The Tool Desk
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Review AI-generated code as an untrusted proposed change, not as finished software. Before merge, define the required behavior and threat model, run the same build, test, security, and dependency gates used for human code, inspect the complete diff, and add tests that were designed independently of the generator. High-risk paths such as authentication, authorization, cryptography, parsers, deployment files, and secret handling need qualified human review, often with a second approval. The person who accepts the change remains accountable for its security and correctness.
What should you establish before reviewing AI-generated code?
Start by writing down what the change is supposed to do and what it must not do. A precise review target prevents a plausible implementation from passing simply because it looks clean.
Define behavior and boundaries
- State the user-visible behavior, API contracts, invariants, error semantics, and performance or availability requirements.
- List the files, modules, database objects, services, and configuration areas the change is allowed to touch. Treat an unexplained extra file as a review finding.
- Record project conventions and architectural constraints, including approved libraries, supported language and framework versions, data-access patterns, and logging rules.
- Classify data handled by the change: public, internal, confidential, personal, regulated, credentials, signing material, or other restricted data.
- Describe the threat model: who can call the code, what they control, what an attacker would gain, and which trust boundaries the code crosses.
GitHub guidance emphasizes checking whether an output solves the right problem and fits the project’s architecture, rather than judging only whether it compiles. The same question should be written into the pull request’s acceptance criteria.
Turn the requirement into reviewable evidence
For each requirement, identify at least one observable check: a test, a schema constraint, a policy rule, a log or metric, or a manual inspection point. Mark assumptions that the tool made, such as a default timeout, a nullable field, a browser trust decision, or an implied authorization role. Unrecorded assumptions are common sources of defects because generated code can make them look intentional.
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Which deterministic checks should run first?
Run reproducible gates before spending time on stylistic discussion. A failing build or scanner gives you an objective failure to fix; a passing gate does not prove the change is safe, but it establishes a baseline.
| Gate | What it can reveal | How to handle a failure |
|---|---|---|
| Compile or build | Type errors, missing symbols, incompatible generated artifacts, packaging mistakes | Stop and fix the build before interpreting test results. |
| Existing unit tests | Regressions in local behavior and contracts | Determine whether the failure is a changed requirement, a broken implementation, or a brittle test; do not delete the test to make the pull request green. |
| Integration tests | Database, queue, filesystem, service, and serialization interactions | Inspect setup, teardown, retries, timeouts, and data isolation as well as the assertion. |
| End-to-end and regression suites | Cross-component workflows and user-visible breakage | Reproduce in a clean environment and compare the changed path with an unaffected path. |
| Static analysis (SAST) | Injection, tainted data flows, unsafe APIs, control-flow and configuration issues | Classify each finding; document an accepted false positive rather than suppressing it silently. |
| Software-composition analysis (SCA) | Known vulnerabilities, transitive risk, unsupported packages, and version conflicts | Upgrade, remove, or formally accept the risk with an owner and expiry. |
| Secret scanning | Credentials, tokens, private keys, and connection strings in source or history | Revoke exposed material immediately, then remove it and repair the source of exposure. |
| Infrastructure and configuration checks | Overbroad cloud permissions, insecure network rules, unsafe CI settings, and deployment drift | Review the effective permissions and production impact, not merely the file diff. |
| Dynamic or abuse checks | Runtime authorization failures, malformed input handling, race conditions, and boundary behavior | Capture the request, identity, state, and timing needed to reproduce the issue. |
GitHub specifically identifies CodeQL, Dependabot, and code-quality checks as useful controls. OWASP’s guidance says AI-generated code should pass the same SAST, SCA, and secret-scanning gates as other code. Run those controls on every relevant pull request; do not create a weaker “AI lane.”
How do you read an AI-generated diff for security and correctness?
Read the diff as if it came from an unfamiliar contributor. First reconstruct the data flow and control flow, then challenge every trust decision.
