Review AI-generated code as if it came from an unknown contributor: understand the intended change, trace data and trust boundaries, verify it with independent checks, and require an accountable human to approve the merge. Keep the agent’s permissions narrow while it works, and treat its context, tools, dependencies and persistent instructions as additional attack surfaces.
The standard for AI-generated code
AI coding agents can create useful implementations quickly, but speed does not change who is responsible for the result. OWASP’s secure code review guidance puts the rule plainly: “Generated code needs at least the same scrutiny as code from an unknown contributor.” The agent is not the approver, and a green test run, static-analysis result or AI review comment is not proof that a change is secure.
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- Does the change implement the stated requirement rather than merely producing plausible code?
- What security boundaries, data flows and permissions does it affect?
- What independent evidence supports the behavior, including failure cases?
- Which named human accepts the remaining risk and owns the merge decision?
Use the workflow below for pull requests, direct agent edits and generated infrastructure. The depth of each step should match the blast radius of the change; authentication, authorization, cryptography, data access and deployment configuration deserve the most scrutiny.
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1. Set a narrow boundary before the agent starts
Security begins with what the agent is allowed to see and do. Give it only the repository paths, commands, tools and network access required for the task. A sandboxed development container, disposable virtual machine or isolated cloud workspace is safer than a workstation that contains production credentials, personal tokens or unrelated repositories.
Limit permissions and network access
- Use a separate service identity or bot account where possible.
- Grant the smallest repository and environment permissions needed for the task, avoiding administrative roles.
- Prefer short-lived, task-scoped credentials that can be revoked and attributed.
- Permit outbound network access only to approved destinations when fetching packages or documentation is necessary.
- Keep production databases, deployment keys and customer data outside the agent environment.
- Do not treat an interactive permission prompt as a complete security boundary; a manipulated agent may still request an apparently reasonable action.
Assume an agent could expose anything in environment variables, shell history, configuration files, mounted home directories or tool output. Remove secrets before starting, use redacted test data and review workspace mounts. If the task needs a privileged operation, perform that operation separately under a human-controlled process instead of widening the agent’s standing access.
Control integrated tools
For MCP or similar tool integrations, maintain an approved tool list. Review tool descriptions and notice changes to those descriptions, because a tool’s declared purpose and its actual behavior are both part of the attack surface. Validate arguments before execution, especially paths, URLs, shell commands, repository identifiers and deployment targets. Log tool calls with the agent identity so an unexpected action can be investigated.
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Start with the issue, acceptance criteria and relevant threat model. Write down what must change, what must remain unchanged, which data is sensitive and which users or services are trusted. This prevents a reviewer from judging the generated code only by whether it compiles.
Inspect the complete pull request
Review the full diff and its metadata, not just the files the agent says it edited. Check for:
- Unexpected files, generated artifacts or scope outside the request.
- New packages, lockfile changes and altered build scripts.
- Configuration, infrastructure or CI/CD edits.
- Deleted tests, weakened assertions or changed mocks.
- Changes to agent instruction files, hooks or repository automation.
- Permission, authentication, CORS, logging or error-handling changes hidden in a seemingly unrelated refactor.
Ask the author to explain every security-relevant part of the change. If nobody can explain why a line exists, the pull request is not ready to merge. Generated code should be readable enough for a human owner to maintain and defend.
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Follow the change beyond the diff
Trace changed values through callers and callees. A local validation check may be bypassed by another entry point; an authorization decision in a controller may not protect a background job; and a sanitized value may become dangerous after a later conversion or concatenation. Identify the invariant the change is supposed to preserve, then inspect the code that establishes, transforms and consumes that invariant.
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3. Prioritize security-sensitive paths
Not every line deserves equal review time. Begin at trust boundaries and operations with high impact if compromised.
Authentication and authorization
- Verify authentication before sensitive work and ensure failures fail closed.
- Check authorization at the actual data or operation boundary, not only in a user interface or route guard.
- Trace tenant, account and ownership checks through every read, write, export and administrative operation.
- Test expired, revoked, malformed and cross-tenant credentials.
- Look for privilege changes caused by defaults, role mapping, object identifiers or background jobs.
Input, parsing and output
- Trace untrusted input from request parsing through validation, storage, logging and output encoding.
- Look for string-built SQL, shell commands, templates, regular expressions and URLs.
- Check size, type, range, character-set and structural limits, including malformed and boundary values.
- Ensure error responses do not disclose secrets, tokens, internal paths or sensitive records.
