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Keep the repository private until a qualified human has reviewed every AI-produced change and your release gates pass. Treat generated source, tests, dependencies, workflows, infrastructure files, and configuration as untrusted input. Publish only after critical findings are fixed or an authorized owner records a specific exception and rationale.
A passing test suite is evidence that selected scenarios work; it is not proof that the repository is secure, correctly licensed, reproducible, or safe to expose. The review below turns an AI-built project into an accountable release decision.
Start with a release gate, not a visibility change
Changing a GitHub repository from private to public is a production release. Set a written gate before anyone changes visibility:
- A named owner is accountable for the release.
- A qualified human engineer, independent of the person or agent that requested generation, has reviewed the implementation. OWASP AISVS states that AI-generated code always goes through review by a qualified human engineer; the AI agent does not count as that reviewer.
- Automated security, dependency, secret, infrastructure, and quality checks have run against the exact commit to be published.
- Critical findings are fixed, or an authorized person has documented why an exception is acceptable, who accepted it, and when it must be revisited.
- The project has a rollback plan, a security contact, and a retained record of evidence.
Keep the default branch and release artifacts private while these checks run. Reviewers should be able to inspect history, generated files, and build outputs without the pressure of a public deadline.
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1. Establish scope, provenance, and accountability
Before reading individual functions, map how the repository came into existence. Create a short inventory that identifies:
- Files and commits written or modified by a model, coding agent, autocomplete tool, or external snippet.
- Prompts, agent instructions, tool calls, and external sources that materially influenced the implementation.
- AI product names and versions, model versions where known, and the dates they were used.
- The human owner, the independent reviewer, and the person authorized to approve publication.
- Any high-impact area touched: authentication, authorization, cryptography, payments, privacy, CI/CD, cloud infrastructure, or production data.
Do not let one person perform every role on a sensitive change. Give security-critical or infrastructure changes a two-person or security-team review. Record the decision in the repository’s change record or release ticket, including the commit hash, findings, exceptions, and approvers. NIST’s DevSecOps guidance calls for AI-generated outputs to be traceable to their source context, passed through established SDLC gates, logged for auditability, and approved by accountable stakeholders.
2. Reconstruct what the repository really does
AI projects often look complete because a README and a green test command create a convincing surface. Reconstruct the actual behavior from the outside in.
Read the control files first
Inspect the README, package manifests, lockfiles, build scripts, Dockerfiles, infrastructure-as-code, CI workflows, release scripts, and deployment configuration before trusting application code. These files determine what runs, with which permissions, against which services, and under what assumptions. Look for commands that download or execute code, hidden network access, disabled checks, default credentials, broad cloud roles, debug flags, and deployment targets that differ from the documentation.
Build and test from a clean environment
Use a fresh checkout with only the network access and credentials required by the documented build. Confirm that the stated commands work without files left over from the developer’s machine. Capture the exact commit, toolchain versions, generated artifacts, and test results. A build that succeeds only because an undeclared local package, environment variable, cache, or credential exists is not reproducible.
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- 90W Pass-Through Charging: Achieve optimal charging with 90W pass-through power to your laptop, supported by a total input of 100W, with the hub reserving 10W for operational efficiency. (Note: Wall charger not included.)
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- What You Get: Anker USB-C Hub (5-in-1, 4K HDMI), welcome guide, 18-month warranty, and our friendly customer service.
Compare implementation with requirements
Make a requirement-to-code checklist. For each promised feature, identify the route, job, command, configuration, and test that implement it. Mark features that are missing, silently narrowed, unreachable, mocked, or guarded by a permanent feature flag. Search for duplicate logic, dead branches, placeholder credentials, broad exception handlers, and integrations that never leave a test double. Compare the behavior with the project’s threat model, not merely with its happy-path examples.
Design tests the generator did not write
Inspect assertions rather than accepting a green status. AI-generated tests may repeat the implementation’s assumptions, verify only response codes, or use mocks that bypass authorization and data validation. Add human-designed cases for malformed input, missing fields, expired sessions, privilege escalation, cross-tenant access, replayed requests, rate limits, dependency failures, partial writes, and sensitive data in errors or logs. Test negative paths and recovery behavior as deliberately as successful paths.
