Codex CLI 0.159 Adds Instant Interrupt, Better Mermaid Rendering, Safer Sandboxes, and Draft Recovery

Codex CLI 0.159 is a practical terminal-workflow release, not a magic safety switch

OpenAI published the stable Codex CLI release tagged rust-v0.159.0 on September 29, 2026, with changes that matter most to people who live inside long-running terminal sessions: opt-in mid-response steering, a cleaner start screen, more predictable warning behavior, better transcript navigation, richer native Mermaid rendering, thread-history pagination for app-server clients, Windows launch fixes, transcript-copy improvements, draft recovery, and tighter filesystem protections around approved commands. The release is best understood as a developer-experience and control-surface update for Codex CLI, not as proof that agentic coding has become automatically safe, correct, reversible, or enterprise-ready in every environment.

The most attention-grabbing item is instant_interrupt. OpenAI’s release notes describe it as an opt-in capability that lets new input steer Codex during model responses or long-running code-mode calls. That wording matters. “Steer” does not mean “safely cancel every operation,” “undo filesystem changes,” “verify task correctness,” or “roll back a partially executed plan.” For developers and administrators, the conservative interpretation is that the feature can improve interactive control during a turn, while the surrounding engineering discipline still has to provide Git checkpoints, permission boundaries, diff review, sandbox policy, and rollback procedures.

This developer guide covers OpenAI Codex 2026 features including remote SSH setup, Codex Hooks, mobile steering, and enterprise deployment considerations. The OpenAI Codex 2026: Remote SSH, Hooks, and Mobile Steering — Complete Developer Guide article is a focused companion for Codex Mid Turn Steering because the marker points to steering an active Codex session, and this target is specifically about Codex steering rather than general asynchronous agent steering.

This release also refreshes the terminal user interface. New sessions use a compact welcome screen, headers are made more consistent, the warnings viewer dismisses reviewed warnings unless they are retained, and users can scroll the transcript while deciding whether to implement a plan. Those changes are operationally useful because Codex sessions often mix planning, command output, diff context, and review prompts. In practice, less visual noise and better transcript access can reduce the chance that an operator approves a plan without seeing relevant context, but the release notes do not claim these interface changes independently prevent unsafe approvals.

Codex CLI 0.159 also improves native Mermaid rendering. OpenAI’s release notes state that native Mermaid syntax support now covers more flowchart edges, labels, and node groups. That is useful for architecture sketches, dependency diagrams, migration plans, and system-design notes generated or reviewed inside Codex workflows. It should not be treated as architectural validation. A diagram that renders cleanly can still describe an insecure trust boundary, a missing queue, an impossible deployment dependency, or a data-flow that violates policy. Rendering is presentation; validation is a separate engineering and review process.

This playbook explains how to validate AI-generated code before release using Blacksmith, Codex-specific validation strategies, and automated testing pipelines. The How to Validate AI-Generated Code Before It Ships: Complete Playbook Using Blacksmith, Codex, and Automated Testing Pipelines article is a focused companion for Mermaid Diagram Validation because although no candidate is specifically about Mermaid, this is the strongest practical match for validating generated developer artifacts before shipping.

The release includes an app-server change for clients that need to work with longer conversations: app-server clients can paginate thread history from a specific item. OpenAI’s release note is narrow and should be read that way. Pagination from a specific item is a thread-history retrieval improvement for app-server clients; it is not a claim about retention duration, administrative eDiscovery, legal hold, workspace export, audit completeness, or immutable logging. Teams that need regulated evidence handling should still define their own retention, export, access-review, and chain-of-custody controls around the official product behavior available to their plan and workspace.

Several 0.159 changes also address reliability and recovery in ordinary coding sessions. Blank sessions retain drafts during task switching, threads can be archived and listed before a first turn, and copying transcript selections preserves Markdown tables, formatting, and significant whitespace. These are small-sounding details with real operational value: a half-written task description, a pasted incident table, or a terminal transcript with indentation can materially change what Codex is asked to do. Preserving drafts and formatting reduces avoidable operator error, but it does not remove the need to review the final prompt, copied transcript, or generated patch before relying on it.

Security teams should pay close attention to the release’s sandbox-related items. OpenAI’s release notes state that approved commands retain explicit filesystem denials, and that .aws directories are protected by default under writable roots. Those are important guardrails because approval flows can otherwise create accidental escalation paths when an operator allows a command that touches broader directories than intended. They are not blanket guarantees that secrets are safe, that arbitrary paths are protected, that network access is harmless, or that environment-variable forwarding is risk-free. Filesystem protections must be evaluated together with the sandbox mode, approval settings, network policy, trusted-project status, and any environment variables made available to Codex.

What OpenAI says changed in the September 29 stable release

The primary source for the release is OpenAI’s GitHub release page for rust-v0.159.0. Supporting official documentation for operational interpretation includes OpenAI’s Codex CLI documentation, Codex configuration documentation, and Codex agent approvals and security guidance. The release is not framed by OpenAI as a single headline feature; instead, it combines interaction improvements, rendering support, recovery behavior, app-server history access, cross-platform fixes, and sandbox hardening.

For teams maintaining internal Codex runbooks, this release should trigger a controlled documentation update. Any onboarding guide that mentions automatic follow-up prompt suggestions, the tui.prompt_suggestions setting, or the bundled plugin-creator skill should be reviewed because OpenAI’s release notes state those items were removed. Any guide that teaches users to start new sessions, review warnings, copy transcript evidence, or inspect plans before implementation should be updated to reflect the compact welcome screen, consistent headers, warning-review behavior, transcript scrolling during plan decisions, and improved copy preservation.

For enterprise administrators, the most important editorial stance is to separate user-interface improvements from control guarantees. A compact welcome screen can improve focus, but it is not an access-control system. A warning viewer can reduce clutter after review, but it is not a substitute for incident tracking. Transcript scrolling can support better approvals, but it is not a formal change-management workflow. Mermaid rendering can make architecture discussion easier, but it is not a threat model. Pagination can help app-server clients retrieve history, but it is not a retention policy. Filesystem denials and protected .aws directories are meaningful, but they do not eliminate the need to define, test, and audit approval scope.

The mid-turn steering change is opt-in and should be governed like a control surface

OpenAI’s release notes describe instant_interrupt as opt-in. That means the safe default assumption for documentation, training, and support is that users should not expect it to be available unless they have intentionally enabled it in an environment where their plan, app, workspace policy, and version support it. The exact operational behavior can vary by configuration and rollout, so release adoption should begin with version confirmation and a small test in a non-critical repository before anyone depends on it during high-risk refactors, deployments, migrations, or incident response.

The practical value of mid-turn steering is easy to understand. If Codex begins answering with the wrong target file, starts reasoning from a stale assumption, or enters a long-running code-mode call that the operator realizes is based on a bad premise, the operator may want to provide a new instruction immediately rather than waiting for the turn to finish. In a terminal coding assistant, latency and turn boundaries affect real human behavior: when users cannot intervene, they may kill the process, open another session, approve too quickly, or lose context. A feature that permits steering during a model response or long-running code-mode call can reduce friction in those moments.

The safety boundary is equally important. Mid-turn steering changes the instruction stream; it does not prove that any already executed command was harmless, that partially generated code is correct, that an interrupted plan left the repository in a clean state, or that the model incorporated the new instruction exactly as the operator intended. If Codex has already edited files, run tests, started a process, or interacted with a tool, the operator must inspect the state after steering. The correct post-steering habit is to ask, “What changed before my new instruction was received, and what evidence confirms the workspace is still in the expected state?”

Operational warning: Treat mid-turn steering as an interaction feature, not as a rollback feature. If the task could modify files, start services, alter dependencies, send data, change permissions, or affect an external system, require a human review of diffs, command history, and relevant logs after steering. Human approval remains mandatory for external messages, submissions, payments, purchases, bookings, destructive actions, permission changes, publication, legal commitments, campaign launches, and other consequential operations.

A conservative enterprise rollout can treat instant_interrupt like any other new control surface: document where it is allowed, decide who can enable it, define which repositories are eligible, and collect examples of both successful steering and confusing edge cases. The goal is not to slow down expert users; it is to prevent teams from teaching the feature as a safety guarantee. If a developer says, “We can let Codex run because we can interrupt it,” the runbook should correct that assumption. A better policy sentence is: “We may use mid-turn steering to redirect Codex, but we still require explicit approvals, sandbox constraints, Git checkpoints, and post-action verification.”

Recommended adoption checklist for instant_interrupt

1. Confirm the installed Codex CLI version. Do not assume a workstation, container image, remote development host, or CI helper has the 0.159 release until the local version has been checked by the operator or administrator.
2. Enable only in a non-production test workspace first. Use a small repository with disposable branches so operators can observe steering behavior without risking important files or external systems.
3. Create a Git checkpoint before testing. Commit, stash, or otherwise preserve a known-good state so post-test diffs can be reviewed and reset if needed.
4. Use harmless steering prompts first. Test instruction changes such as “pause and summarize your current assumption” or “switch to explaining the plan instead of editing files,” rather than destructive or external actions.
5. Inspect command history and diffs after steering. Determine what happened before, during, and after the new input was accepted.
6. Record confusing cases. If the operator cannot tell which instruction governed a specific edit or command, treat that as a documentation and training issue before broader rollout.
7. Keep high-risk approvals separate. Do not let the existence of mid-turn steering weaken approval requirements for filesystem changes, network-enabled work, permission changes, deployments, or external communications.

