OpenAI Codex Review: Plans, Models, and a Planted-Bug Test

OpenAI Codex is presented as a multi-surface coding agent available through ChatGPT desktop, CLI, IDE extensions, cloud environments, and GitHub pull requests, with plan access, model availability, and security permissions varying by subscription and API-key use. The review evaluates the November 2026 version of Codex, noting recent model retirements and recommending that automated workflows explicitly pin model names. In a controlled pull-request test containing four planted defects, Codex’s CLI reviewer consistently identified an authorization flaw, an HTML injection risk, a discount-boundary error, and missing quote-error handling, providing file locations, reproduction details, and suggested fixes without changing the workspace or running tests. However, GitHub reviews reportedly show only P0 and P1 findings, meaning the two user-visible P2 issues in the test might not appear in pull-request comments, while the reviewer also did not execute the application or validate runtime behavior. A separate browser-based check detected those visible defects and passed after fixes, illustrating that code review and end-to-end verification cover different risks. Codex is characterized as useful for teams seeking a consistent agent across development environments, but its GitHub severity filtering, lack of runtime testing, and rapid model changes make supplementary CI checks, browser testing, repository review rules, and carefully configured sandbox permissions important.

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