Cryptography and sensitive data
- Confirm that the implementation uses approved, current primitives and safe key handling rather than inventing algorithms or defaults.
- Check password storage, token generation, random-number sources, certificate validation and key rotation.
- Inspect whether personal, financial or operational data is copied into logs, prompts, telemetry, caches or test fixtures.
Deserialization and file handling
- Review serialized input for gadget execution, type confusion and unrestricted object creation.
- For uploads, validate content and size, generate storage names, prevent path traversal, isolate execution and enforce authorization on download.
- Check archive extraction, temporary files and cleanup for symlink and resource-exhaustion problems.
Integrations and infrastructure
New public endpoints, third-party integrations, data stores, network exposure, infrastructure definitions and CI/CD workflows can widen the trust boundary. Compare them with the threat model. Look for exposed resources, broad service roles, disabled security controls, unpinned actions or images, and untrusted pull-request code executing with privileged credentials.
4. Validate every dependency independently
An agent’s package suggestion is not evidence that a package exists, is legitimate or is current. For each new dependency, verify the exact package in the expected registry and confirm that it is the intended project rather than a similarly named package. Inspect its version, provenance, maintainer history, release activity and relevant security advisories.
- Search the registry and confirm the package name, publisher and repository identity.
- Read the package’s declared dependencies and installation scripts for unexpected behavior.
- Check the selected version against the team’s approved support policy and vulnerability database.
- Run the normal software-composition or dependency audit used by the project.
- Pin the version through the repository’s ordinary lockfile and update process.
- Document why the dependency is needed and remove it if the standard library or an existing approved component is sufficient.
OWASP warns that models can hallucinate package names, recommend typosquatting targets and miss recent vulnerability disclosures. Treat those as supply-chain risks, not minor quality issues.
5. Review tests as critically as production code
Generated tests often prove that generated code behaves as written, not that it satisfies the requirement. Examine the test design and add cases the agent did not author.
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Look for weakened evidence
- Deleted tests or assertions changed from exact expectations to broad truthiness checks.
- Mocks that bypass authorization, validation, cryptographic verification or the real data access path.
- Fixtures that omit ownership, tenant or failure-state data.
- Tests that encode an accidental implementation detail instead of the acceptance criterion.
- Snapshots updated without a deliberate review of the resulting behavior.
Add adversarial and boundary cases
Depending on the system, test malformed input, maximum and minimum values, duplicate requests, expired tokens, authorization failures, concurrent access, partial outages, retries, time-zone boundaries and corrupted stored data. Security-critical tests for authentication, authorization, input validation and cryptography warrant independent verification by a reviewer who understands the threat model.
Passing tests establish only that those test cases passed in that environment. They do not establish that untested business rules, deployment settings or integration behavior are safe.
6. Run layered verification and interpret results correctly
Use several independent signals on the pull request:
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| Static analysis | Known insecure patterns, type and data-flow issues | Every context-dependent authorization or business-logic flaw |
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| Secret scanning | Recognizable credentials and sensitive tokens in the change | Secrets hidden by encoding, runtime retrieval or unrecognized formats |
| Threat modeling | Trust-boundary changes, abuse cases and impact | A substitute for implementation and runtime testing |
| Fuzzing or web application scanning | Robustness and externally observable weaknesses where applicable | Complete coverage of internal workflows and business rules |
NIST’s developer-verification guidance presents these techniques as broadly applicable minimum practices. Select the combination that fits the system, then investigate findings rather than suppressing them to obtain a clean dashboard.
AI-generated review comments are hypotheses. They may miss a real issue, misunderstand a code path or recommend an inaccurate fix. Reproduce a finding, inspect the surrounding flow and record why it was accepted, fixed or rejected.
7. Treat agent context as untrusted input
An agent may process issue text, pull-request comments, README files, dependency changelogs, logs, fetched web pages and tool responses. Any of these can contain instructions that redirect the agent. A comment that says to disable a check, print a secret or run a deployment command is data, not authority.
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Reduce prompt-injection opportunities
- Give the agent only the context needed for the task.
- Restrict arbitrary outbound fetching when external content is unnecessary.
- Keep secrets and sensitive source out of prompts and tool responses.
- After the agent reads external material, inspect its commands, edits and network activity.
- Require a human confirmation for destructive, privileged or production-facing actions.
Do not assume that repository documentation is trustworthy merely because it is version-controlled. Persistent steering files and hooks, including AGENTS.md, CLAUDE.md, .github/copilot-instructions.md and tool-specific rules, should be reviewed as security-critical configuration. Require explicit review when they change, and reject edits that weaken controls or tell the agent to ignore security checks.