A sample internal training exercise can demonstrate the distinction between steering and rollback without touching sensitive systems. Create a disposable branch, ask Codex to draft a small README change, steer it mid-response to summarize instead of editing, and then inspect the working tree. The point of the exercise is not to benchmark response speed; it is to teach operators that they must verify whether a file changed before the steering instruction took effect. That habit is more valuable than any single demonstration outcome because it applies across code-mode calls, generated patches, and tool-assisted workflows.

The refreshed terminal interface targets attention, review, and recoverability

OpenAI’s release notes say new sessions now use a compact welcome screen and consistent headers. In a terminal tool, this kind of polish has practical consequences because users often work inside split panes, small remote shells, screen-sharing sessions, or long scrollback buffers. A compact welcome view can preserve more vertical space for the prompt, plan, warnings, and command output. Consistent headers can make it easier to distinguish assistant reasoning summaries, tool output, approval prompts, and transcript sections, especially when users are switching between multiple repositories or terminal tabs.

The warnings viewer change is also workflow-relevant. OpenAI states that the warnings viewer dismisses reviewed warnings unless retained. For an expert user, dismissal after review can reduce repeated attention drain; for a regulated or high-risk team, it can create a training requirement. “Dismissed from the warnings viewer” should not be taught as “resolved.” If a warning identifies a policy issue, risky permission, untrusted project, or unusual environment condition, the operator should record the decision where the team’s process requires it. Retaining a warning can be appropriate when it must remain visible until a mitigation is complete.

Transcript scrolling while deciding whether to implement a plan is one of the most important usability changes in the release. Plan approval is a decision point where users need context: what files were discussed, what constraints were given, whether the repository is trusted, what risks the assistant named, and whether earlier user instructions prohibited certain actions. If the user can scroll the transcript during that decision, the approval can be more informed. The release note does not claim that Codex will surface every relevant risk automatically, so the operator still owns the review.

A practical approval pattern is to require three checks before implementing a plan: scope, reversibility, and evidence. Scope means the plan is limited to files, commands, systems, and data the user is authorized to use. Reversibility means the user has a Git checkpoint, test fixture, backup, or manual rollback path appropriate to the change. Evidence means the plan references enough repository context, test expectations, and constraints for the user to understand why those steps are being proposed. Transcript scrolling supports this pattern because the operator can look back before deciding, but it does not replace the decision.

Recommended plan-review prompt before implementation:

Before implementing, restate:
1. The exact files or directories you expect to read or modify.
2. Any commands you expect to run.
3. Any network access, credentials, secrets, or environment variables you believe are unnecessary.
4. The rollback path if your proposed change is wrong.
5. The tests or checks that should be run after the change.

Do not modify files until I approve the implementation plan.

This prompt is a recommendation, not an OpenAI-provided release note. It helps turn transcript scrolling and plan review into a repeatable operator habit. Teams can adapt it for their own repositories, but they should not include real secrets, credentials, private keys, customer identifiers, privileged legal material, or unnecessary confidential content in the prompt. If the task requires sensitive data, the safer default is to minimize what is shared and use approved internal procedures for secure handling.

Mermaid rendering improves native diagrams, but architecture still needs validation

OpenAI’s release notes state that Codex CLI 0.159 adds native Mermaid rendering support for more flowchart edges, labels, and node groups. Mermaid is commonly used to express software flow, service dependencies, state transitions, approval paths, and incident-response procedures in text form. Better native rendering inside Codex can make generated or reviewed diagrams easier to inspect without leaving the coding session, particularly when a developer is discussing a refactor, documenting a service boundary, or explaining a plan to a teammate.

The operational benefit is strongest when diagrams are treated as review artifacts rather than decoration. A developer can ask Codex to produce a Mermaid flowchart for a proposed migration, render it natively, and then compare it with the repository’s actual modules, infrastructure configuration, and security requirements. The visual form may expose missing error paths, ambiguous ownership, or unreviewed external calls. The diagram’s successful rendering only confirms that the syntax is supported well enough to display; it does not confirm that the depicted system is true, complete, secure, or compliant.

Example Mermaid review request:

Draft a Mermaid flowchart for the proposed authentication refactor using only the files and notes already discussed in this session.
Label each edge with the condition that triggers it.
Group nodes by trust boundary.
Mark any assumption that is not directly supported by repository evidence as "needs verification."
Do not treat the diagram as implementation approval.

This sample prompt is useful because it asks for labels, groups, and uncertainty markers, which match the kinds of Mermaid improvements OpenAI describes while preserving the separation between rendering and validation. A secure design review should still inspect whether tokens are stored safely, whether authorization is enforced server-side, whether logs avoid sensitive content, whether failure modes are handled, and whether dependencies match the deployed environment. A diagram can guide those questions, but it cannot answer them by existing.

Teams should also define a lightweight validation checklist for diagrams that Codex generates. For example, require that every external system have an owner, every data store have a classification, every edge crossing a trust boundary have an authentication or authorization note, and every asynchronous queue have a failure and retry path. Those checks are recommendations, not claims about Codex’s built-in validation behavior. They help prevent a visually polished Mermaid diagram from becoming a substitute for architecture review.

The main takeaway for advanced Codex users is that richer native rendering can shorten the loop between explanation and review. It can help users see the proposed system more clearly while they are still inside the terminal. It should not change the approval threshold for architecture changes, access-control changes, production migrations, customer-data handling, or compliance-sensitive workflows. The more persuasive the diagram looks, the more important it is to verify its claims against actual evidence.

App-server thread pagination and transcript copying improve evidence handling, with limits

OpenAI’s release notes say app-server clients can paginate thread history from a specific item. For tool builders and internal platform teams, that can matter when building clients that need to display, resume, inspect, or synchronize longer Codex threads without treating the entire conversation as one undifferentiated blob. Starting from a specific item can support more efficient navigation and targeted retrieval in clients that integrate with Codex’s app-server behavior.

The safe interpretation is deliberately narrow. Pagination is a retrieval capability; it is not a governance framework. The release note does not establish legal retention rules, prove completeness for audit, define who can access which histories, or promise immutable evidence preservation. Organizations that need incident evidence, legal review, regulated development records, or internal audit trails should define those controls separately and verify what their plan, workspace, app-server implementation, and policies actually provide.

The transcript-copying improvement is easier for individual users to feel immediately. OpenAI states that copying transcript selections preserves Markdown tables, formatting, and significant whitespace. That matters when the transcript contains a test matrix, a migration checklist, a Markdown table of files and risks, a code-adjacent indentation block, or a prompt contract that depends on spacing. Losing table structure or whitespace can convert useful evidence into ambiguous notes. Preserving it reduces cleanup and reduces the chance of misquoting the session in a pull request, incident note, or internal ticket.

Preserved formatting does not mean the copied content is safe to share. Before pasting a transcript into a ticket, chat room, pull request, vendor support request, or public issue, the user should review it for secrets, internal hostnames, personal information, customer data, privileged communications, unreleased product details, and security-sensitive implementation details. The same warning applies to copied Mermaid diagrams, command output, stack traces, and configuration snippets. Convenience increases the need for a pre-share review because copying becomes faster and more faithful.

Recommended transcript-sharing checklist:

- Confirm the destination is authorized for this content.
- Remove secrets, tokens, private keys, credentials, and unnecessary environment details.
- Remove personal data and customer data unless sharing is authorized and required.
- Preserve enough context to avoid misleading reviewers.
- Mark uncertain model-generated statements as unverified.
- Include the Codex CLI version and repository branch when relevant.
- Keep the original transcript available according to your team's retention policy.

This checklist is a recommended workflow, not an OpenAI product guarantee. It is designed for teams that use Codex transcripts as engineering evidence while recognizing that model outputs can contain assumptions, partial context, or sensitive text supplied by the user. The strongest evidence package usually includes the user’s prompt, Codex’s plan, approved commands, resulting diffs, test output, and human review notes, rather than a transcript excerpt alone.

CE113 explicit opt-in boundary: instant_interrupt is an opt-in capability; it is not enabled by default. Enabling it allows new input to steer an active turn, but does not prove that an interrupted task is correct, safe, complete, or automatically reversible.

Cross-platform fixes matter most when Codex is wired into real developer machines

OpenAI’s Codex CLI 0.159 release notes describe a cluster of operational fixes that are less visually dramatic than native Mermaid rendering or mid-response steering, but they are the changes most likely to affect daily reliability in mixed Windows, macOS, proxy, and sandboxed development environments. The release notes specifically call out Windows launch fixes for MCP servers, code-mode hosts, and piped commands; fallback behavior for restrictive launchers; Markdown-preserving transcript selection copies; draft retention for blank sessions; pre-first-turn thread archiving and listing; local ChatGPT sign-in reliability; macOS TLS access fixes for network-enabled sandboxes and proxy-dependent remote environments; preservation of explicit filesystem denials on approved commands; and default protection for .aws directories under writable roots.

The practical reading is narrow: Codex 0.159 reduces several failure modes that made terminal-agent workflows feel brittle, especially when a session crossed process boundaries, approval boundaries, or platform boundaries. It does not remove the need to decide which project is trusted, which directories are writable, whether network access is appropriate, which environment variables may be forwarded, or whether a human reviewer has approved the final command, diff, message, submission, release, purchase, permission change, or other consequential action.

This guide covers configuring Codex auto-review mode, sandbox rules, network policies, and identity management for secure AI-assisted development. The How to Configure Codex Auto-Review Mode and Sandbox Rules for Secure AI-Assisted Development article is a focused companion for Codex Sandbox Permissions because the marker is directly about Codex sandbox permissions, and this article focuses on sandbox rules and secure Codex configuration.

