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Do not merge AI-generated code on the strength of a passing test suite or an automated review. Treat every agent-authored change as a contribution from an unknown contributor: establish what it is supposed to do, trace its data flow and trust boundaries, verify it with independent checks, and have an identified person make the merge decision.
This workflow keeps an agent useful without giving it uncontrolled authority. It covers pull-request review, high-risk code paths, dependency and test validation, sandboxing, credential protection, prompt-injection risks, and the evidence you should record before deployment.
What makes AI-generated code require a different review posture?
AI coding agents can produce plausible code quickly, but plausibility is not evidence of correctness, security, package legitimacy, or compatibility with your system’s threat model. An agent may also act on instructions hidden in an issue, README, log, web page, dependency changelog, or tool response. Those materials must be treated as untrusted input, not as authority.
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1. Set a narrow boundary before the agent starts
Security is easier to preserve when the agent cannot reach unnecessary systems or credentials. Define the task and the maximum authority in advance, then enforce those limits technically.
Limit files, commands, and network access
- Give the agent only the repository paths needed for the issue. Do not mount unrelated source trees, home directories, cloud credential folders, or production configuration.
- Allow only the commands and package registries required for the task. Disable arbitrary shell execution when the workflow permits.
- Use a sandboxed development container, disposable virtual machine, or isolated cloud workspace. A clean workspace should be easy to destroy after the task.
- Restrict outbound network access. If downloading dependencies or documentation is necessary, allow approved destinations rather than unrestricted browsing.
- Keep production secrets out of the environment. Assume that environment variables, shell history, configuration files, mounted volumes, and command output could become visible to the model.
Permission prompts are useful for visibility, but they are not a complete security boundary. A malicious instruction embedded in repository content can persuade an agent to request an apparently reasonable permission. Enforce the boundary outside the conversation as well.
Use attributable, short-lived identities
Run the agent under a distinct service identity or bot account where possible. Scope credentials to the exact repository and operation, avoid administrative roles, and make tokens short-lived. Log actions under that identity so a reviewer can determine which changes, commands, and external calls came from the agent.
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Control tool integrations
For MCP or similar tool integrations, maintain an approved tool list. Review tool descriptions and watch for changes to them. Validate arguments before execution, especially paths, URLs, repository references, and commands that can write data or alter permissions. A tool should receive only the minimum context needed for its operation.
2. Establish the intended behavior before reading the implementation
Start with the issue, acceptance criteria, design note, or other human-owned statement of intent. Write down the behavior that must remain true, including authorization rules, data ownership, failure behavior, and compatibility requirements. This gives the reviewer something stronger than “the generated code looks reasonable.”
Build a change inventory
Inspect the complete pull-request diff and its surrounding files. Record:
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- Every changed file and whether it was expected.
- New or upgraded dependencies, lock-file changes, generated artifacts, and configuration edits.
- New endpoints, event handlers, jobs, storage locations, network calls, or permissions.
- Deleted tests, weakened assertions, changed mocks, and altered error handling.
- Changes to persistent instruction files or hooks, including
AGENTS.md,CLAUDE.md,.github/copilot-instructions.md, and tool-specific rules. - Unexpected formatting-only churn that could hide a substantive change.
Do not stop at the displayed diff when surrounding behavior matters. Follow changed values through callers and callees, inspect the interfaces they cross, and identify the invariant the new code is supposed to preserve.
Require an explanation
Ask the author—or the human who directed the agent—to explain every security-relevant part of the change. If no responsible person can explain why a branch, permission, query, cryptographic choice, or dependency is present, the change is not ready to merge. Generated code that cannot be explained should be simplified, rewritten, or rejected rather than accepted on faith.
3. Prioritize security-sensitive paths
Review time should follow risk, not line count. A small authorization change can be more dangerous than hundreds of generated tests. Use the following areas as mandatory inspection points when the diff touches them.
| Area | Questions to answer |
|---|---|
| Authentication | Are credentials, sessions, tokens, reset flows, and expiry checks handled correctly? Are failure messages and rate limits appropriate? |
| Authorization | Is access enforced at the actual data or operation boundary, rather than only in a user interface? Are tenant, ownership, role, and object-level checks present on every relevant path? |
| Input parsing and validation | What happens with malformed, oversized, duplicated, encoded, or boundary-value input? Is validation performed before storage, interpretation, or execution? |
| Queries and output | Are queries built with safe parameters? Can untrusted data reach HTML, templates, commands, logs, headers, or other interpreters without context-appropriate encoding? |
| Cryptography | Are modern, supported algorithms and libraries used? Are keys generated, stored, rotated, and compared safely? Is deprecated or home-grown cryptography being introduced? |
| Deserialization and file handling | Can an attacker control serialized input, file names, paths, types, sizes, archives, or temporary locations? Are uploads isolated and scanned as appropriate? |
| External integrations | Are URLs, webhooks, APIs, and credentials validated? Are timeouts, retries, certificate checks, and response limits defined? |
| Infrastructure and CI/CD | Did the change expose a resource, widen a role, disable a control, use an unpinned action or image, or run untrusted pull-request code with privileged credentials? |
New public endpoints, third-party integrations, data stores, CORS rules, infrastructure definitions, and workflow files are trust-boundary or high-blast-radius changes. Compare each with the system’s threat model and inspect both success and failure paths.
