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Freedom251 › Blog › AI-generated code

AI-Built Repository Review: What to Check Before Going Public

By Freedom251 Team · · 10 min read

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Keep an AI-built repository private until a qualified human has reviewed it, automated security gates have passed, and an accountable owner has approved publication. Treat every generated file, test, workflow, dependency declaration, container, and configuration as untrusted input. Passing tests shows only that those tests passed; it does not establish that the code is secure, complete, correctly licensed, or safe to expose.

Set the scope and ownership before opening the repository

Start the review while the repository is private. Write down what is being considered for release: source code, generated files, documentation, examples, notebooks, build outputs, container definitions, infrastructure-as-code, CI workflows, release scripts, and the commit or tag that would become public.

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Build a provenance record

Identify which files and commits came from an AI assistant, which prompts or agent runs influenced them, and which external snippets, models, datasets, or packages were used. Record the AI tools and versions, the human owner, the intended audience, and the release decision. This record lets a reviewer trace an unusual line of code back to its context instead of treating the repository as an unexplained artifact.

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Assign a qualified human engineer who did not request the generation to perform the review. OWASP AISVS states: “AI-generated code always goes through code review by a qualified human engineer.” The agent that produced the code cannot serve as that reviewer. Authentication, authorization, cryptography, payments, privacy, CI/CD, and infrastructure changes deserve a second reviewer or a security-team review.

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Define the decision rule

Before looking at findings, agree that critical issues block publication unless an authorized person records a written exception, the rationale, the compensating control, and an expiry or follow-up date. Decide who can accept high-risk residual issues and who can change repository visibility. Without this authority chain, a technical review can be bypassed by a last-minute “publish now” request.

Reconstruct what the repository actually does

Read the repository from the outside in. The goal is to discover behavior that a generated feature description may omit, hide, or describe incorrectly.

Read the control files first

  • README files and setup instructions
  • Build scripts and package manifests
  • Lockfiles and dependency-resolution settings
  • Dockerfiles and container entrypoints
  • Infrastructure-as-code and deployment manifests
  • Continuous-integration workflows and release scripts
  • Configuration schemas, migration scripts, and feature flags

Compare these files with the application code. Look for dead code, duplicated implementations, unreachable branches, silently omitted features, mock-only integrations, placeholder credentials, disabled checks, and configuration that differs between development and production. A generated README can promise behavior that no route or command implements; a workflow can deploy a different artifact from the one tested locally.

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Reproduce the build and tests in a clean environment

  1. Check out the exact commit under review into a disposable environment.
  2. Use the repository’s documented installation, build, migration, and test procedures without relying on files or credentials from a developer workstation.
  3. Record the toolchain versions, operating-system assumptions, network access, and external services needed for each step.
  4. Confirm that the resulting artifact is the one the release process will publish or deploy.
  5. Repeat the critical path with a fresh checkout so untracked files and local caches cannot hide a failure.

Tests should exercise real behavior, not just return values from mocks. Inspect assertions for negative paths, malformed input, authorization boundaries, failure handling, retries, data validation, and tenant separation. Add human-designed cases for abuse, privilege escalation, data leakage, and unexpected state transitions. A passing AI-generated test suite is not proof of security; generated tests can reproduce the same mistaken assumptions as the generated implementation.

Compare implementation with requirements and threats

For each requirement, identify the code path, test, and configuration that enforce it. For each threat in the project’s threat model, identify the control and the evidence that it works. Mark requirements that are only documented, only mocked, or dependent on a production setting that has not been verified. A missing control is a release finding even when the visible feature works.

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Run security and infrastructure gates

OWASP AISVS recommends SAST, IAST, DAST, secret scanning, infrastructure-as-code scanning, and software composition analysis for pull requests containing AI-generated code. Use the repository’s normal severity policy, but make the evidence specific to the commit that will become public.

Gate What it examines Evidence to retain
Static analysis and linters Source-level defects, unsafe APIs, type errors, and policy violations Tool, version, configuration, commit, findings, and disposition
Interactive or runtime analysis Behavior reached only while the application runs Environment, exercised paths, alerts, and unresolved limitations
Dynamic testing Exposed services, validation, authentication, authorization, and error paths Target build, test scope, results, and blocked findings
Secret scanning Current files, generated output, history, and common credential formats Scan scope, detections, revocation records, and clean-up verification
IaC and container scanning Terraform, Kubernetes, Docker, cloud settings, and deployment manifests Configuration reviewed, alerts, and approved exceptions
Composition analysis Direct and transitive packages, actions, and images Dependency inventory, vulnerability results, and SBOM

Inspect CI workflows as production code

Review pull-request triggers, especially workflows that run for forks or untrusted contributions. Check whether untrusted code can execute with write tokens, cloud credentials, signing keys, or access to deployment environments. Verify that permissions are no broader than the job needs and that release jobs require an explicit approval path.