Windows console-window fixes reduce launch noise, not review obligations

OpenAI’s 0.159 release notes say the release fixes Windows launches to suppress stray console windows for MCP servers, code-mode hosts, and piped commands. That is a quality-of-life improvement for teams using Codex in Windows terminals, integrated shells, or automation-adjacent workflows where unexpected windows can interrupt focus, confuse a screen share, or make it unclear which process owns a command.

The security interpretation should stay conservative. A suppressed stray console window is not the same as a suppressed process, a denied permission, or a sandbox guarantee. If Codex starts an MCP server, invokes a code-mode host, or runs a piped command, administrators still need to know what process is being invoked, what working directory it sees, what files are writable, what environment variables are exposed, and whether network access is enabled by local policy. A cleaner launch path reduces distraction; it does not prove that a command is safe.

For enterprise administrators, this matters because console-window behavior often becomes an informal signal. A user may trust a command more because it no longer flashes a separate terminal window. That is the wrong decision rule. The correct decision rule is whether the command and its requested capabilities match the task, whether the repository is trusted, whether the writable root is narrow, whether protected paths remain protected, and whether the human reviewer understands the diff or side effect before approving it.

Teams that previously banned or discouraged Codex usage on Windows because helper processes opened visible windows should still run a staged canary. A practical canary is to select one non-production repository, confirm the installed Codex version, run a simple read-only request, run a write request in a disposable branch, verify the absence of stray windows, inspect the transcript, and confirm that approval prompts still show the expected command, path, and permission boundary. The point is to validate both usability and control visibility, not merely to confirm that the old annoyance disappeared.

Restrictive launchers may fall back to embedded mode, so teams should test the degraded path

The release notes also state that restrictive launchers may fall back to embedded mode. That statement is operationally important because many enterprise endpoints are not plain developer laptops. They may have application control, endpoint detection, shell restrictions, network inspection, remote desktops, managed certificates, or policy wrappers that change how child processes start.

Fallback behavior should be tested, documented, and treated as a separate operating mode. If a launcher prevents the preferred process model and Codex uses an embedded fallback, the team should verify whether logging, approvals, sandbox restrictions, working-directory assumptions, and network behavior still look the same to the user and the administrator. Do not assume that a successful fallback is equivalent to the primary path unless you have evidence from the local environment.

A safe enterprise rollout should include one positive test and at least three negative tests. The positive test confirms that Codex can complete a harmless task in the approved launcher. The negative tests confirm that Codex cannot write outside approved roots, cannot read or write protected directories, and cannot access blocked network resources when policy says network access should be unavailable. Negative tests are essential because cross-platform process fixes can otherwise be mistaken for a broader safety improvement.

Recommended launcher test plan, not an OpenAI command:

1. Confirm the Codex CLI version in a non-production environment.
2. Start Codex from the standard enterprise launcher.
3. Ask for a read-only repository summary.
4. Ask Codex to propose, but not apply, a small documentation change.
5. Approve a write only inside a disposable branch and approved writable root.
6. Attempt a write outside the approved root and confirm denial.
7. Attempt access to a protected directory and confirm denial.
8. Attempt a network operation when network is disabled and confirm failure.
9. Save the transcript, command output, and policy evidence for rollout review.

This workflow is a recommendation for local validation, not a claim about a universal Codex permission model. OpenAI’s configuration documentation describes configuration precedence and notes that project configuration is skipped for untrusted projects. That means the same repository, command, or profile can behave differently depending on trust status, CLI overrides, selected profiles, user configuration, cloud-managed defaults, system configuration, and built-in defaults. Administrators should test the exact combination they intend to support.

Markdown-preserving transcript copies improve audit packets and handoffs

OpenAI says 0.159 preserves Markdown tables, formatting, and significant whitespace when copying transcript selections. This is a concrete documentation improvement for developers and reviewers who turn Codex transcripts into pull-request notes, incident records, change-management packets, security reviews, or internal support tickets.

Before this type of fix, copying a terminal transcript could flatten a table, collapse indentation, or obscure whether a line was code, output, prompt text, or a model suggestion. Preserving formatting makes it easier to show what Codex proposed, what the user approved, what command ran, what output appeared, and what follow-up reasoning was captured. That matters when a team must reconstruct why a code change was accepted or rejected.

However, transcript copying can also move sensitive material into places with weaker controls. A copied selection may contain filenames, internal service names, snippets of proprietary code, terminal output, stack traces, or environment details. Teams should instruct users to redact secrets and unnecessary identifiers before pasting transcripts into tickets, chat channels, vendor support portals, legal files, classroom examples, or public repositories. Better formatting is not a data-loss-prevention policy.

A strong handoff format is to include the task objective, repository or project identifier approved for internal use, Codex version, trust status, active profile name if policy permits sharing it, requested write roots, network setting, commands approved, command outputs, diffs reviewed, tests run, and the named human approver. That packet should be stored in the team’s normal system of record, not in an ad hoc personal note that disappears when an employee changes roles.

Blank-session draft retention helps task switching, but abandoned drafts still need hygiene

OpenAI’s release notes say blank sessions retain drafts during task switching. This is a small interface fix with an outsized effect on knowledge workers and developers who begin a prompt, jump to another terminal or thread, and return later. Losing an unsent prompt can waste time; retaining it reduces friction and helps users finish a well-scoped instruction instead of rushing a vague replacement.

Draft retention also changes the privacy and operational hygiene of terminal usage. An unsent draft may contain a summary of a security issue, a proposed refactor, a customer-impact note, a legal question, or a path to a sensitive file. Even if it has not been submitted to the model, it can still be visible on the local screen, captured in a screenshot, exposed during screen sharing, or recovered by someone using the same workstation session. Teams should treat retained drafts as local working material that needs ordinary workstation controls.

For shared machines, classrooms, labs, pair-programming stations, or recorded demos, the conservative policy is simple: clear drafts before handing off the session, stop screen sharing before composing sensitive prompts, and avoid drafting content that includes secrets, personal data, privileged communications, or confidential customer material unless the environment is approved for that content. The release improves recovery; it does not convert a terminal into a secure note vault.

Operational recommendation: write prompts as task descriptions, not data dumps. A good retained draft says “summarize the failing test output already in this repository and propose next steps.” A risky retained draft pastes credentials, customer records, private keys, medical information, legal strategy, or confidential incident details that the task does not require.

Teams should also decide whether retained drafts belong in training and support procedures. Help-desk staff need to know that a user may report “Codex remembered my unsent prompt” after task switching. Security staff need to know that retained drafts are a local interface behavior to include in screen-share and shared-device guidance. Educators need to tell students not to leave half-written prompts containing personal or assessment material on lab machines.

Pre-first-turn archiving supports cleaner queues and evidence discipline

The release notes say threads can be archived and listed before a first turn. This matters for users who create a thread while triaging work but decide not to start it, or who open sessions for several possible tasks and later need to clean up. Pre-first-turn archiving makes thread management more consistent because a thread does not have to contain a model exchange before it can be organized.

The limitation is equally important: an archived empty or near-empty thread is not evidence that no decision occurred outside Codex. A developer might open a thread, draft an instruction, abandon it, and then perform the change manually. A security reviewer might create a thread for an investigation but move the real analysis into another tool. If the organization uses Codex transcripts as part of change evidence, it should define what counts as the authoritative record for a decision.

A practical rule is to treat Codex thread archives as session-management metadata, not as a complete audit system. For consequential work, the authoritative record should include the repository branch, commit hashes, reviewed diffs, test results, approval records, deployment ticket, incident ticket if applicable, and the human owner. Codex transcripts can enrich that record, but they should not replace the established change-management source of truth.

Pre-first-turn archiving is especially helpful for founders and small teams that use Codex across many projects. It allows them to keep a cleaner workspace without forcing meaningless first messages just to make a thread manageable. The disciplined habit is to archive unused threads, label active work through the team’s approved system, and preserve transcripts only when they contain material relevant to a decision, review, or reproducibility requirement.

Local ChatGPT sign-in reliability is a workflow fix, not an authorization shortcut

OpenAI’s release notes mention more reliable local ChatGPT sign-in. In practice, sign-in reliability reduces interruptions when a developer authenticates the CLI locally and needs to return to a coding task. It can also reduce support tickets caused by sign-in loops or inconsistent local authentication behavior.

Administrators should still separate authentication from authorization. A successful ChatGPT sign-in confirms that a user has authenticated through the supported flow available to that account and environment; it does not mean every repository, secret, command, network destination, deployment operation, or organizational data source is approved for Codex use. Workspace policy, project trust, sandbox settings, command approvals, local configuration, and human review remain separate layers.

For managed environments, sign-in reliability should be paired with onboarding language that explains the permitted use cases. A developer may be allowed to use Codex for local test generation and documentation updates but not for production incident response, customer-data analysis, regulated records, privileged legal material, payment actions, access-control changes, or external communications without additional approval. The CLI becoming easier to sign into does not expand the user’s mandate.

macOS TLS and proxy fixes help network-enabled sandboxes in constrained environments

OpenAI says Codex 0.159 fixes macOS TLS access for network-enabled sandboxes and proxy-dependent remote environments. That is a meaningful reliability improvement for organizations where development machines sit behind corporate proxies, managed certificate stores, outbound filtering, or remote development infrastructure. TLS or proxy failures can otherwise make a network-enabled sandbox behave unpredictably: a command that works in a normal shell may fail inside the sandbox, or remote package access may break during an otherwise straightforward task.

