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Discover how php2shell vulnerabilities expose PHP servers to remote code execution and how Harness virtual patching protects endpoints while you deploy fixes. On October 21, 2026, the PHP project rushed out emergency patches for two critical flaws nicknamed php2shell (CVE-2026-109618 and CVE-2026-109619, both rated 9.2 out of 10). This isn't a bug in some plugin or a server someone set up wrong. It's in PHP itself, in the way the language has handled errors since 2012.

Learn how Harness MCP OAuth proxy mode enables enterprise developers to use their own identity provider without static tokens or shared credentials. OAuth proxy mode lets enterprises run Harness MCP behind their own identity provider, with no static tokens anywhere in the chain. Coding agents write the change. Harness agents take it through production, running the builds, tests, scans, and deployments that follow, with policy enforced on every action.

AI Budgets turns an AI cost policy into a limit customers can watch hold in real time, so overspend gets caught within days instead of surfacing on next month's invoice. With AI Budgets, the Cost Management Agent turns your AI cost policy from a document everyone signed once and forgot into a limit you can see working. ‍ In our last post, we showed why 73% of organizations have an AI cost policy, but only 13% can tell whether it's working.

VegaLoad is a free, open-source load testing tool for agentic developers. Your agent writes the test, runs it, and gives you a report. VegaLoad is a free, open-source load testing tool. You ask your agent for a test. It writes the file, runs it, and gives you a report.

The problem: our AI spend was growing faster than our understanding of its ROI. Like most engineering organizations, Harness went from "a few people are trying out AI coding tools" to "AI tooling is a line item in the monthly finance review" in a matter of months. The cost itself wasn't really the problem.

AI writes code faster, but CI still repeats costly work. See how Harness CI cuts unnecessary tests, recompilation and dependency downloads. Six months ago, your team adopted an AI coding assistant. Pull requests doubled. Generated code started landing in branches faster than reviewers could read it. The CI pipeline kept up for about three weeks. Now the queue backs up by mid-morning. Builds take 38 minutes. 4,000 tests run on every commit when 60 are relevant.