4. Trace data flow and trust boundaries
For each changed input, draw a short path from origin to effect: request or message, parser, validator, business rule, storage or external call, and response or side effect. Mark where the data changes trust level. A validation check in a controller may not protect a later background job; a UI permission may not protect a direct API call.
Check authorization where the operation occurs
Verify that the code performing the read, write, delete, export, or administrative action checks the caller’s authority for the specific object and tenant. Test direct requests that bypass the normal interface, alternate identifiers, batch operations, and race-prone sequences.
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Inspect error and cleanup paths
Look for stack traces, tokens, personal data, internal paths, or query details in errors and logs. Confirm that failed authorization does not leave a partial write, temporary file, open transaction, or reusable token. Generated code often handles the happy path more completely than cancellation, timeout, retry, and concurrent-access cases.
5. Validate every dependency before using it
An agent’s package suggestion is not evidence that the package exists, is maintained, or is the intended project. OWASP warns about hallucinated package names and typosquatting, and models may not know recent vulnerability disclosures.
- Confirm that the package exists in the expected registry and that its name exactly matches the intended project.
- Inspect the version, publisher or maintainer history, release cadence, source repository, license, and provenance signals used by your organization.
- Check relevant security advisories and run the normal software-composition or dependency audit.
- Review the dependency’s transitive additions and requested installation or runtime privileges.
- Pin the approved version through the team’s ordinary lock-file and update process. Do not silently accept a broad or floating range.
- Record why the dependency is needed and who verified it.
If the package cannot be verified, remove it and implement the required behavior with an approved dependency or existing platform capability.
6. Review tests as critically as production code
Passing tests show that tested scenarios passed; they do not establish that the scenarios were sufficient or that the implementation is secure. Examine test changes for deleted cases, weaker assertions, mocks that bypass the behavior under test, and fixtures that accidentally grant excessive privileges.
Add independent adversarial cases
Write or request cases the agent did not author. Depending on the system, include:
- Malformed, missing, oversized, encoded, and boundary-value inputs.
- Expired, revoked, malformed, or wrong-audience tokens.
- Unauthenticated, unauthorized, cross-tenant, and object-ownership failures.
- Duplicate requests, retries, concurrent updates, and partial failures.
- Unexpected file types, path traversal attempts, archive expansion, and storage limits.
- Dependency or external-service failures, timeouts, and hostile responses.
Authentication, authorization, input validation, and cryptography tests deserve independent verification by a reviewer who understands the security requirement, not just the generated assertion.
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7. Run layered verification and interpret it correctly
Use multiple checks because each catches a different class of defect. NIST’s developer verification guidance describes broadly applicable minimum techniques; select the layers that fit the system and risk.
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|---|---|---|
| Unit and integration tests | Regression in specified behavior, interfaces, and known edge cases. | That untested business rules, abuse cases, or deployment settings are safe. |
| Static analysis | Known insecure patterns, type errors, tainted flows, and some configuration mistakes. | Correct authorization intent or every context-dependent business-logic flaw. |
| Software composition analysis | Known issues and policy violations in direct and transitive dependencies. | That a package is legitimate, correctly configured, or free of undisclosed defects. |
| Secret scanning | Credentials and common token formats committed to code or history. | Secrets stored in external systems, novel formats, or credentials exposed through runtime behavior. |
| Threat modeling | Trust-boundary changes, abuse cases, assets, and likely attack paths. | Implementation correctness without code-level verification. |
| Fuzzing and black-box tests | Unexpected parser, state, and robustness failures under varied input. | Complete coverage of authorization or business policy. |
| Web application scanning | Some externally observable weaknesses in a running deployment. | Source-level intent, hidden paths, and every configuration or logic error. |
Run the repository’s normal test suite, relevant static analysis, dependency analysis, and secret scanning on the pull request. Add structural or black-box tests, fuzzing, threat modeling, historical regression tests, or web scanning when the system warrants them.
Interpret findings rather than counting them. A scanner can miss context-dependent authorization and business-logic defects. An AI review comment is a hypothesis: verify the cited code, reproduce the behavior, and reject inaccurate fixes. A clean scan is the start of review, not its conclusion.