The release-note claim should not be stretched into a broad network-safety guarantee. A network-enabled sandbox can still reach destinations allowed by the local environment and policy, and network access can still leak information through request metadata, package names, URLs, error messages, or uploaded payloads if a command is poorly scoped. Fixing TLS access makes authorized network operations more reliable; it does not make all network operations appropriate.

Enterprise teams should document when Codex may use network access. Common approved cases might include fetching public package metadata, running a dependency installation in a disposable branch, or calling an internal development service explicitly approved for the project. Disallowed cases might include uploading source code to unapproved services, sending proprietary logs to public paste tools, calling production APIs without change approval, or using network access to bypass an internal review process.

Recommended network policy questions:

- Is network access needed for this Codex task, or can it be completed offline?
- Is the target host, registry, or service approved for this repository?
- Does the command send source code, logs, prompts, files, or identifiers over the network?
- Are proxy settings and TLS trust configured by approved enterprise policy?
- Does the transcript capture enough evidence to explain why network access was used?
- Is there a rollback or cleanup step if a package, cache, or generated artifact changes?

Security teams should add network-enabled Codex tasks to their ordinary monitoring model rather than treating them as a separate category outside policy. If a developer can run a network command manually, Codex may propose a similar command; the difference is that the agentic workflow can make the command feel like a step in a conversation rather than a deliberate shell action. Approval prompts, network constraints, and logs should preserve that deliberateness.

Preserved filesystem denials narrow approval scope when users approve commands

OpenAI’s 0.159 release notes say approved commands retain explicit filesystem denials. This is one of the most important safety-relevant changes in the release because approvals are often where users accidentally broaden scope. If a command is approved but a denial boundary silently disappears, the effective permission could become wider than the reviewer intended. The release note indicates that explicit denials are preserved when commands are approved.

That behavior should be understood as a boundary-preservation improvement, not a substitute for approval review. A human still needs to inspect what the command does, what paths it touches, whether the requested writable roots are appropriate, and whether the denied paths are complete. A preserved denial only helps if the denial was present, correctly scoped, and tested against the actual task.

This hardening guide explains local Codex trust boundaries, layered configuration, sandboxes, approvals, web search controls, and secret filtering. The Harden Codex Local Projects: Trust Boundaries, Layered Config, Sandboxes, Approvals, Web Search, and Secret Filtering article is a focused companion for Protected Filesystem Boundaries because protected filesystem boundaries are a local Codex hardening concern, and this target directly addresses trust boundaries and sandboxed local projects.

A strong local policy starts with a narrow writable root. For example, if Codex is helping edit documentation, the writable root should not include the entire home directory. If Codex is generating tests, the writable root can often be limited to the repository and perhaps a temporary build directory. If Codex is investigating a production incident, the team should be especially careful: incident logs, credentials, customer data, legal communications, and access-control files may sit near ordinary developer paths but require different handling.

Negative tests should be deliberately boring. Ask Codex to write a harmless file inside the approved root and confirm success. Then ask it to write a harmless file in a denied subtree and confirm failure. Then ask it to read or alter a protected path that policy forbids and confirm the denial appears in the transcript. Do not use real secrets or sensitive directories for demonstrations; use controlled dummy paths that exercise the same boundary pattern without exposing confidential material.

Default .aws protection under writable roots is useful, but secrets policy still belongs to the organization

OpenAI’s release notes state that .aws directories are protected by default under writable roots. This is a concrete protective default because .aws directories commonly contain cloud configuration or credential-related material on developer machines. Protecting that directory by default reduces the chance that an approved writable root accidentally exposes a sensitive cloud-configuration subtree.

The limitation is straightforward: .aws is not the only sensitive directory pattern, and default protection does not prove that secrets are absent elsewhere. Credentials and confidential configuration can appear in environment variables, shell history, dotfiles, package manager configuration, cloud-provider directories, deployment manifests, local caches, test fixtures, screenshots, copied transcripts, or application-specific config files. Teams should treat the .aws default as one guardrail inside a broader secrets-management policy.

Developers should not test this protection with real cloud credentials. A safe validation uses a dummy directory structure in a disposable environment, confirms that the policy denies access as expected, and records the result without exposing actual account identifiers or credential files. Security teams should discourage screenshots of real home-directory listings or credential-path contents when documenting the control.

Recommended dummy-path validation, not a request to expose real credentials:

- Create a disposable test workspace.
- Create a non-sensitive dummy folder that mirrors the protected-path pattern.
- Configure the writable root according to the rollout policy.
- Ask Codex to perform an allowed write inside the approved root.
- Ask Codex to perform a denied write to the protected-pattern folder.
- Confirm that the denied attempt fails and appears in the transcript.
- Delete the disposable workspace after recording the control result.

Cloud administrators should pair local filesystem protection with environment-variable filtering. Many cloud SDKs and internal tools can read credentials or configuration from environment variables instead of files. If a Codex task can inherit a broad environment, a protected .aws directory may not prevent accidental exposure of cloud-related values through command output or generated logs. OpenAI’s configuration documentation emphasizes preserving explicit permission, sandbox, network, and environment-variable boundaries; local administrators should convert that principle into a specific allowlist or denylist appropriate for their environment.

Trusted-project review remains the gate for project configuration

OpenAI’s configuration documentation says configuration precedence includes CLI overrides, trusted project .codex/config.toml, selected profiles, user config, cloud-managed defaults, system config, and built-in defaults, and that project config is skipped for untrusted projects. This is the central governance point for teams adopting 0.159: release-note behavior may improve the client, but local policy still decides whether project-level settings should be read and applied.

Trusted-project review should be treated like dependency review. A repository can contain useful Codex configuration that narrows permissions, selects a profile, or standardizes behavior; it can also contain configuration that is inappropriate for a user’s role or environment. Before marking a project as trusted or relying on its project configuration, teams should inspect the repository origin, maintainers, recent changes, configuration files, scripts, tool definitions, and expected side effects.

A practical trusted-project checklist should include at least five questions. First, is the repository owned by the organization or an explicitly approved partner? Second, does the project configuration request only the permissions necessary for the task? Third, are writable roots narrow and explicit? Fourth, are network settings and environment-variable rules consistent with policy? Fifth, is there a rollback path if Codex applies an unwanted change? If any answer is unclear, treat the project as untrusted until a qualified reviewer resolves the gap.

Because current behavior can vary by plan, account, app, region, rollout, and workspace policy, documentation should avoid promising a single universal Codex experience. Instead, maintain an environment-specific runbook that records the installed version, supported platforms, approved installation method, trust policy, profiles, writable roots, network policy, environment-variable filtering, logging expectations, and escalation contacts.

Explicit writable roots should be designed around tasks, not user convenience

The safest writable-root strategy is task-centered. If Codex is asked to update a README, the writable root should be the repository or documentation subtree required for that change. If Codex is asked to generate unit tests, the writable root can often be limited to test directories and supporting fixtures. If Codex is asked to perform a migration, the writable root may need to include source, tests, and migration files, but not unrelated home-directory folders or cloud-credential directories.

Convenience-based roots, such as an entire home directory or a broad workspace folder containing unrelated clients, experiments, and credentials, make review harder. They also make approvals harder to reason about because a generated command can touch paths that were not in the reviewer’s mental model. Codex 0.159’s preserved denials and default .aws protection help, but they do not eliminate the risk created by broad roots.

A useful policy is to define root templates by task class. Documentation tasks get a documentation root. Test-generation tasks get source and test roots. Build-debugging tasks get the repository and a temporary build cache. Release tasks get no automatic authority to publish, tag, upload, send announcements, change permissions, or deploy without separate human approval. Production tasks require the organization’s existing change-management process and should not be collapsed into a conversational approval.

Recommended decision rule: if the task would require a change ticket, peer review, incident commander approval, legal review, finance approval, or security signoff outside Codex, it still requires that approval when Codex suggests or assists with the action.

Environment-variable filtering is the hidden control that many teams under-specify

Filesystem protections are visible because users can point to directories. Environment-variable protections are easier to overlook because variables are inherited silently by child processes, build tools, scripts, package managers, test runners, and language-specific tooling. A command that never reads .aws may still expose sensitive values if the environment contains tokens, internal endpoints, customer identifiers, or deployment credentials and the command prints them during debugging.

OpenAI’s configuration documentation places environment-variable boundaries alongside permissions, sandbox, and network boundaries. A conservative Codex rollout should therefore define which variables may be inherited, which must be stripped, and which require a special profile. The policy should cover both obvious secrets and operationally sensitive values such as internal service URLs, tenant IDs, feature flags, incident identifiers, and private registry tokens.

For developer usability, environment filtering should be predictable. If every task fails because required build variables are absent, users will pressure administrators to broaden the environment. A better approach is to define minimal profiles: a read-only analysis profile with very few variables, a local

Controlled upgrade workflow for Codex CLI 0.159

OpenAI’s Codex CLI documentation gives one official standalone install and update command for macOS and Linux: curl -fsSL https://chatgpt.com/codex/install.sh | sh. A controlled upgrade should use that command exactly, then prove the resulting binary version, configuration chain, project trust boundary, sandbox behavior, transcript behavior, and cross-platform launch behavior before the upgrade reaches daily engineering work. Treat this as a release-validation workflow, not as a productivity ceremony, because Codex 0.159 changes input timing, transcript handling, Mermaid rendering, filesystem-denial preservation, app-server pagination, and several platform-specific execution paths.