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8. Keep credentials, code and instructions protected
Know what context is sent to the model, where it is processed and how long it may be retained under your organization’s policy. Exclude production secrets, customer records, private keys and unrelated proprietary code. Use an attributable agent identity and scoped, short-lived credentials so actions can be audited and revoked.
Review changes to ignore files, hooks, build scripts, CI definitions and instruction files with the same care as application code. A small configuration edit can change which tests run, which files are uploaded or which credentials are available. Compare the effective permissions before and after the pull request, not just the textual diff.
9. Make the merge decision explicit
The pull request should record what was reviewed, which checks ran and what risks remain. Require an identified human owner to approve security-relevant changes. For sensitive paths, use a trained reviewer or security champion and obtain the independent approval required by your policy.
Minimum approval record
- Issue or acceptance criteria linked to the implementation.
- Files, data flows and trust boundaries inspected.
- Tests, static analysis, dependency checks and secret scanning performed, with results.
- Threat-model, fuzzing or web-scanning evidence when appropriate.
- Unresolved risks, compensating controls and a follow-up owner.
- Name or attributable identity of the person approving the merge.
If the change affects authentication, authorization, cryptography, sensitive data, infrastructure or CI privileges, “the agent approved it” is never an acceptable decision record.
10. Choose tools by coverage and workflow, not by a single score
There is no universal best security tool for agent-authored code. Compare a candidate tool or service on the dimensions that affect your repository:
- Supported languages, frameworks and build systems.
- Pull-request and CI integration, including support for changed-file analysis.
- Coverage of source code, dependencies, secrets and infrastructure.
- Policy customization, severity handling and exception workflows.
- Clarity of findings in the diff and the ability to reproduce them independently.
- Operational cost, scan time, data handling and maintenance requirements.
One documented platform example combines CodeQL, dependency advisories and secret scanning to validate repository changes. Its announcement on June 9, 2026 describes a feature whose availability depends on current repository settings; verify those settings before relying on it. The example illustrates layered validation, not a guarantee that the checks find every issue or that the platform fits every team.
11. A pull-request checklist you can reuse
- Boundary: Confirm the agent used an isolated workspace, least-privilege identity, scoped credentials and approved tools.
- Intent: Read the issue and acceptance criteria; identify sensitive data, users, services and invariants.
- Scope: Inspect the complete diff, dependencies, generated files, configuration, CI changes, tests and instruction files.
- Data flow: Trace untrusted input, authorization decisions, storage, logging and output through callers and callees.
- High risk: Examine authentication, authorization, cryptography, deserialization, uploads, integrations and infrastructure first.
- Dependencies: Verify package identity, provenance, maintenance, advisories and pinned versions.
- Tests: Reject weakened assertions and add independent negative, boundary and concurrency cases.
- Checks: Run tests, static analysis, composition analysis and secret scanning; add threat modeling, fuzzing or web scanning where suitable.
- Context: Review external content, tool calls and persistent instructions for prompt-injection or control changes.
- Decision: Record findings, residual risk and the named human approval before merge.
12. What to do when a review fails
The agent changed more than requested
Stop and revert the unneeded edits rather than reviewing an enlarged scope under time pressure. Re-run the agent with narrower paths and commands, then compare the new diff with the original acceptance criteria.
A package looks suspicious
Do not install or build with it on a privileged machine. Remove it, verify the intended package through the expected registry and review the workspace and dependency lockfile for related changes. If credentials were available, rotate them and investigate access logs.
A scanner reports a finding
Reproduce the data flow and determine exploitability, affected versions and the correct remediation. Do not suppress the finding solely because tests pass or an AI reviewer disagrees. Record the rationale if the finding is a confirmed false positive or accepted risk.
Secrets may have entered the context or diff
Revoke or rotate the exposed credential immediately, remove it from the repository and generated artifacts, inspect logs and provider access records, and determine whether model or tool retention requires additional notification under your policy.
The agent followed an instruction from a comment or document
Preserve the relevant logs, isolate the workspace, inspect every action taken after the content was read and restore the last known-good state. Tighten context, tool and network restrictions before retrying.
Bottom line
Secure review of AI-generated code is ordinary secure engineering with stricter attention to provenance and agency. Narrow the agent’s authority, verify the full change and its dependencies, examine trust boundaries and failure paths, use layered automated checks, treat context as untrusted, and make a named human responsible for the merge. Automation expands coverage; it does not transfer accountability.
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