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3. Apply layered code, application, and infrastructure scans
OWASP AISVS recommends static analysis, interactive analysis, dynamic analysis, secret scanning, infrastructure-as-code scanning, and software-composition analysis for pull requests containing AI-generated code. Use the repository’s normal severity policy, and make the exact tool versions and configurations part of the review record.
| Control | What it examines | Human follow-up |
|---|---|---|
| Static analysis (SAST) and language linters | Source-level defects, unsafe APIs, data-flow problems, and maintainability rules | Confirm findings are real, inspect suppressed rules, and explain every accepted high-severity result. |
| Interactive or dynamic analysis (IAST/DAST) | Runtime behavior, reachable attack paths, malformed input, and response handling | Run against a representative service with test accounts and verify authorization boundaries manually. |
| Secret scanning | Keys, tokens, passwords, private keys, certificates, and credential-like strings | Investigate matches, revoke exposed values, and scan the complete history and artifacts. |
| IaC and container scanning | Terraform, Kubernetes, Docker, cloud permissions, images, and deployment defaults | Check public exposure, encryption, logging, privileged mode, and the identity used at runtime. |
| Software-composition analysis | Direct and transitive packages, known vulnerabilities, and license metadata | Remove unnecessary risk, set a remediation owner, and review changes to lockfiles. |
Run the checks on the proposed public state, not an earlier branch. A critical issue should block publication unless an authorized human records a written exception. Review pull-request triggers especially carefully: a fork workflow that executes untrusted code with write tokens or cloud credentials can turn a harmless contribution into a supply-chain compromise.
4. Verify dependencies and the software supply chain
Generate a complete list from manifests and lockfiles, including transitive dependencies. For every new package, ask four questions:
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- Was it intentionally selected for a requirement, or did an agent add it to silence an error?
- Is the package correctly named, maintained, and obtained from the expected registry?
- What install, build, or post-install scripts execute on a developer or CI machine?
- Does its license and security posture fit the project?
Check for typosquatted names, abandoned projects, duplicate libraries, unused packages, and dependencies that provide a much larger capability than the feature requires. Review lockfile diffs as carefully as source diffs; a small version change can alter executable installation behavior. Pin third-party CI actions and container images to immutable references where practical, and record how updates will be reviewed.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsRun dependency review and vulnerability scanning against the commit proposed for publication. Export an SPDX-compatible software bill of materials when the tooling supports it. Keep the SBOM with the release record so you can answer which components were present when a vulnerability is disclosed. A scanner result is not a waiver: a vulnerable package still needs an upgrade, removal, compensating control, or documented exception with an owner and deadline.
5. Find secrets and private material in the tree and history
Scan more than tracked source files. Include generated files, build output, archives, notebooks, fixtures, screenshots, logs, issue templates, release bundles, and every reachable Git commit. Search for API keys, access tokens, passwords, private keys, certificates, cloud credentials, database URLs, personal data, internal hostnames, and copied production configuration.
If a secret was committed
- Revoke or rotate it first. Removing text from the latest file does not invalidate a credential.
- Remove the value from Git history using an approved history-rewrite process, then force the corrected references to all relevant remotes.
- Check mirrors, forks, caches, package registries, releases, container layers, CI logs, and downloadable artifacts for retained copies.
- Review access logs for use of the exposed credential and preserve the incident record.
- Replace the value with an environment variable, vault entry, or encrypted secret-store reference.
Do not put operational credentials in project-tree files that an AI tool can read. Examples and tests should use unmistakably fake values that cannot authenticate anywhere. Treat a secret found in a screenshot or notebook as compromised even if the application never imports that file.
6. Review security behavior, not just syntax
Read the paths that protect data and privilege line by line. Confirm that:
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- Authentication verifies identity consistently, handles session expiry, prevents fixation, and does not fall back to a default account.
- Authorization is checked on every sensitive object and action, including background jobs and administrative endpoints; ownership checks cannot be bypassed by changing an identifier.
- Cryptography uses appropriate, maintained primitives and protects keys; plaintext secrets never appear in source, logs, URLs, or error responses.
- Input validation, output encoding, and parameterization address injection, cross-site scripting, command execution, and unsafe deserialization.
- File and URL handling constrains paths and destinations, with SSRF protections where the server fetches user-controlled URLs.
- Rate limits, lockouts, and abuse controls cover expensive or security-sensitive operations.
- Errors fail closed, reveal no credentials or personal data, and provide operators enough context without exposing internals.
- Logs are useful for detection, protected from tampering, and free of tokens, passwords, and unnecessary personal information.
- Tenants, projects, and environments cannot read one another’s data through alternate routes, exports, caches, or background tasks.
Pay extra attention to code generated from vague prompts or copied from an external snippet. Review every new public security group, bucket, endpoint, CORS rule, debug route, default account, and administrative path. Infrastructure and CI configuration are production code, not harmless plumbing.
7. Set a licensing and intellectual-property position
Inventory source code, generated code, vendored files, model outputs, training or application data, fonts, images, examples, and dependencies. For each item, record the license and obligations: attribution, notice files, source-availability terms, redistribution limits, and incompatible conditions.