The safest upgrade sequence is a canary rollout against representative repositories, with no secrets in prompts, commands, screenshots, logs, fixtures, or copied transcripts. Use placeholder repositories, placeholder issue descriptions, placeholder file names, and placeholder environment-variable names. If a team needs to test secret-handling controls, the fixture should contain strings such as PLACEHOLDER_API_TOKEN_DO_NOT_USE and PLACEHOLDER_ACCOUNT_ID, never real credentials, cloud account numbers, private keys, session cookies, customer data, health data, payment data, or privileged legal material.

Recommended canary matrix

The canary should include at least one small repository, one medium repository with generated files, one repository with documentation diagrams, one repository with a local app server, and one repository with deliberately constrained filesystem permissions. The point is not to benchmark Codex; it is to detect regressions in the paths most affected by 0.159: interruption, native Mermaid rendering, transcript selection copying, warnings review, app-server pagination, and sandbox denial preservation.

Step 1: create a Git checkpoint before touching the CLI

Begin inside each canary repository with a clean working tree. If the repository already contains local work, stop and either commit, stash, or move the work according to the team’s normal change-management process. Codex documentation encourages keeping Git checkpoints and reviewing diffs; for a release canary, those checkpoints are the rollback boundary for any accidental file edits, generated artifacts, or configuration experiments.

# Placeholder-only preflight. Do not paste secrets into shell history.
git status --short
git branch --show-current
git log --oneline -n 3

# Optional checkpoint branch for canary validation.
git switch -c codex-0159-canary-placeholder

# Optional empty marker commit if your team uses auditable checkpoints.
git commit --allow-empty -m "chore: checkpoint before Codex CLI 0.159 canary"

If the repository cannot be checkpointed because it contains uncommitted confidential material, do not use it as a canary. Create a sanitized fixture repository that preserves the directory structure and file types without containing customer records, credentials, unreleased financial information, privileged communications, personal data, or proprietary source that is unnecessary for validating the CLI behavior.

Step 2: install or update using the official standalone command only

For macOS and Linux, use OpenAI’s official standalone install/update command and record the date, platform, shell, and target user account in an internal upgrade note. Do not replace this with an invented package-manager command, a third-party mirror, an unofficial binary, or a copied script from an old runbook. The official command is the only install/update command in scope for this article.

# macOS/Linux official standalone install or update command from OpenAI documentation.
# Review your organization's policy before running remote installation scripts.
curl -fsSL https://chatgpt.com/codex/install.sh | sh

Enterprise administrators should apply their normal controls before allowing any remote installer script on managed devices. That may include reviewing OpenAI’s current documentation, running the command in a disposable canary account, routing through approved network controls, and documenting the endpoint-management exception or approval. Do not embed access tokens, proxy passwords, device-management secrets, or private certificate material in the command, terminal transcript, or screenshot.

Step 3: confirm the installed version and record the result

After installation, confirm the installed Codex CLI version before running any task. The release page identifies the stable release as rust-v0.159.0, published September 29, 2026. The exact version command and output format can vary by CLI behavior, shell, and distribution path, so the operational requirement is to capture the version evidence that your installed CLI exposes and compare it with the release you intend to validate.

# Use the CLI's available version output on your machine.
# Keep output sanitized and do not include private user paths in shared screenshots.
codex --version

# Record:
# - platform: PLACEHOLDER_OS_AND_VERSION
# - shell: PLACEHOLDER_SHELL
# - Codex version output: PLACEHOLDER_CODEX_VERSION_OUTPUT
# - validation date: PLACEHOLDER_DATE
# - canary repository: PLACEHOLDER_REPOSITORY_NAME

If the version output does not identify the expected release, pause the canary. Do not proceed by assuming the update succeeded. Check the shell path, user-local binary location, endpoint-management policy, and whether the previous binary is still first in PATH. Keep the evidence bland: paths may reveal usernames, client names, or internal project names, so redact those before attaching screenshots to a shared ticket.

Step 4: inspect configuration precedence before testing behavior

OpenAI’s configuration documentation states that Codex configuration precedence runs from CLI overrides, to trusted project .codex/config.toml, to selected profiles, to user config, to cloud-managed defaults, to system config, and then built-in defaults. It also states that project config is skipped for untrusted projects. A controlled upgrade should therefore inspect where the effective behavior is coming from before attributing any change to Codex 0.159 itself.

# Placeholder config inventory. Do not print real secrets or sensitive env values.
# Capture file presence and relevant non-secret keys only.
ls -la .codex 2>/dev/null || true
git status --short .codex 2>/dev/null || true

# If showing config excerpts, redact values and preserve only structural evidence:
# [sandbox]
# writable_roots = ["PLACEHOLDER_RELATIVE_PATH"]
# denied_paths = ["PLACEHOLDER_DENIED_PATH"]
#
# [tui]
# instant_interrupt = PLACEHOLDER_BOOLEAN

The 0.159 release removed automatic follow-up prompt suggestions and the tui.prompt_suggestions setting, according to OpenAI’s release notes. During configuration inspection, remove stale reliance on that setting from canary expectations. Do not infer that a missing suggestion means the model is malfunctioning; in this release, the release notes say the prompt-suggestion feature and setting were removed.

Step 5: establish permission and sandbox baselines

Before testing new task behavior, record the intended permission baseline. The release notes say approved commands retain explicit filesystem denials, and .aws directories are protected by default under writable roots. Those are important safety improvements, but they are not a substitute for reviewing approval scope, environment-variable forwarding, writable roots, network access, or workspace policy. A canary should prove that the local baseline remains conservative after the upgrade.

# Placeholder-only sandbox baseline note.
# Do not include real private paths, usernames, project secrets, or cloud identifiers.

CANARY_REPOSITORY=PLACEHOLDER_REPOSITORY
WRITABLE_ROOTS=PLACEHOLDER_RELATIVE_ALLOWED_DIRECTORY_LIST
EXPLICIT_DENIED_PATHS=PLACEHOLDER_RELATIVE_DENIED_DIRECTORY_LIST
NETWORK_BASELINE=PLACEHOLDER_DISABLED_OR_APPROVED_SCOPE
ENV_FORWARDING_BASELINE=PLACEHOLDER_ALLOWED_NAMES_ONLY_NO_VALUES
APPROVAL_MODE=PLACEHOLDER_APPROVAL_BASELINE

A negative sandbox test should attempt only harmless writes to placeholder paths, never destructive operations. For example, ask Codex to create PLACEHOLDER_ALLOWED_DIR/codex-canary.txt and then ask it to create PLACEHOLDER_DENIED_DIR/codex-canary.txt. The expected result is that the allowed path follows the configured approval flow and the denied path remains denied even if a command is otherwise approved. If the denied write succeeds, stop the rollout and investigate configuration precedence, project trust, and workspace policy before continuing.

Sample prompt for a negative sandbox test:
"Use only placeholder files. Create a one-line canary file at PLACEHOLDER_ALLOWED_DIR/codex-canary.txt containing the text 'codex 0.159 canary'. Then explain, without attempting destructive work, whether PLACEHOLDER_DENIED_DIR/codex-canary.txt is writable under the current sandbox policy. Do not access secrets, credentials, hidden cloud directories, or files outside the repository fixture."

Do not ask Codex to inspect real .aws contents, cloud credentials, private SSH directories, package-registry tokens, password managers, browser profiles, production configuration, or organization secrets. If a test needs to verify that .aws is protected under a writable root, create a sanitized directory named for the behavior with placeholder text only, and avoid commands that enumerate or print credential-like material.

Step 6: test opt-in instant_interrupt without treating it as a kill switch

OpenAI’s release notes describe instant_interrupt as opt-in and say it lets new input steer Codex during model responses or long-running code-mode calls. The canary should verify whether the setting is disabled or enabled in the specific test profile, then test steering with a harmless long-running fixture. Do not describe this as guaranteed interruption, safe cancellation, transaction rollback, or a replacement for human approval.

# Placeholder config excerpt only. Confirm against your actual approved config path and policy.
# Do not publish full local config if it contains sensitive workspace details.

[tui]
instant_interrupt = true

A safe interrupt fixture is a documentation or test-generation task that can be redirected without damaging the repository. Avoid prompts that start migrations, delete files, publish packages, rotate keys, modify permissions, send messages, or contact external systems. The purpose is to prove user steering during an in-progress response or long-running code-mode call, not to test how abruptly Codex can stop a dangerous operation.

Sample prompt for an interrupt canary:
"Using only placeholder content, draft a short test plan for PLACEHOLDER_MODULE. Do not edit files yet. Include ten numbered checks."

Interrupt input while the response is still being produced:
"Steer the plan toward Mermaid rendering and transcript-copy checks instead. Keep it as a draft and do not run commands."

The pass condition is observable steering: the active response or task should reflect the new instruction in a way that is visible in the transcript. If the task continues with the original plan, record that outcome rather than forcing success. Current behavior can vary by plan, account, app, region, rollout, workspace policy, local configuration, and task mode, so evidence matters more than assumptions.