Check authorization before functionality
Verify that every read, write, update, delete, administrative action, and indirect object reference is authorized for the current identity and tenant. Look for checks performed only in a user interface, a route that accepts an identifier without ownership validation, role names compared as strings, and fallback branches that grant access when identity information is missing. Confirm that authorization is enforced again in background jobs and direct service calls, not only at the first HTTP endpoint.
Trace untrusted input
Follow request fields, headers, uploaded files, messages, environment variables, and database values to SQL, shell, template, HTML, path, deserialization, and logging sinks. Check validation, canonicalization, encoding, size limits, and parser settings at the point of use. A generated helper may appear to validate input while accepting a broader type or silently truncating dangerous content.
Challenge failure and timing behavior
Inspect timeouts, retries, cancellation, partial writes, idempotency, and transaction boundaries. Ask what happens when a dependency is slow, returns malformed data, closes a connection, or succeeds after the caller times out. Look for retries on non-idempotent operations, unbounded loops, swallowed exceptions, and error messages that expose secrets or internal topology.
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Review cryptography and sensitive data handling
Do not approve new cryptographic code merely because it uses a familiar algorithm name. Verify key generation, storage, rotation, nonce or IV handling, authenticated encryption, certificate validation, password hashing, and downgrade behavior against your organization’s standard. Ensure tokens, passwords, personal data, and encryption keys are absent from logs, traces, analytics events, test fixtures, and exception payloads.
Look for concurrency and resource hazards
Examine shared state, locking, cache invalidation, async cancellation, connection pools, file handles, thread safety, and ordering assumptions. Generated code often passes single-threaded tests while failing under duplicate messages, concurrent updates, or a retry storm. Check that limits exist for memory, CPU, request size, queue depth, and recursion where an attacker or unexpected input can control them.
Assess architecture and ownership
Ask whether the new abstraction belongs in the layer where it was placed, whether it duplicates an existing utility, and whether another maintainer can diagnose it six months from now. Prefer a smaller change that follows established patterns over a broad generated framework. Remove dead branches, speculative configuration, and comments that describe intentions the code does not enforce.
Can you trust tests written by an AI coding tool?
No. Generated tests are useful starting points, but they can repeat the implementation’s assumptions and assert that the code behaves as written rather than as required. Verification that relies on AI can complement, but cannot replace, deterministic checks.
Separate test authorship from implementation assumptions
Write a short requirement-based test plan before reading or accepting generated tests. Derive cases from contracts, threat models, state transitions, and failure behavior. Then compare the tool’s tests with that plan. If every test was generated from the same prompt and code path, treat the suite as correlated evidence rather than independent verification.
Add negative and boundary cases
- Malformed, missing, duplicated, oversized, and out-of-order inputs.
- Unauthorized users, expired sessions, cross-tenant identifiers, and privilege changes during a request.
- Empty collections, maximum limits, Unicode and normalization edge cases, clock skew, and time-zone boundaries.
- Dependency timeouts, partial responses, connection resets, retries, cancellation, and process restarts.
- Concurrent updates, duplicate deliveries, replayed requests, and transactions that fail halfway through.
- Abuse cases designed to bypass validation, rate limits, isolation, or audit logging.
Inspect assertion quality
A test that only checks a status code, that a promise resolves, or that a mock was called may pass while the wrong record is returned or sensitive data leaks. Require meaningful assertions on state, authorization, side effects, error classes, and externally observable output. Check that fixtures are realistic, cleanup is reliable, and tests fail when the relevant behavior is deliberately broken.
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Use independent implementation paths where practical
For critical calculations, compare against a hand-derived oracle, a known-good fixture, a database constraint, or a second implementation with different logic. Mutation testing, fault injection, and property-based tests can reveal suites that remain green after the behavior is damaged. NIST’s 2025 GenAI Code Challenge Evaluation Plan measures how effective AI-generated unit tests are for elementary Python code; it is an evaluation plan, not a universal defect-rate statistic for production systems.
How should you verify dependencies introduced by generated code?
Assume a new package may be hallucinated, unnecessary, compromised, incompatible, or licensed in a way your project cannot accept until it is checked.
- Confirm that the package exists in the trusted registry your organization uses and that the requested name is not a look-alike.