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Review Dockerfiles, infrastructure manifests, and release scripts with the same care as application code. Look for public security groups, public storage buckets, overbroad IAM, disabled encryption or logging, privileged containers, default accounts, and insecure defaults. A secure application can still become public through an exposed deployment configuration.

Block publication for critical findings unless the authorized owner records a written exception. The exception should name the affected component, explain why release is necessary, identify compensating controls, and set an owner and review date.

Verify dependencies and supply-chain integrity

Inventory the complete dependency graph

Enumerate direct and transitive dependencies from manifests and lockfiles. Include development tools, build plugins, container base images, CI actions, vendored code, fonts, and generated assets. A package used only during a build can still execute with access to source, credentials, or signing material.

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Check identity, maintenance, and provenance

  • Confirm that every new package is intentional and correctly named; investigate near matches that could be typosquats.
  • Check whether the package is maintained and whether its release source matches the expected registry or repository.
  • Inspect install and post-install scripts for unexpected network access, file changes, or credential handling.
  • Remove abandoned, duplicate, or unnecessary packages rather than accepting them because they were selected by an assistant.
  • Review lockfile changes as carefully as source-code changes and pin third-party actions and container images where practical.

Run dependency review and vulnerability scanning against the proposed public state, not merely the working branch. Export an SPDX-compatible software bill of materials when the tooling supports it. Keep the manifest, lockfile, scan output, and SBOM tied to the exact commit so a later reader can determine what was actually released.

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Find and remove secrets and private material

Search the current tree, generated files, build artifacts, and complete Git history for API keys, access tokens, passwords, private keys, certificates, cloud credentials, database URLs, personal data, internal hostnames, and signed configuration. Check examples, fixtures, notebooks, screenshots, logs, issue templates, test snapshots, release archives, and container layers. AI tools often copy a value into a harmless-looking example or diagnostic file.

If a secret was ever committed

  1. Revoke or rotate the credential first. Removing text from the latest commit does not invalidate a key that may already have been used.
  2. Remove the value from repository history using an approved history-rewrite process.
  3. Inspect mirrors, forks, release archives, package caches, container layers, CI artifacts, and backups for retained copies.
  4. Review access logs for misuse and document the incident and replacement credential.
  5. Run the history and artifact scans again before publication.

Store operational secrets in environment variables, a vault, or an encrypted secret store. Do not keep credentials in project-tree files that an AI tool can read. Use clearly nonfunctional example values in documentation and tests, and verify that examples cannot be mistaken for production credentials.

Review security behavior, not just syntax

Authentication, authorization, and sessions

Trace how identities are established, how permissions are checked, and where those checks occur. Test direct requests to protected routes, object identifiers belonging to another user or tenant, expired sessions, changed roles, disabled accounts, and missing or malformed credentials. Confirm that authorization is enforced server-side rather than inferred from a client-controlled flag.

Input, output, and resource handling

Inspect validation, output encoding, file uploads, URL fetches, deserialization, command execution, database queries, template rendering, and redirects. Consider injection, server-side request forgery, path traversal, unsafe archive extraction, and resource-exhaustion cases. Verify that limits and rate controls apply to unauthenticated and authenticated paths where abuse is possible.

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Errors, logs, and tenant isolation

Make security failures fail closed. Error responses should not reveal secrets, stack traces, tokens, internal hostnames, or personal data. Logs need enough context for investigation without duplicating credentials or sensitive payloads. For multi-tenant systems, test that queries, caches, background jobs, exports, and object storage cannot cross tenant boundaries.

Check newly public exposure

  • Open security groups, public buckets, and permissive cross-origin settings
  • Debug endpoints, health checks that disclose configuration, and default accounts
  • Unrestricted administrative routes or undocumented maintenance commands
  • Deployment credentials exposed to pull-request or fork workflows
  • Encryption, audit logging, or alerting disabled by generated defaults

Pay special attention to code produced from vague prompts or copied from an external snippet. A syntactically correct helper can still encode an unsafe trust boundary or an assumption that does not hold in your deployment.