Step 7: run Mermaid fixtures and validate the diagram source separately

OpenAI’s release notes say native Mermaid rendering now supports more flowchart edges, labels, and node groups. A canary should include diagrams that exercise those features, but the review must separate rendering from correctness. A rendered diagram can still describe the wrong dependency, security boundary, data-flow direction, or approval path.

flowchart LR
  subgraph PLACEHOLDER_GROUP_A[Placeholder group A]
    A1[Placeholder input] -- labeled edge --> A2{Placeholder decision}
  end

  subgraph PLACEHOLDER_GROUP_B[Placeholder group B]
    B1[Placeholder process] -. dotted label .-> B2[Placeholder output]
  end

  A2 -- approved placeholder path --> B1
  A2 -- denied placeholder path --x C1[Placeholder blocked action]

The Mermaid canary should include source review, rendered review, and semantic review. Source review verifies that the Markdown was not rewritten unexpectedly. Rendered review verifies that labels, node groups, and edge styles are legible. Semantic review verifies that the architecture claim is true according to the repository’s actual design documents, code owners, and security boundaries. Do not use native rendering as evidence that the architecture is valid.

Step 8: test transcript selection copying with Markdown tables and whitespace

OpenAI’s release notes say copying transcript selections preserves Markdown tables, formatting, and significant whitespace. This matters for audit packets, handoffs, bug reports, and compliance review because broken tables can change the meaning of evidence. The canary should copy a transcript segment that includes a table, an indented code block, a bullet list, and a warning note, then paste it into a plain-text evidence file for comparison.

Sample transcript-copy fixture to ask Codex to produce without secrets:

"Create a placeholder evidence summary in the transcript only. Include:
1. A Markdown table with columns Check, Expected, Observed, Status.
2. A four-line indented code block containing PLACEHOLDER values only.
3. A bullet list of warnings using no real paths, secrets, or customer names.
Do not edit repository files."

After copying the selection, compare the pasted result with the visible transcript. The table pipes, indentation, and significant whitespace should remain usable. If the pasted evidence loses formatting, document the exact platform, terminal, app, clipboard manager, and destination editor. Do not include proprietary transcript content in the defect report; reproduce the behavior with the placeholder fixture.

This article describes how to prepare a governed safety-case evidence room containing claims, records, evaluation artifacts, incident evidence, access controls, and remediation history for independent assessment. The Prepare a Safety-Case Evidence Room for Independent AI Assessment: Claims, Access, Methods, Confidentiality, and Remediation article is a focused companion for Transcript Evidence Preservation because the marker concerns preserving transcript evidence, and this article provides the closest governance context for retaining and organizing evidence records.

Step 9: verify warning review, dismissal, and retention behavior

The 0.159 release notes say the warnings viewer dismisses reviewed warnings unless retained. This is a workflow improvement, but it can also create evidence gaps if a team assumes every warning remains visible forever. The canary should deliberately create benign warnings, review them, retain one if the interface supports doing so in the current environment, and confirm which warnings remain visible after task switching or session restart.

Security teams should define which warnings must be retained as evidence. A practical rule is to retain warnings tied to sandbox denials, network access, environment-variable forwarding, write attempts outside approved roots, approval prompts, and project-trust decisions. Routine informational warnings can be summarized in the canary note if they are not needed for later review.

Step 10: test blank-session draft recovery and task switching

OpenAI’s release notes say blank sessions retain drafts during task switching. This reduces accidental loss when a user prepares a prompt, changes context, and returns before the first turn. The test should use a placeholder draft that contains no secrets and no private facts, then switch away and return to confirm whether the draft remains available in that environment.

Placeholder draft for recovery test:
"Draft only. Do not submit yet. Review PLACEHOLDER_MODULE for documentation gaps using PLACEHOLDER_REQUIREMENTS. Do not access credentials, external services, private user data, or files outside the canary repository."

Draft retention is not a substitute for prompt hygiene. If a user starts typing a privileged legal strategy, unreleased earnings detail, incident-response secret, or personal record into a draft, the safer fix is not better recovery; it is not putting that material into the prompt at all unless the organization has explicitly approved the workflow, data classification, and retention implications.

Step 11: validate app-server pagination from a specific item

The release notes say app-server clients can paginate thread history from a specific item. Teams that integrate Codex with app-server workflows should test this with a synthetic thread fixture, not with production user conversations. The fixture should contain placeholder thread IDs, deterministic item labels, and enough entries to require pagination.

{
  "thread_id": "PLACEHOLDER_THREAD_ID",
  "items": [
    { "id": "PLACEHOLDER_ITEM_001", "role": "user", "text": "Placeholder first request" },
    { "id": "PLACEHOLDER_ITEM_002", "role": "assistant", "text": "Placeholder first response" },
    { "id": "PLACEHOLDER_ITEM_003", "role": "user", "text": "Placeholder follow-up" },
    { "id": "PLACEHOLDER_ITEM_004", "role": "assistant", "text": "Placeholder follow-up response" }
  ],
  "pagination_test_start_item": "PLACEHOLDER_ITEM_003"
}

The validation question is simple: when pagination starts from PLACEHOLDER_ITEM_003, does the client return the expected subsequent placeholder records without duplication, omission, or ordering confusion? If a product depends on transcript completeness for audit or support review, compare the paginated reconstruction with the original fixture. Keep real user messages, customer identifiers, internal incident details, and privileged conversations out of the test.

Step 12: cover Windows, macOS, and Linux cases separately

The 0.159 release has platform-specific implications. OpenAI’s release notes describe Windows launch fixes that suppress stray console windows for MCP servers, code-mode hosts, and piped commands, with restrictive launchers potentially falling back to embedded mode. The notes also describe macOS TLS access fixes for network-enabled sandboxes and proxy-dependent remote environments. Linux should still be included because it is common for development workstations, CI-like terminals, and remote shells, even when the highlighted fixes focus on Windows and macOS.

Do not use platform canaries to test production infrastructure access. For macOS network-enabled sandbox tests, use a non-sensitive placeholder endpoint approved by administrators. For Windows launcher tests, do not weaken endpoint security to make a test pass; record the fallback path and decide whether that operating mode is acceptable under your organization’s policy.

Step 13: run a removal-regression check for prompt suggestions and bundled skills

OpenAI’s 0.159 release notes say automatic follow-up prompt suggestions and the tui.prompt_suggestions setting were removed, and the bundled plugin-creator skill was removed. A canary should confirm that local onboarding material, internal screenshots, and developer training no longer instruct users to rely on those features. This is especially important for teams that wrote runbooks around automatic next-step suggestions.

This removal check prevents two common upgrade failures: users waiting for UI behavior that is no longer present, and administrators diagnosing expected removal as a local misconfiguration. If a team still needs suggested next steps, the safer replacement is an explicit checklist in the repository’s contributor guide, reviewed by maintainers and security owners.

Step 14: review diffs, archive evidence, and decide whether to promote

After every canary run, inspect the Git diff before accepting the upgrade. The diff should show only expected placeholder files or no file changes at all. If Codex edited generated files, configuration, documentation, lockfiles, or hidden directories unexpectedly, do not hand-wave the change as harmless. Revert the repository to the checkpoint and capture the transcript segment that explains what happened.

# Post-canary inspection.
git status --short
git diff --stat
git diff -- . ':!PLACEHOLDER_ALLOWED_CANARY_FILE'

# If reverting placeholder test files:
git restore PLACEHOLDER_ALLOWED_DIR/codex-canary.txt

# If abandoning the canary branch:
git switch PLACEHOLDER_ORIGINAL_BRANCH
git branch -D codex-0159-canary-placeholder

The promotion decision should be written as an operational record with three possible outcomes: promote to the next canary ring, hold for investigation, or block the upgrade. Promote only when version evidence, configuration precedence, sandbox negative tests, interrupt behavior, Mermaid fixtures, transcript-copy fidelity, warning retention, app-server pagination, and platform-specific checks have all been reviewed by the accountable owner. Holding is appropriate when behavior is ambiguous. Blocking is appropriate when denied writes succeed, secrets appear in logs, launch behavior bypasses policy, or evidence cannot be reproduced.

Suggested evidence packet for security and platform teams

A useful evidence packet is concise, reproducible, and sanitized. It should prove what was tested without exposing credentials, repository secrets, private source, customer records, personal data, privileged material, or internal infrastructure details that are not necessary for review. Use placeholders in shared artifacts, and store the full packet only in an approved internal system with normal access controls.

• Release identifier: rust-v0.159.0, matching OpenAI’s September
  

  What the removed prompt suggestions and bundled skill change in practice

  OpenAI’s Codex CLI 0.159 release notes state that automatic follow-up prompt suggestions were removed, along with the tui.prompt_suggestions setting and the bundled plugin-creator skill. That is a narrow removal set, not a statement that Codex removed every skill, every plugin-related workflow, or every extension mechanism. Teams should treat the change as a user-interface and bundled-content cleanup until they verify the exact behavior in their own installed version, configuration stack, and workspace policy.

  The removal of automatic follow-up prompt suggestions matters because suggested continuations can subtly shape developer behavior. In prior workflows, users may have relied on visible suggestions to decide whether to ask Codex for a refactor, a test, a commit summary, or a next investigation step. With those suggestions removed, teams that used them as informal onboarding aids should replace them with explicit internal runbooks, prompt templates, or review checklists that are maintained by the organization rather than inferred from the terminal interface.

  The removed tui.prompt_suggestions setting should also be handled as a configuration cleanup item. If a user, dotfiles repository, machine image, or project-level .codex/config.toml still contains that setting, administrators should not assume it continues to do anything useful. OpenAI’s configuration documentation describes a precedence order that includes CLI overrides, trusted project configuration, selected profiles, user configuration, cloud-managed defaults, system configuration, and built-in defaults. A stale setting can therefore create confusion even when it is ignored, because reviewers may think they are controlling a behavior that the current release no longer exposes.