8. Treat repository context as an attack surface
When an agent reads external material, assume that material may contain instructions intended to redirect it. Relevant sources include issue and pull-request text, comments, README files, dependency changelogs, logs, fetched web pages, generated documentation, and tool responses.
Reduce and inspect context
- Provide only the files and conversation history needed for the task.
- Restrict arbitrary fetching when documentation can be supplied from an approved source.
- After the agent reads external content, inspect its commands, tool calls, edits, and network requests.
- Pay special attention to unexpected permission changes, disabled checks, data exfiltration attempts, and instructions to ignore security rules.
Protect persistent instructions and hooks
Instruction files and hooks are security-critical configuration. Require explicit human review when files such as AGENTS.md, CLAUDE.md, .github/copilot-instructions.md, or tool-specific rules change. Reject edits that weaken sandboxing, suppress tests, redirect secrets, or tell the agent to bypass review. Apply the same scrutiny to pre-commit hooks, CI scripts, and repository automation.
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Before execution, identify what code and data will be sent to the model or exposed through tools. Exclude production secrets, private keys, regulated data, and unrelated proprietary material. Use redaction or a smaller representative fixture when full data is unnecessary.
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Rotate any credential that may have been exposed in an agent workspace, command output, logs, or generated patch. Make sure access logs identify the agent identity and human owner. For persistent environments, remove temporary tokens and caches after the task and destroy disposable workspaces.
10. Make the merge decision accountable
Require an identified human owner to approve security-relevant changes. For sensitive paths, use a trained reviewer or security champion and require the independent approval policy appropriate to your organization.
Record a concise decision trail:
- What the change was intended to do and what files or boundaries it affected.
- Which tests, analyzers, dependency checks, secret scans, and other verification layers ran.
- Important findings, accepted risks, unresolved questions, and follow-up owners.
- Who reviewed the security-sensitive paths and who approved the merge.
If the evidence is incomplete, narrow the change, add verification, or defer deployment. Do not convert uncertainty into approval merely because the agent produced the code quickly.
Choosing verification tools for your workflow
There is no single best security tool. Compare categories against the system and your review process rather than selecting by a feature count.
| Decision factor | Questions |
|---|---|
| Language and framework support | Does the tool understand the languages, frameworks, templates, infrastructure files, and generated code you actually use? |
| Pull-request and CI integration | Can findings appear in the review workflow with reproducible runs, ownership, and failure policies? |
| Coverage | Does it inspect code, dependencies, secrets, infrastructure, or only one of those areas? |
| Policy and customization | Can you encode severity thresholds, approved dependencies, exception expiry, and organization-specific rules? |
| Finding quality | Can reviewers understand the affected data flow, reproduce the issue, and distinguish real defects from false positives? |
| Independent validation | Can a human or a second method confirm the result instead of accepting an opaque score? |
One documented platform example is GitHub’s third-party coding-agent validation announcement from June 9, 2026, which describes repository changes being checked with CodeQL, dependency advisories, and secret scanning. Availability and repository settings can change, so confirm the current configuration before relying on that capability. Even when enabled, those checks do not catch every issue and do not replace human approval.
A practical pull-request checklist
- The issue and acceptance criteria are clear, and a human can explain the generated design.
- The agent had only the required files, tools, network access, and short-lived credentials.
- The complete diff, surrounding callers and callees, generated files, and configuration were inspected.
- Authentication, authorization, parsing, cryptography, deserialization, file handling, integrations, infrastructure, and CI changes were reviewed when relevant.
- Every new dependency was verified for identity, provenance, maintenance, advisories, and version policy.
- Tests include malformed input, authorization failure, boundary, concurrency, and failure-path cases appropriate to the system.
- Repository tests, static analysis, composition analysis, secret scanning, and additional risk-based checks were run.
- Instruction files, hooks, tool definitions, and external content were checked for prompt-injection or control changes.
- Secrets and sensitive context were protected, and any possible exposure was handled.
- An identified human recorded the checks, unresolved risks, and approval.
When to stop and reject the change
Stop the merge if the package cannot be authenticated, the security boundary is unclear, a reviewer cannot explain the code, tests were weakened to make the build pass, privileged credentials were exposed, or the agent changed persistent instructions without explicit review. Also stop when a scanner reports a serious issue that has not been reproduced or dispositioned, when authorization relies only on a UI check, or when the requested scope expands beyond the original acceptance criteria.
The safest recovery is usually to revert the generated change, rotate any exposed credentials, preserve logs for investigation, and restart with a narrower task and a clean workspace. A fast rewrite is preferable to retaining code whose behavior and provenance no one can establish.
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