Do not assume that AI-generated material is public-domain or license-free. An agent may reproduce distinctive text or code from a source with obligations that are absent from the generated file. Obtain legal review for copied-looking material, unusual assets, data of uncertain origin, and licenses that conflict with the project’s intended distribution. Decide whether the project or jurisdiction requires disclosure of AI involvement or generated-code provenance, and document that decision before publication.
8. Make the release reproducible and reversible
Prepare the public release from a clean checkout. Restrict network access and credentials to what the build needs. Verify that the artifact corresponds to the exact commit under review, and retain checksums and provenance metadata. If the project signs commits or tags, confirm the signing policy is actually enforced rather than merely documented.
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Before changing visibility, verify branch protection, required reviews, required status checks, and the rollback path. A rollback should identify the last known-good commit, the person who can restore it, and the steps for withdrawing a compromised release or image. Publish a security contact, vulnerability-reporting process, supported versions, setup instructions, configuration requirements, and known limitations. Preserve scan output, the SBOM, license inventory, exception approvals, and final sign-off.
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9. Control the AI tools used during review
Review automation can create a second attack surface. Treat free-form model output as untrusted text: never execute it automatically as a shell command, query, or workflow step. Run review agents in least-privilege, network-isolated sandboxes with no mounted repository secrets, ephemeral credentials, and allow-listed egress.
When policy and privacy requirements permit, log prompts, tool calls, outputs, and privileged actions. Keep merges, approvals, releases, and visibility changes behind a separate human authorization path. An agent may identify a finding or propose a patch; it must not be the sole authority that applies the patch and publishes the repository.
Choose a process that matches the risk
A small documentation utility and a payment service should not use the same release evidence. Compare the process on these dimensions:
| Dimension | Lightweight project | Team release | Regulated or high-impact release |
|---|---|---|---|
| Reviewer independence | One qualified human separate from the generator | Peer review plus an owner approval | Independent engineering and security or two-person approval |
| Scan coverage | Linting, SAST, secrets, dependencies | Add runtime, IaC, container, and workflow scans | All applicable scans with retained, repeatable evidence |
| Critical findings | Block unless the owner records an exception | Security or engineering lead accepts any exception | Formal risk acceptance, expiry date, and compensating controls |
| Dependencies and SBOM | Lockfile and vulnerability review | Dependency review and SPDX-compatible SBOM | SBOM, provenance, update ownership, and audit trail |
| Secret history | Current tree and reachable history | Add artifacts, mirrors, and CI logs | Document rotation, impact analysis, and evidence of eradication |
| Infrastructure and CI | Manual review of exposed resources | IaC and workflow scanning with protected branches | Separate security approval and tightly scoped credentials |
| License and provenance | Inventory and project-level decision | Per-component notices and generated-code record | Legal review, jurisdiction analysis, and retained provenance |
| Release controls | Known-good commit and rollback instructions | Signed or protected release with status checks | Attested artifacts, formal approval, monitoring, and tested rollback |
A practical final review sequence
Use this order when the repository is nearly ready. It minimizes the chance that a late discovery invalidates earlier work.
- Freeze the candidate. Record the commit hash and stop unreviewed changes.
- Complete the provenance record. Name tools, versions, contributors, reviewers, owner, and approver.
- Rebuild cleanly. Run documented build and test commands in an isolated environment and save results.
- Read control files. Review manifests, lockfiles, Dockerfiles, IaC, workflows, deployment, and release scripts.
- Perform human functional review. Compare requirements and threat model; add abuse, validation, authorization, and failure tests.
- Run automated gates. Execute SAST, IAST or DAST where applicable, secret, IaC, container, and dependency scans.
- Investigate every high-severity result. Fix it or obtain a written, time-bounded exception from the authorized owner.
- Complete supply-chain and license checks. Confirm package intent, provenance, licenses, notices, and the SBOM.
- Scan history and artifacts. Rotate any exposed secret and verify that copies are removed or inaccessible.
- Verify release mechanics. Check branch protection, required approvals, checksums, provenance, security contact, and rollback.
- Approve publication. The accountable human signs the release record, then changes visibility or publishes the selected artifact.
- Monitor after publication. Watch vulnerability reports, dependency alerts, CI changes, and suspicious access; keep the same gates for subsequent updates.
What “ready to publish” actually means
Ready does not mean “the AI says it is finished” or “all tests are green.” It means the repository’s behavior, dependencies, infrastructure, history, licenses, and release process have named owners and evidence. Unknowns are either resolved or explicitly accepted at the right level of authority. The public commit is reproducible, reversible, and supported by a record another reviewer can audit.
That standard is deliberately stricter for authentication, payments, privacy, cryptography, CI/CD, and infrastructure. Those areas can expose users or production systems even when the visible application feature appears small.
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