  The practical remediation is to inventory configuration files, remove obsolete references where appropriate, and record the removal in the upgrade change log. Do not delete project configuration blindly: project config is skipped for untrusted projects according to OpenAI’s documentation, and trusted-project review remains a separate safety step. A safe cleanup uses version control, a minimal diff, and a reviewer who can distinguish obsolete UI preferences from active sandbox, approval, network, filesystem, and environment-variable controls.

  # Recommended cleanup example: search for removed suggestion setting.
  # Run only in repositories and dotfile directories you are authorized to inspect.
  
  grep -R "tui.prompt_suggestions" ~/.codex ./ 2>/dev/null
  
  # If found, remove through a reviewed change rather than a blind script.
  # Preserve sandbox, approval, network, environment, and filesystem settings.

  The bundled plugin-creator skill removal should be interpreted just as carefully. OpenAI’s release note says the bundled plugin-creator skill was removed; it does not say that all skills, all plugin authoring, or all integration work disappeared from every Codex environment. If a team had onboarding documentation that told developers to invoke that bundled skill, the documentation should now be updated to say that the bundled skill is no longer present in Codex CLI 0.159 and that any replacement workflow must be explicitly approved, sourced, and reviewed.

  Security teams should pay particular attention to old instructions that encouraged users to generate plugins, tools, or connectors quickly without a design review. Even when a convenience skill is no longer bundled, a user may still ask the model to draft integration code. That output can introduce permission requests, network calls, credential-handling paths, data retention behavior, or publication steps. Human approval remains mandatory before external submissions, package publication, permission changes, production deployment, or connecting to third-party systems.

  Removed item in Codex CLI 0.159	What OpenAI’s release note supports	What teams should not infer	Recommended operational response
  Automatic follow-up prompt suggestions	Automatic suggestions are no longer part of the release behavior described by OpenAI.	Do not infer that Codex no longer supports follow-up prompts or iterative conversations.	Replace implicit UI nudges with team-owned prompt templates, review checklists, and onboarding examples.
  tui.prompt_suggestions	The setting was removed with the suggestion feature.	Do not assume stale configuration entries still control current behavior.	Search trusted configuration files, remove obsolete entries through reviewed diffs, and document the cleanup.
  Bundled plugin-creator skill	The bundled skill is no longer included in this release.	Do not claim that all skills, plugin workflows, or integration development were removed.	Update onboarding material, require integration design review, and verify any replacement workflow in the target environment.

  Rollout vocabulary: canary, discrepancy triage, regression ledger, stop conditions, fallback, rollback, and sampling

  A canary rollout is a deliberately small first deployment used to detect problems before a wider promotion. For Codex CLI 0.159, a canary should include a representative but limited group: one or more developers who use code-mode workflows, one person who frequently reviews terminal transcripts, one platform or security reviewer, and at least one machine that exercises the organization’s most sensitive operating-system path, such as Windows launch behavior, macOS proxy/TLS behavior, or Linux sandbox policy. The canary should be time-boxed and evidence-driven rather than based on whether the release “feels fine.”

  Discrepancy triage is the process for deciding what to do when observed behavior differs from the release note, documentation, or internal expectation. A discrepancy is not automatically a product defect; it may be caused by rollout timing, account eligibility, workspace policy, local configuration precedence, a trusted-project boundary, a stale shell path, a restrictive launcher, or an undocumented internal assumption. Triage should identify the environment, the exact command path, the configuration source in effect, the project trust state, the operating system, and the reproducible steps before assigning blame.

  A regression ledger is the running record of tested behaviors, expected outcomes, observed outcomes, evidence links, owners, severity, and decisions. It is more useful than a chat thread because it supports promotion decisions, later audits, rollback justification, and release-to-release comparison. The ledger should include successful checks as well as failures; a passed test for preserved filesystem denials or blank-draft recovery can be important evidence when a later incident review asks what was validated before rollout.

  Stop conditions are pre-agreed findings that pause or block wider deployment. For Codex CLI 0.159, reasonable stop conditions include an approval path that appears broader than intended, a sandbox configuration discrepancy that cannot be explained, inability to preserve explicit filesystem denials in a tested workflow, unexpected access to protected credential directories, transcript-copy evidence that loses material context needed for review, a platform-specific launch failure that affects a critical team, or a stale configuration entry that masks the real active policy. Stop conditions should be written before the canary begins so the team does not rationalize a risky finding after the fact.

  Fallback is the temporary operating mode used when the new release is not promoted but the team still needs to keep work moving. A fallback can be a documented manual workflow, a reduced-permission Codex profile, a non-networked sandbox posture, a narrower writable root, a temporary pause on code-mode calls for sensitive repositories, or a requirement to use standard shell commands with human review rather than automated agent execution. Fallback is not the same as pretending the upgrade succeeded; it is a controlled continuity plan.

  Rollback is the deliberate return to a previously approved state after a release cannot be safely promoted or must be withdrawn. Rollback planning should include how to restore the prior Codex binary or package state according to the organization’s approved installation method, how to restore configuration files from version control or device management, how to communicate the temporary state to users, and how to preserve evidence from the failed rollout. Do not delete failure evidence merely to make machines clean; keep enough logs, transcripts, configuration snapshots, and decisions to support later root-cause analysis.

  This playbook compares Codex stable and 0.154 alpha release channels with guidance on canary repositories, permissions, checkpoints, and rollback. The Codex Stable vs 0.154 Alpha Playbook: Release Channels, Canary Repositories, Permissions, Checkpoints, and Rollback article is a focused companion for Codex Release Rollback because the marker is explicitly about Codex release rollback, and this target directly covers Codex release-channel evaluation and rollback planning.

  Documentation updates are part of the rollout, not an afterthought. Any internal guide that mentions automatic follow-up suggestions, tui.prompt_suggestions, or the bundled plugin-creator skill should be corrected. Guides should also state that instant_interrupt is opt-in, that Mermaid rendering improves display rather than architecture validity, and that default .aws protection under writable roots does not replace secret-management policy or review of environment-variable forwarding.

  Ongoing sampling is the post-promotion practice of checking a small set of real workflows after the initial rollout window. Sampling catches configuration drift, local overrides, inconsistent machine images, and behavior that appears only after developers resume normal work. A useful sampling plan reviews a few transcripts, a few denied filesystem paths, one or two Mermaid diagrams, at least one blank-draft recovery case, and platform-specific launch behavior where relevant. The goal is not to surveil developers; it is to verify that the approved control assumptions still match reality.

  Recommended canary-to-promotion workflow for Codex CLI 0.159

  The following workflow is a recommendation for organizations that treat Codex as part of a controlled development environment. It is not an OpenAI-mandated deployment process, and it should be adapted to the organization’s change-management, endpoint-management, and legal obligations. The important principle is to make release adoption observable: every promotion decision should be tied to tested behavior, documented exceptions, and named owners.

  1. Define the canary scope. Select a small group that covers core Codex usage patterns, operating systems, trusted-project configuration, and security review needs. Exclude highly sensitive repositories from the first pass unless the security team has approved a reduced-permission test plan.
  2. Record the starting state. Capture current Codex version, installation path, relevant user and project configuration, selected profile, project trust state, sandbox posture, network posture, and known local launcher constraints.
  3. Create Git checkpoints. Before running agentic code changes, ensure each test repository has a clean checkpoint or a deliberately documented dirty state. OpenAI’s Codex guidance emphasizes keeping Git checkpoints and reviewing diffs.
  4. Update through the approved path. Where the standalone installer is appropriate, OpenAI documents curl -fsSL https://chatgpt.com/codex/install.sh | sh for macOS and Linux. Organizations with managed devices should follow their own approved software-distribution process rather than asking users to bypass it.
  5. Confirm the running version. Validate that the shell, terminal app, automation host, and any editor-integrated launcher are invoking the intended Codex binary rather than a stale path.
  6. Test removed-feature expectations. Verify that automatic prompt suggestions and tui.prompt_suggestions no longer appear in the active workflow, and confirm that documentation no longer depends on the bundled plugin-creator skill.
  7. Exercise opt-in interruption. If testing instant_interrupt, confirm that it is intentionally enabled through the relevant configuration path and that users understand it is a steering feature, not a guaranteed safety stop.
  8. Run negative permission tests. Attempt safe, non-destructive access checks against paths that should remain denied, including explicit filesystem denials and protected credential directories where appropriate. Do not use real secrets as test material.
  9. Validate transcript evidence. Copy selected transcript sections that include Markdown tables, formatting, and significant whitespace, then paste into the organization’s review medium to confirm the evidence remains understandable.
  10. Check diagram workflows. Render known Mermaid fixtures and separately validate the underlying architecture claims with the responsible engineer or architect.
  11. Document discrepancies. Enter every unexpected result into the regression ledger with environment details, reproduction steps, evidence, severity, owner, and disposition.
  12. Apply stop conditions. Pause promotion if the canary hits a pre-agreed blocker. Avoid expanding the rollout while a control discrepancy is still unexplained.
  13. Promote, fallback, or rollback. Decide based on evidence. Promotion should include updated documentation; fallback should define temporary constraints; rollback should preserve investigation material.
  14. Schedule ongoing sampling. Recheck a small number of real workflows after users have worked with the release long enough for drift and edge cases to surface.

  Regression ledger template for platform and security teams

  A regression ledger should be simple enough that developers actually use it and structured enough that security, platform, and engineering leaders can make decisions from it. The table below is a recommended template. Store it in the organization’s normal change-management system, issue tracker, or release repository rather than in an ephemeral chat. Avoid pasting secrets, tokens, private customer data, privileged legal material, or unnecessary personal information into the ledger.

