who shared their report-validator protocol for separating real findings from noise. That protocol feeds straight into our intake, and into the report standard we want to co-create next.

The counts are perhaps not the most interesting part. We've resolved more security issues (10) than the minimum number (8) our proposal had committed to over the entire six-month project. We had assumed the meat of the task would be finding and fixing vulnerabilities. It turns out that the more interesting challenge will be adapting Drupal's security process to the volume and nature of higher-quality-than-expected AI-generated and AI-assisted reports.

So far that adaptation has happened downstream, after an issue has been reported. Shepherding issues to a fix, filing CVEs, automating that filing, and automating the analysis of published advisories are important and help scale the response process. But it is all at the bottom of the funnel. The opportunity we would like to explore is higher up, at intake, where issues arrive.

We've started exploring what that might look like. In discussions with core maintainers, some design principles emerged: AI stays limited to a single triage activity per issue and no bot noise on every commit and merge request. Ideally, early intake tooling would pre-filter inbound security issue reports and run a gated check that confirms whether they include enough context and reproduction detail before they reach a human.

What's next

The next six weeks will build on what is working and push the intake question in two directions. The first is triage. The volume of incoming security issues is expected to keep growing and AI-assisted triage of that queue is an area to explore. We are interested in looking at how modern tooling can sort and deduplicate incoming issues so human attention can be focused where it's actually needed.

The second is the report itself. A clear issue report helps the Security Team and maintainer community move faster; a vague or bloated one slows everyone down. We want to explore and define what a useful AI-generated or AI-assisted security report should contain and draft a working standard, co-created with the Security Team and maintainers. If you are a maintainer or security reporter and have examples of good (or bad) AI-generated reports, please share them in Drupal Slack #security-discussion.

Six weeks of supplemental funding has already made a couple things clear. The roles the Drupal ecosystem depends on (security work as well as release management) need a durable, community-owned funding model, not one-time support. And we need to keep talking and collaborating across ecosystems like this.

Thanks

Huge thank you to Alpha-Omega for the support, funding and for access to AI tooling from Anthropic that enabled several of the findings above; to the Linux Foundation; and to the Drupal Association for coordination. And of course, none of this works without the two decades of effort from Drupal's amazing Security Team.