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AI-generated code should be treated as an untrusted draft, not as a finished change. To fix it safely, first write down the intended behavior, reproduce the failure, and reduce it to the smallest failing case. Then inspect the complete diff, verify every dependency, run independent tests and security checks, and have a responsible developer approve the result before merge or deployment. This process catches ordinary bugs, hallucinated packages, weakened tests, prompt-injection risks, secret leakage, and unrelated edits that look harmless in a quick review.
Microsoft’s VS Code guidance warns that generated code can contain bugs, security issues, and subtle logic errors. The practical rule is simple: every AI change receives the same—or stricter—review, testing, and scanning as code written by a person.
Why does AI-generated code fail even when it looks plausible?
Most failures come from the gap between plausible text and verified behavior. A model predicts a likely implementation from its context; it does not guarantee that the code matches your requirements, your installed versions, your threat model, or the files it is changing.
Wrong or irrelevant output
Generated output can drift from the request, return nonsensical code, or trigger unexpected tool calls. GitHub describes this as probabilistic model behavior. Stop an active agent when its actions become unexpected, inspect the files and commands it touched, and retry with a narrower prompt or a different model.
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Hidden assumptions and ordinary bugs
AI often assumes a value is non-null, an input is trusted, a call cannot fail, or two operations cannot run concurrently. Check types, null and error paths, boundary conditions, concurrency, resource cleanup, and whether the implementation actually satisfies the stated requirements. A small reproducer is more useful than asking for a broad rewrite.
Dependencies that do not exist or are unsafe
A model may invent a package name, confuse similarly named libraries, or select an unmaintained project. A real package can still be a typosquat or contain a vulnerable version. Dependency installation is an execution step, not a harmless suggestion.
Security and trust-boundary mistakes
Generated code may omit authorization, validation, output encoding, safe path handling, secure deserialization, or appropriate error handling. Security-sensitive code, trust-boundary changes, CI and build files, and dependency changes require elevated review.
Scope drift, weakened tests, and leaked context
Agents can edit lockfiles, workflows, Dockerfiles, package scripts, deployment manifests, or tests outside the requested task. They can also delete tests, weaken assertions, replace real dependencies with permissive mocks, or encode the bug they just introduced. Sensitive prompts and repository context can expose credentials, proprietary data, or private source code to an assistant provider.
How do I debug code written by an AI assistant?
Use a repeatable sequence instead of accepting the model’s explanation. The sequence below separates diagnosis from editing and creates an auditable path to a safe merge.
- Record the intended behavior. Write acceptance criteria, inputs, outputs, error behavior, security constraints, and the files that are in scope. If the request is ambiguous, resolve that ambiguity before changing code.
- Reproduce the defect. Run the smallest failing test or command you can create. Capture the input, observed output, expected output, environment, and exact error. A minimal reproducer prevents the assistant from hiding the original failure inside a larger rewrite.
- Inspect the complete diff. Review every changed line and every changed file, including files that appear unrelated. Classify changes as application logic, tests, dependencies, CI or build configuration, deployment configuration, or documentation. Unexpected categories are a reason to pause.
- Check the implementation against the requirements. Trace normal, error, null, boundary, timeout, retry, concurrency, and cleanup paths. Confirm that data crossing a trust boundary is authenticated, authorized, validated, encoded, and logged without exposing secrets.
- Verify dependencies before installation. Confirm that each package exists, is the intended package, has credible maintainers, and is not a typosquat. Check age and download history where appropriate, pin versions through your normal dependency-management process, and run an audit against a current advisory database. OWASP specifically warns not to blindly run
npm install,pip install, orgo getwith names suggested by an AI. - Apply automated checks. Run the project formatter, compiler or type checker, unit tests, integration tests, static analysis, dependency scanning, and secret scanning. Add fuzzing or a web-application scanner when the component and risk justify it.
- Add tests the generator did not write. Exercise malformed inputs, expired tokens, boundary values, concurrency, authorization failures, and other negative cases. Independent tests are important because a model can produce tests that merely ratify its own implementation.
- Review agent activity and permissions. Inspect network requests, shell commands, file reads, and tool calls. Confirm that approval prompts stayed enabled, the agent was sandboxed, credentials were short-lived and least-privilege, and tools were allowlisted.
- Obtain accountable approval. A responsible developer must understand the change and approve it. Merge only after security-sensitive, trust-boundary, CI/CD, dependency, and test changes receive the level of human review they require.
How do I fix a hallucinated or risky dependency?
Do not install a package merely because the generated code imports it. Treat each new dependency as a supply-chain decision and verify it independently.
1. Confirm identity
Search your package registry and the project’s official documentation for the exact name and supported versions. Check that the package’s purpose matches the import, rather than selecting a similarly named library. A one-character difference can lead to a typosquat.
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2. Evaluate maintenance signals
Review the project’s maintainers, release history, age, and download history where those signals are relevant. A package with little history or unclear ownership deserves a stronger justification—or replacement with an already approved internal or standard-library option.
3. Pin and audit through normal controls
Use your existing lockfile and version-management process rather than copying a version from the model’s answer. Run dependency auditing against current advisory databases and review transitive packages as well as the direct dependency. Re-run the audit whenever the generated patch changes the dependency graph.
4. Inspect install and build behavior
Read package scripts and build hooks before executing them. Pay particular attention to scripts that run during installation, compilation, testing, or deployment. Compare lockfiles and build configuration in the diff; an apparently small import can alter what runs in CI.
