Sovereignty is moving from buzzword to buying criterion. The European Commission’s proposed Cloud and AI Development Act (CADA) would set assurance levels for the cloud and AI services public authorities buy, defining where data should live and who can access it. Parts of it could reach private entities too.GitLab submitted its own comment on CADA because we share the goal of strengthening Europe’…
A critical vulnerability needs a fix and a map. The fix is usually fast. Finding where the vulnerable component lives, who owns it, and whether it's exposed to the Internet is what costs you time. Most security teams piece that answer together from scanner reports, spreadsheets, and a Slack thread asking who owns a service nobody recognizes, and you stay exposed for every hour that takes.GitLab Or…
An agent’s true cost isn’t only the tokens it burns. It’s the review that calls your code “safe to merge” after seeing the diff, but without any context on the pipeline. We call that artificial confidence, not artificial intelligence. Agents guess because code is all they can see; they can’t see the merge request that last changed it, the pipeline that ran it, the vulnerability it carries, or the…
We're entering a new era of application security. Frontier AI models are uncovering complex flaws that traditional tools overlooked, and attackers are using the same models to exploit them faster. Remediation isn't keeping pace: Verizon's 2026 Data Breach Investigations Report (DBIR) measures the median time to fully remediate a known-exploited vulnerability at 43 days, up from 32 in 2025.Security…
Developers mostly use GitLab Duo Agent Platform where they already work: the IDE, merge requests, and issues. But DevSecOps teams also support the business systems the rest of the company runs on, and those systems break too.In Salesforce, for example, when a validation fails, a callout times out, or a null slips through a trigger, the error gets logged, but the log doesn’t explain the cause. An e…
GitLab is not affected by CVE-2026-112900 or the other vulnerabilities fixed in Pillow 13.0.0. GitLab products do not include the Pillow library, and our Security team has found no evidence of impact to GitLab-managed infrastructure. GitLab.com, GitLab Self-Managed, and GitLab Dedicated are not impacted.Pillow is one of the most widely used image libraries in the Python ecosystem. It often sits se…
GitLab is not affected by PHP2Shell (CVE-2026-109618 and CVE-2026-109619). The GitLab application is not written in PHP and does not run it to serve web requests, and our Security team has found no evidence of impact to GitLab-managed infrastructure. GitLab.com, GitLab Self-Managed, and GitLab Dedicated are not impacted.PHP is a widely used programming language that runs on web servers and powers…
Broken object-level authorization ranks first on the OWASP API Security Top 10, and OWASP says that traditional static application security testing (SAST) tools struggle to find access control flaws. A missing authorization check is an absence of code, so a scanner that matches patterns has nothing to match. You can find the gap only if you already know what the endpoint is meant to enforce.We're…
AI coding agents are writing and changing code, from GitLab Duo Agent Platform agents to externally connected agents like Claude Code, Cursor, and Codex. You are accountable for what those agents do, yet most of their activity stays invisible: which agents ran, who used them, and what they touched. Teams often struggle with understanding whether a developer used a tool or model that is approved or…
AI agents are sending more and more code into review queues built for human-paced work. A coding assistant can speed up code writing, but it can’t show you whether review, security checks, and release approvals can keep pace with all that extra code. AI-native engineering — the practice of intentionally designing the engineering process so people and agents each do what they are best suited to — s…
When code becomes abundant trust becomes scarce.That is the argument GitLab CEO Bill Staples made in his recent essay: AI is collapsing the cost of producing software, and everything around production, including context, verification, governance, and evidence, becomes proportionally more important. Both security and collaboration are key elements to producing trust. AI widens what has to be truste…
You already have an AI assistant. It completes your code, explains code you didn’t write, and maybe even drafts a whole feature while you sip your coffee. An agent on GitLab picks up where your editor stops.The AI in your editor only knows what's open in your editor. It doesn't know that your merge request has been sitting in the queue for two days, or that your pipeline failed for the third time…
AI agents have changed how much code a team can produce in a day. Review queues haven’t kept up, and delivery stalls while changes wait for a closer look. The usual workarounds (reminders, reviewer rotations, assigning a team) buy a little time and then burn people out.Client-side review with AI helps the author by catching issues as the code is being written, before a merge request even opens. Bu…
Agents are working across your team's software development lifecycle, but most still operate as separate tools, leaving the context, handoffs, and permissions for your team to stitch together. When your agents use APIs for these disparate tools, they often guess at endpoints and use up their context window on extra calls. The GitLab MCP server, now generally available with GitLab 19.5, connects yo…