- Inspect maintainer identity, release history, repository activity, supported runtime versions, and whether ownership changed recently.
- Review direct and transitive vulnerabilities, abandoned releases, install scripts, native components, and network or filesystem behavior.
- Check the license and notices against your distribution and compliance requirements.
- Ask whether the dependency is necessary. Prefer an existing approved library or a small local implementation when that reduces attack surface and operational burden.
- Pin versions according to policy, update the lockfile intentionally, and record why the dependency was added.
- Build and test from a clean environment so undeclared local packages or cached artifacts cannot hide a missing dependency.
Dependency analysis should cover transitive packages, not only the line added to the manifest. A package that solves a trivial task can create a large maintenance and vulnerability surface.
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Risk-based review is more effective than treating every line identically. The following areas warrant a qualified reviewer and, where policy requires, a second or security-team sign-off.
| Code path | Why the risk is high | Additional evidence to require |
|---|---|---|
| Authentication and session management | Small errors can enable account takeover or session fixation. | Threat-model review, negative authorization tests, secure cookie or token checks, and security-owner approval. |
| Authorization and tenant isolation | Data exposure may affect many users while ordinary tests remain green. | Cross-role and cross-tenant tests, direct-service checks, audit evidence, and a second reviewer. |
| Cryptography and key handling | Incorrect primitives or lifecycle decisions can be invisible until compromise. | Approved design, key-management review, rotation and revocation tests, and specialist sign-off. |
| Parsers, deserializers, and file processing | Complex inputs can trigger injection, denial of service, or memory corruption. | Fuzzing or malformed-input tests, resource limits, sandboxing, and dependency review. |
| CI/CD, build, and deployment files | Changes can alter what code ships or which credentials a job can use. | Effective permission diff, protected-environment review, reproducible build evidence, and approval-gate testing. |
| Secrets and production configuration | Exposure or overbroad access can be immediate and difficult to reverse. | Secret scan, credential-scope review, rotation plan, and confirmation that production values were not supplied to the agent. |
| External interfaces and webhooks | Untrusted callers and retries create authentication, replay, and availability risks. | Signature and replay tests, rate limits, idempotency checks, and timeout behavior. |
AISVS guidance calls for elevated review of security-critical files and automated testing on every AI-generated pull request. Apply the same standard to generated changes that indirectly alter a high-risk path, such as a shared middleware, serializer, policy helper, or build script.
How do you contain an AI coding agent while it works?
Review begins before code is generated. The agent’s permissions and inputs determine how much damage a mistaken instruction or malicious repository file can cause.
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- Use a sandbox, isolated branch, disposable workspace, or ephemeral development environment for untrusted tasks.
- Grant the minimum filesystem, network, repository, and command permissions needed for the task. Allowlist tools where possible.
- Use short-lived, narrowly scoped credentials. Never provide production secrets, signing keys, administrator tokens, or broad cloud credentials.
- Require an explicit approval prompt before irreversible actions such as deleting data, pushing protected branches, publishing packages, changing deployment policy, or rotating credentials.
- Keep network access restricted and log tool calls, commands, file changes, and approvals.
- Treat repository instruction files, issue text, comments, and tool descriptions as untrusted input. Review them before allowing an agent to act on their directions.
- Destroy or reset the environment after the task when it contained sensitive context or elevated access.
Containment limits blast radius; it does not make generated output trustworthy. The resulting diff still needs the same tests, scans, and human approval as code written without an agent.
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Make the decision traceable enough that another engineer can reconstruct what was generated, what changed, and why it was accepted.
- Label the commit or pull request as AI-assisted according to your repository policy.
- Describe which files or functions were generated, which were substantially amended by a human, and which tool or model contributed if your policy requires that detail.
- Attach build, test, scanner, dependency, and infrastructure-check results, including the version or configuration needed to reproduce them.
- Record exceptions, accepted findings, compensating controls, owners, and expiry dates. Do not hide an unresolved warning in a generic “known issue” note.
- Identify the human approver and confirm that the approver understood the threat model and reviewed the complete diff.