  Field	What to record	Decision value
  Test ID	A stable identifier such as codex-0159-permission-aws-01.	Makes later discussions precise and supports retesting.
  Expected behavior	The behavior expected from OpenAI’s release note, official documentation, or internal policy.	Separates documented expectations from informal assumptions.
  Observed behavior	What happened, including exact platform, project trust state, active profile, and command context.	Helps identify rollout variation, local configuration, or true regression.
  Evidence pointer	Reference to a sanitized transcript, screenshot, commit diff, terminal log, or configuration snapshot.	Allows reviewers to verify claims without rerunning every test.
  Severity	Classify as blocker, high, medium, low, or informational using pre-agreed criteria.	Prevents subjective promotion decisions.
  Owner	The person responsible for triage and final disposition.	Prevents unresolved discrepancies from disappearing.
  Disposition	Promote, document exception, fix configuration, fallback, rollback, or escalate.	Turns test findings into release decisions.

  {
    "test_id": "codex-0159-removed-suggestions-01",
    "release": "rust-v0.159.0",
    "platform": "record actual OS and terminal host",
    "project_trust_state": "trusted or untrusted",
    "expected": "Automatic follow-up prompt suggestions are not present; tui.prompt_suggestions is removed.",
    "observed": "Record the actual observed behavior without secrets.",
    "evidence": "Pointer to sanitized transcript or issue attachment",
    "severity": "informational | low | medium | high | blocker",
    "owner": "named internal owner",
    "decision": "promote | fallback | rollback | document exception | retest"
  }

  Stop conditions that should block wider deployment

  Stop conditions should be tied to controls that matter, not cosmetic preferences. A compact welcome screen, consistent headers, and warning-dismissal behavior can improve usability, but they should not distract from permission boundaries, sandbox posture, network behavior, filesystem denials, evidence quality, and user understanding. If a finding creates uncertainty about what Codex can read, write, execute, transmit, or preserve, it deserves higher priority than a visual difference in the terminal.

  • Permission boundary uncertainty: Block promotion if an approval appears to authorize broader filesystem access than the reviewer intended, or if explicit filesystem denials are not preserved in a tested approval flow.
  • Protected-directory concern: Block promotion if a writable-root test suggests that protected credential directories such as .aws are accessible contrary to the expected default protection, unless the team can prove the test configuration intentionally changed that boundary.
  • Network posture mismatch: Block promotion if a sandbox expected to be offline can reach network resources, or if a network-enabled sandbox behaves differently from the documented workspace policy.
  • Environment-variable exposure concern: Block promotion if sensitive environment variables appear in prompts, transcripts, subprocesses, logs, or model-visible context without explicit approval and documented necessity.
  • Evidence-loss issue: Block promotion if copied transcripts omit material formatting, table structure, command context, or warnings needed for audit and code review decisions.
  • Unsafe integration path: Block promotion if users continue following old plugin-creator instructions that bypass design review, permission review, or publication approval.
  • Unexplained platform discrepancy: Block promotion if Windows, macOS, Linux, proxy, TLS, launcher, or embedded-mode behavior differs in a way that affects critical teams and cannot be explained.
  • Stale configuration confusion: Block promotion if reviewers cannot determine which configuration source is active because user, project, profile, system, and cloud-managed settings conflict.

  Fallback and rollback patterns that preserve evidence

  A fallback pattern should reduce risk while keeping developers productive. For example, a team can temporarily disable network-enabled workflows for sensitive repositories, restrict writable roots to a disposable branch or fixture repository, require manual execution of commands proposed by Codex, or limit Codex usage to planning and diff explanation until permission discrepancies are resolved. The fallback should have an expiration date or review trigger; otherwise, temporary constraints become undocumented permanent practice.

  A rollback pattern should restore a known prior state and preserve enough evidence to understand why the rollback happened. If the organization manages Codex through endpoint tooling, rollback should occur through that tooling rather than ad hoc local fixes. If users installed the standalone CLI directly, the team should still coordinate the rollback method, record the version restored, and confirm that editor integrations, shells, automation hosts, and terminals no longer invoke the withdrawn version.

  Rollback communication should be specific. A useful notice states which release is paused, which users are affected, which workflows should stop or switch to fallback, what evidence should be preserved, who owns triage, and when the next update will arrive. Avoid vague warnings such as “Codex is broken,” because they encourage users to invent workarounds. Also avoid overconfident reassurance such as “no secrets were at risk” unless the organization has actually completed the review needed to support that statement.

  Recommended release notice language: “We are pausing wider adoption of Codex CLI 0.159 while we investigate a discrepancy in our tested sandbox configuration. Continue using the approved fallback profile for authorized repositories only. Do not paste credentials or private customer data into prompts or transcripts. Preserve relevant sanitized transcripts and diffs in the release ledger. The platform team will provide the next update after triage.”

  Documentation changes users should see after the upgrade

  Developer-facing documentation should be updated in the same pull request or change ticket that promotes the release. The update should remove references to automatic prompt suggestions as an onboarding aid, delete or annotate tui.prompt_suggestions, and replace bundled plugin-creator instructions with a review-based integration workflow. A short “what changed for users” section is usually more effective than asking every developer to read raw release notes.

  Security-facing documentation should emphasize that Codex CLI 0.159 improves some safety-relevant mechanics without eliminating review obligations. Preserved filesystem denials and default .aws protection under writable roots are useful boundaries, but they do not prove that every secret location, environment variable, network route, or approval prompt is safe. Documentation should tell users how to report unexpected access, how to sanitize evidence, and when to stop work pending review.

  Architecture documentation should explain the Mermaid update carefully. Native rendering support for more flowchart edges, labels, and node groups can make diagrams easier to review in the terminal, but a rendered diagram is not validation that dependencies, trust zones, threat boundaries, data flows, or failure modes are correct. A diagram generated or modified with Codex should still be reviewed by someone who owns the system design.

  Training material for instant_interrupt should state that it is opt-in and that new input can steer Codex during model responses or long-running code-mode calls. Users should understand that steering can be operationally useful when they notice a wrong direction, but it is not a substitute for permissions, sandboxing, checkpoints, diff review, or stopping a consequential action before it reaches an external system. If a command could publish, purchase, deploy, delete, submit, alter permissions, or create a legal commitment, human approval is still required.

  Ongoing sampling after promotion

  After promotion, sample real usage rather than only synthetic fixtures. A post-promotion sample might inspect a sanitized transcript from a planning session, a code-mode run in a low-risk repository, a Mermaid rendering workflow, a warning-review interaction, and a blank-session draft recovery case. The reviewer should confirm that the evidence matches the promoted assumptions: users understand removed suggestions, obsolete settings are not relied on, protected paths remain protected, and copied transcripts retain enough context for review.

  Sampling should also look for drift in local configuration. A developer may have a stale user config, a project may become trusted after the canary, a profile may override a system default, or a managed image may lag behind the documented version. OpenAI’s configuration precedence model makes this especially important: the effective behavior is the result of multiple layers, not just a single file. A sampling checklist should therefore ask, “Which layer supplied this behavior?” before concluding that the release itself changed.

  Security and platform teams should keep sampling proportionate. The aim is to validate controls and spot unsafe patterns, not to collect unnecessary personal information or confidential business content. Use sanitized excerpts, minimal reproduction repositories, and redacted evidence where possible. If a sample reveals possible exposure of credentials, private data, or privileged material, stop routine sampling and switch to the organization’s incident-handling process.

  Bottom line for Codex CLI 0.159 adoption

  Codex CLI 0.159 is best understood as a workflow-control release with several developer-experience improvements and several safety-relevant boundary changes. The opt-in instant_interrupt feature can help steer an in-progress response or long-running code-mode call, but it should not be treated as a guaranteed kill switch. Mermaid rendering can make terminal diagrams more useful, but it does not validate architecture. Preserved filesystem denials and default .aws protection under writable roots are meaningful safeguards, but they do not eliminate the need to review approvals, writable roots, network posture, environment-variable forwarding, or secret-handling policy.

  The removals are equally important to operationalize. Automatic follow-up prompt suggestions, tui.prompt_suggestions, and the bundled plugin-creator skill should disappear from current runbooks, tests, and onboarding material. Teams should not overstate that change: OpenAI’s release note supports those specific removals, not a broad claim that every skill, plugin-related workflow, or integration pattern has been removed. The safe response is configuration cleanup, documentation cleanup, and replacement of convenience-driven workflows with reviewed internal procedures.

  For administrators, the decision rule is straightforward: promote the release only after a canary validates the controls your organization depends on. Keep Git checkpoints, confirm the active version, inspect configuration precedence, run negative permission tests, validate transcript evidence, test platform-specific behavior, and maintain a regression ledger. If a discrepancy affects permission boundaries, network behavior, filesystem access, evidence integrity, or integration review, use fallback or rollback rather than expanding the rollout on hope.

  For developers and advanced users, the practical advice is to treat Codex as a powerful assistant inside a governed development process. Use the new terminal and recovery improvements to work more clearly, but keep human review at the center of consequential changes. Review diffs, preserve evidence, avoid pasting secrets, test diagrams against reality, and ask your platform or security team when the observed behavior does not match the approved configuration. That discipline is what turns a useful CLI release into a reliable engineering workflow.

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  Useful Links

  • OpenAI Codex release notes for rust-v0.159.0
  • OpenAI Codex CLI documentation
  • OpenAI Codex basic configuration documentation
  • OpenAI Codex agent approvals and security guidance