📝 TL;DR 📝 Anthropic released the Claude Security plugin for Claude Code in beta, letting developers scan uncommitted changes or an entire repository for vulnerabilities directly from the terminal, no separate tool required. Under the hood, it runs a coordinated team of AI subagents that map the codebase, model threats, research findings, and then challenge their own results before surfacing anything to you. This is developer-only, level 3+ territory, but a genuinely practical addition for anyone shipping real code with AI assistance. 🧠 Overview 🧠 If you've been vibe coding real, shippable products with Claude Code, this addresses a gap that's been easy to overlook: generating working code and reviewing that code for security issues are two separate skills, and most people using AI to build software have only really been doing the first one. Claude Security folds a genuine security review step directly into the same terminal workflow you're already using, rather than requiring you to export code into a separate static-analysis tool or hire a security consultant for every release. This is squarely a tool for people already building with Claude Code. It won't show up in most people's daily AI use, but if you or your team ship code regularly, it's worth understanding what it actually does and doesn't catch. 📜 The Announcement 📜 The Claude Security plugin is now live in public beta for Claude Code users on Claude Enterprise, with wider rollout to other paid plans following shortly after. Organization admins enable it through the admin console at claude.ai/admin-settings/claude-code, and it runs on the same Claude inference developers already have through their existing subscription, rather than requiring a separate service or bill. Once installed, developers can scan just their recent, uncommitted changes before a commit, or trigger a full-codebase review, both directly from the terminal without switching tools. Anthropic specifically pitches this as more than simple pattern matching: the plugin traces how data actually flows across multiple files and components, aiming to catch complex, context-dependent vulnerabilities that traditional rule-based scanners typically miss because they only check for known patterns in isolation.