- Retain enough evidence for incident response, audit, and future maintenance without storing secrets or unnecessary personal data.
UK Home Office engineering guidance states that AI-assisted outputs must be reviewed and approved by a human before production, and that AI-assisted code must meet the same security expectations as human-written code. Teams retain full accountability even when a tool produced most of the text.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does a merge-ready checklist look like?
Use this checklist at the final decision point. A “yes” means evidence exists, not merely that someone believes the condition is true.
- The requirement, interfaces, data classification, threat model, and allowed scope are documented.
- The build is reproducible and passes with the repository’s supported toolchain.
- Unit, integration, end-to-end, and regression tests relevant to the change pass.
- Independent boundary, malformed-input, authorization, concurrency, failure-recovery, and abuse tests exist where applicable.
- SAST, SCA, secret scanning, infrastructure checks, and relevant dynamic tests have run.
- Every new dependency is real, necessary, supported, vulnerability-checked, license-checked, and pinned as required.
- The diff has been inspected for injection, authorization gaps, unsafe cryptography, leakage, races, resource exhaustion, and architectural mismatch.
- High-risk files have qualified review and any required second approval.
- The agent used isolated, least-privilege access without production secrets or signing keys.
- AI contribution, amendments, results, exceptions, and human approval are recorded.
If any item lacks evidence, keep the change out of the merge queue or narrow its scope until the gap is resolved. A green pipeline is one input to the decision, not the decision itself.
How can you compare two review workflows?
When choosing between processes, compare controls rather than counting how many automated tools each process invokes.
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| Axis | Questions to ask |
|---|---|
| Coverage | Does it include build, unit, integration, end-to-end, SAST, SCA, secret, infrastructure-as-code, and dynamic or abuse testing? |
| Independence | Were requirements and tests authored separately from the generated implementation, or did the same prompt produce both? |
| Risk sensitivity | Does it add controls for authentication, authorization, cryptography, parsers, deployment, and secret paths? |
| Traceability | Are AI attribution, approvals, exceptions, artifacts, and reproducible results recorded? |
| Agent containment | Are workspace, network, credentials, tools, and irreversible actions isolated and approval-gated? |
| Maintainability | Does the workflow check dependency quality, architecture fit, readability, operational ownership, and upgrade burden? |
A workflow that runs fewer but relevant checks with independent test design and strong containment is safer than one that produces a large volume of correlated, automatically generated evidence.
What does current evidence say about AI-written code quality?
There is no authoritative defect-rate or productivity percentage that generalizes safely across models, languages, repositories, and review processes. Quality depends heavily on the prompt, context supplied to the tool, project controls, and the human review that follows.
The 2025 NIST GenAI Code Challenge Evaluation Plan is designed to measure AI-generated unit tests for elementary Python code; it should not be read as a production-wide estimate. OWASP AI Testing Guide Version 1, published in 2025, provides broader trustworthiness-testing context for AI systems but does not establish a defect rate for AI-written application code. These limits are a reason to rely on observable gates and independent review rather than a claimed average success rate.
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Frequently Asked Questions
Should AI-generated code be merged in a separate branch or repository?
Isolation can reduce accidental impact and make permissions easier to control, but branch location does not change the acceptance standard. Merge only after the change passes the project’s normal gates and a human approver can inspect the final diff and evidence.
What should I do when an AI tool’s test fails intermittently?
Treat flakiness as a defect in the test or the system, not as harmless noise. Capture timing, order, environment, and external-service details, then remove shared state and uncontrolled dependencies or fix the race before relying on the result.
How can a small documentation-only AI change be reviewed?
Confirm that it cannot alter executable configuration, generated artifacts, permissions, or release automation. Check links, commands, version claims, and security guidance for accuracy, and run the lightweight validation required by the repository even when a full application test suite is unnecessary.
Who is accountable if an AI-generated vulnerability reaches production?
The organization and the human who approved and shipped the change remain accountable under normal engineering and security policies. AI assistance does not transfer responsibility to the tool or reduce the duty to investigate, remediate, and document the incident.
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