The code is written. Now who reviews it?The volume of code has never been greater, but delivery hasn’t kept pace. At Meta, code changes on internal platforms grew 220% year over year, while changes that reached users as new features grew only 36% (Reuters, August 2026). Every one of those changes has to clear review before it reaches users.Automated review absorbs much of that load. Code Review Fl…
Credentials often start in an engineer's local dev environments, then get copied into unscoped CI/CD variables to keep pipelines running. Stored there as a raw value rather than a reference to a scoped secret, a signing key reaches every job in the pipeline — compromised or not.Standing up a standalone vault narrows that exposure but requires mirroring every change to project membership, protected…
As AI models take on longer, multi-step work, the challenge is putting that power to use across the software lifecycle without adding risk or cost. Teams need to be able to move faster, within enterprise controls and guardrails.Google's Gemini 4 Argon, now available on GitLab Duo Agent Platform, was built for that kind of work. With Argon, your team can delegate long, multi-step actions and receiv…
AI agents are fundamentally changing the economics of software development. As the cost of producing code falls, the volume of changes moving through the software factory is accelerating beyond what infrastructure designed for human developers was built to handle.GitLab anticipated this shift and began developing next-generation source code management (SCM) to support thousands of concurrent agent…
In a recent engineering blog post, Anthropic described how agentic coding pushed its source control past what Git was designed for: Merges to its monorepo grew from around 2,500 a day in May to more than 30,000 a day by September. Its team built an in-house system, Scion, around the assumption that nearly all clients are agents that are always online.Anthropic is clear that Scion isn't meant to be…
Costs multiply with every task you hand to an agent. Each task consumes tokens and credits, and as agents spread across your teams and automations, consumption can grow faster than usage. If you run the platform, you own that growth, including which teams and workflows drive it and whether the usage ships work.At GitLab Transcend, we introduced the governed software factory, our architecture for a…
AI coding tools have changed how fast software gets written, but not how fast it ships. As code generation accelerates, the bottleneck moves downstream into verify, review, and ship. Those stages still run on human time. While agents write fixes in minutes, delivery slows on constant wait times and manual handoffs. Reviewer signoffs, pipeline alerts, scan triage, and step-by-step prompting are exa…
If you run application security across more than one top-level group, getting a single organization-wide view of your risk has meant manually pulling together data. That is operational work rebuilt in spreadsheets and one-off scripts every time someone asks. This manual work is easy to get wrong, and is often out of date the moment it is published. Every hour spent assembling that view is an hour…
At Transcend, we made over a dozen announcements across all four layers of our architecture for agentic software engineering: agent orchestration, data and context, DevOps workflows, and governance and security. Watch the replay, or read on for what shipped and how to turn on the new capabilities.At a glanceWhat we launchedRelease statusWhat it doesGoal-driven flows (/goal)GA this monthCarry a cha…
Every software build is assembled from open source packages, base images, and libraries, with your code on top. When one of those components is missing or has drifted, the build fails. At agent speed, those failures multiply, and no team can clear them as manual exceptions.Project-by-project registry sprawl makes it worse because retention rules, storage limits, and publish access all live separat…
Malicious packages could turn up in the public registries your builds depend on. Increasingly, the developer is not the one choosing what gets pulled in because AI coding agents now add open source dependencies on their own, often without anyone reviewing or even seeing what landed in the build.That combination means a malicious or vulnerable package can enter your build without anyone deciding to…
GitLab's engineering team builds a lot of our own internal tooling, but one platform in particular forced us to rethink how we handle authorization: our internal GRC tool that serves two very different customer bases under one roof — our Security Compliance team and our Internal Audit team.If you're building or maintaining an internal Django tool that's starting to serve more than one audience — d…
GitLab's Threat Research Group discovered a command execution vulnerability (GHSA-grg2-7gc6-36m6, CVE-2026-102437) in DeepSeek-Reasonix Studio, a desktop git client designed for developers pairing with AI coding assistants. The flaw, called ConfigPoisoning, could allow attacker-supplied code to execute when a developer views a file's diff. To address this vulnerability, you should update to DeepSe…