Check licenses and intellectual property

Inventory the license and origin of source code, generated code, vendored files, model outputs, training or reference data, fonts, images, examples, and dependencies. Record obligations such as attribution, notice files, source-availability requirements, patent terms, and incompatible licenses. Keep the notices and attribution files required for redistribution.

Do not assume that AI-generated means public-domain or license-free. If an output is unusually distinctive, closely resembles a known project, or came from a source with unclear terms, obtain legal review before publishing it. Decide whether the project or applicable jurisdiction requires disclosure of AI involvement or generated-code provenance. OWASP SAMM calls for an explicit licensing and intellectual-property posture for AI-generated code; make that decision part of the release record rather than an informal assumption.

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Make the release reproducible and reversible

Prove what will be published

  1. Build from a clean checkout with network access and credentials restricted to what the build requires.
  2. Verify the exact commit, tag, generated files, and release artifact.
  3. Record checksums and provenance metadata for artifacts, containers, and packages where supported.
  4. Confirm that the public repository will not contain local configuration, ignored files needed for operation, or private submodules.

Prepare controls for the visibility change

Configure branch protection, required reviews, required status checks, and signed commits or tags where your project uses them. Confirm who can change visibility and who can publish releases. Test a rollback path: know how to restore the previous artifact, revoke a compromised credential, and temporarily return the repository to private visibility if a defect is discovered.

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Publish the information users need

Before changing visibility, add a security contact, vulnerability-reporting process, supported versions, setup instructions, configuration requirements, dependency and license notices, and known limitations. Preserve the review record, scan outputs, SBOM, license inventory, approved exceptions, and final approval. NIST’s DevSecOps model says: “AI-generated outputs should be traceable to their source context, reviewed through established SDLC control gates, logged for auditability, and approved by accountable stakeholders.”

Control the AI tooling used in the review

Free-form model output is untrusted text. Do not execute it automatically as shell commands, database queries, workflow steps, or deployment instructions. Require a human to inspect commands and their effects before execution.

Use least privilege and isolation

  • Run review agents in ephemeral, network-isolated sandboxes.
  • Do not mount repository secrets, production credentials, signing keys, or broad home-directory access.
  • Use short-lived credentials and allow-listed network egress only when a task needs external access.
  • Separate read-only analysis from actions that modify files, merge changes, approve checks, publish releases, or change visibility.

Log prompts, tool calls, outputs, and privileged actions when policy and privacy requirements permit. Keep merge, approval, release, and visibility changes behind a separate authorization path controlled by people, not by the reviewing agent.

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Choose a review depth that matches the risk

A small documentation utility and a service handling payment or personal data should not use the same release process. Use the following as a decision framework; the categories describe recommended control depth, not a claim that any one label is a universal standard.

Process Reviewer independence Scan and test coverage Infrastructure and CI review Evidence and release control
Lightweight A qualified reviewer separate from the requester Static analysis, dependency, secret, and relevant runtime checks Review any workflow, container, or deployment file included in the release Recorded findings, owner approval, and a tested way to undo publication
Team Two-person review for security-sensitive changes All applicable SAST, IAST, DAST, IaC, secret, and composition gates Explicit review of fork triggers, credentials, permissions, and public exposure SBOM, license inventory, provenance, required checks, and documented exceptions
Regulated or high impact Independent security or compliance review in addition to engineering review Repeatable gates with retained results, negative-path testing, and vulnerability disposition Formal threat model, least-privilege evidence, environment separation, and rollback exercise Traceable approvals, signed artifacts where required, audit retention, and accountable risk acceptance

Increase the depth when the repository handles authentication, money, health or personal data, cryptography, production infrastructure, privileged automation, or a large external user base. The presence of AI is an additional reason to preserve provenance and review evidence, not a reason to skip ordinary controls.

Record the final go/no-go decision

Make the publication decision from a single release record that identifies the exact commit and owner. It should show:

  • Human review completed by a qualified reviewer independent of the requester
  • Build and tests reproduced from a clean checkout
  • Security, infrastructure, secret, dependency, and license findings resolved or explicitly accepted
  • History and artifact scans completed after any credential rotation or cleanup
  • SBOM, provenance, notices, and release documentation attached where applicable
  • Rollback, incident contact, and vulnerability-reporting paths available
  • Authorized approval for the visibility change

Only after these conditions are satisfied should the owner change the repository’s visibility. If a critical finding remains, keep the repository private, fix it, or obtain a documented exception from the person accountable for the risk.

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