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OpenAI Open-Sources Codex Security CLI, Leaving the Scanner Behind a Gate

OpenAI released the command-line tool and SDK for Codex Security, formerly Aardvark, under Apache-2.0, while the underlying scanner stays limited-beta.

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OpenAI Codex open source developer tools security CLI
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Overview

OpenAI has open-sourced the command-line interface and TypeScript SDK for Codex Security, its AI-powered vulnerability scanner, publishing the code under an Apache-2.0 license on July 29, 2026, according to The Decoder. The npm package, @openai/codex-security, is described in its GitHub repository as “a CLI and TypeScript SDK for finding, validating, and fixing security vulnerabilities in your code.”

The release was not accompanied by a formal announcement. In a post quoted by Cyber Security News, OpenAI said: “We quietly released the open-source Codex Security CLI, but Hacker News found it before we had a chance to share it here… You can now use it to scan repositories, track findings across runs, verify fixes, and add security checks to CI/CD.” OpenAI’s own developer community post describes the tool similarly: “Codex Security helps security and engineering teams find, confirm, and fix vulnerabilities,” and frames the launch as “an early release,” pointing users to a quickstart guide, a cloud setup path for connected GitHub repositories, and the public GitHub repository.

What We Know

  • The CLI and SDK run on Node.js 22.13.0 or later (including the 24.x and 26.x lines) and require Python 3.10 or later, according to the GitHub repository. Installation is a standard npm command: npm install @openai/codex-security.
  • Basic usage follows a two-step pattern: npx @openai/codex-security login to authenticate, then npx @openai/codex-security scan . to run a scan against a repository, per the repository’s documentation. Authentication can go through ChatGPT sign-in or an API key, and setting the OPENAI_API_KEY or CODEX_API_KEY environment variable lets the tool run non-interactively inside CI pipelines.
  • The tool supports repository-wide assessments, targeted path reviews, pull-request diff scanning, scan history, and CI integration, according to GBHackers. Two additional commands let teams track results over time: findings list to view open findings and scans compare to diff results between two scan IDs.
  • Deep-scan mode, which runs a more exhaustive search, stops after 96 hours by default, though the duration is configurable, per the GitHub repository. The CLI also supports routing scans through alternate inference providers, including OpenRouter, Fireworks, and Amazon Bedrock.
  • Codex Security did not start as an open project. It began internally as Aardvark and became available as a research preview in March 2026 for enterprise ChatGPT subscribers, according to The Decoder.
  • What OpenAI open-sourced is the client, not the engine. TNW reports that “the command-line tool and its code are public, but access to the underlying scanner remains a limited beta for approved customers,” and that “generated patches still need a human to sign off” before they’re applied.
  • TNW frames the move as competitive positioning against Anthropic, noting the release is “a direct shot at Anthropic. Its rival launched Claude Security to do much the same job, scanning code and proposing patches.” The Decoder makes the same comparison, describing Codex Security as competing directly with Anthropic’s Claude Security.

What We Don’t Know

  • OpenAI has not published a general-availability timeline for opening the underlying scanner beyond the current limited beta, or said what criteria approved customers must meet.
  • The scale of adoption since the CLI’s release — how many repositories have been scanned or how many findings have been verified through the open-source client specifically — has not been disclosed by OpenAI.

Analysis

The split OpenAI has drawn — open client, gated engine — mirrors a pattern increasingly common among AI vendors shipping developer security tools: the interface and integration layer become open source to drive adoption and CI/CD embedding, while the proprietary model doing the actual vulnerability reasoning stays behind an approval process. For development teams, that means the CLI’s scripting and comparison features (findings list, scans compare, structured CI output) are freely inspectable and extensible, but the core capability — an AI system reading code, reasoning about exploitability, and proposing fixes — still runs through OpenAI’s own infrastructure and still requires a human to approve any patch before it merges.