5. Remove the dependency when it is unnecessary
If the requested behavior can be implemented with code already present in the project, deleting the invented package is safer than trying to make it fit. Ask the assistant to explain why the dependency is required, then verify that explanation against the actual code and requirements.
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Review security by attack surface, not by how polished the generated code looks. OWASP’s guidance treats AI output as subject to the same code review, security scanning, and testing obligations as any other code.
Authentication and authorization
Verify identity checks, session handling, token expiry, role or tenant boundaries, and authorization on every sensitive operation. Test both an unauthenticated request and an authenticated user who lacks the required permission. Do not infer authorization from a user-controlled identifier or UI state.
Input, output, path, and URL handling
Test malformed, oversized, unexpected, and boundary inputs. Confirm that output is encoded for its destination and that file paths and URLs cannot escape the intended directory or reach an unintended host. Check redirects, archive extraction, upload names, and any user-controlled format strings.
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Deserialization and cryptography
Identify every deserialization boundary and ensure that untrusted data cannot select executable types or invoke dangerous constructors. Confirm that cryptography uses an approved implementation and sound key handling; do not accept a model’s algorithm choice without checking your organization’s standards.
Secrets and error handling
Search the diff, generated files, logs, and configuration for hardcoded keys, tokens, passwords, and connection strings. Errors should help operators diagnose failures without disclosing credentials, internal paths, stack traces, or sensitive records to an attacker.
Trust-boundary and deployment review
Give extra scrutiny to changes that cross process, network, tenant, privilege, or data-classification boundaries. Review CI workflows, container files, deployment manifests, and package scripts as production code; an unsafe command in a workflow can be as damaging as a vulnerability in the application.
How do I protect an AI coding agent from prompt injection?
Assume that repository files, issue text, pull-request comments, README files, logs, and fetched documentation may contain instructions designed to manipulate an agent. Their presence in the context does not make those instructions trustworthy.
- Keep approval prompts enabled. Require a person to approve shell commands, network access, file writes, and other consequential actions.
- Sandbox the agent. Limit filesystem and network reach so a mistaken instruction cannot reach unrelated repositories, production systems, or personal data.
- Use least-privilege, short-lived credentials. Give the agent only the permissions needed for the task and rotate or revoke them when the task ends.
- Allowlist tools and destinations. Restrict which commands, services, registries, and hosts the agent can use.
- Inspect activity, not just the final diff. Review network requests, shell history, file reads, and tool calls for unexpected behavior.
- Minimize context. Exclude
.envfiles, keys, credentials, proprietary data, and sensitive directories. Check what the assistant sends to its provider before pasting logs or source.
How do I detect out-of-scope edits?
Compare the requested scope with the complete patch, not just the files named in the assistant’s summary. Agents often make collateral changes while trying to satisfy a request.
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Classify every changed file
Group files into application code, tests, dependencies and lockfiles, CI workflows, Dockerfiles, package scripts, deployment manifests, generated artifacts, and documentation. For each group, ask why it changed and whether the acceptance criteria require it.
Stop and reset when scope drifts
If an active agent starts editing unrelated files or running unexpected tools, stop it. Preserve the original reproducer, revert or isolate the unrelated changes, and retry with an explicit file allowlist and a single acceptance criterion. A clean, smaller patch is easier to review than a broad patch that happens to pass one test.
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Pay special attention to configuration
Lockfiles, CI workflows, Dockerfiles, package scripts, and deployment manifests can change what runs with elevated privileges or what enters production. Review these files even when the application diff appears correct.
Can I trust tests generated by an AI?
AI-generated tests are useful for coverage ideas, but they are not independent evidence by themselves. A model can omit important cases, weaken assertions, mock away the real failure, or encode the implementation’s mistake as the expected result.
Look for weakened or misleading tests
- Deleted tests or reduced assertion counts.
- Assertions that only check a response exists instead of checking its content, authorization, or side effects.
- Mocks replacing the database, network, filesystem, or other dependency that contains the real risk.
- Expected values copied directly from the generated implementation.
- Tests that never exercise failures, malformed input, expiry, concurrency, or boundary values.
Add independent negative tests
Write cases from the requirements and threat model rather than from the patch. Include malformed inputs, expired tokens, boundary values, concurrent operations, and authorization failures. Where feasible, have a different person or a separate test-generation step create these cases without seeing the implementation details.
Test at multiple levels
Use unit tests to isolate logic, integration tests to verify real boundaries, and black-box or end-to-end tests to check externally visible behavior. NIST IR 8397 (2021) lists threat modeling, automated testing, static code scanning, hardcoded-secret checks, black-box tests, structural tests, historical tests, fuzzing, web-application scanners, and checks of included libraries, packages, and services among broadly applicable verification techniques.
What should a complete verification pipeline include?
No single check establishes that generated code is safe. Layer the checks so that a defect missed by one method is likely to be found by another.
| Verification layer | Primary purpose | Typical AI-generated-code failure it can expose |
|---|---|---|
| Formatter and compiler or type checker | Detect syntax, type, and structural errors | Invalid APIs, incompatible types, and incomplete edits |
| Unit and integration tests | Check intended behavior at isolated and real boundaries | Wrong logic, missing error paths, and integration assumptions |
| Static analysis | Find suspicious flows and unsafe coding patterns | Injection paths, unchecked values, and resource mistakes |
| Dependency and library checks | Verify included packages and known advisories | Hallucinated, typosquatted, obsolete, or vulnerable dependencies |
| Secret scanning | Detect credentials in source and configuration | Hardcoded keys, tokens, passwords, and connection strings |
| Fuzzing and black-box tests | Exercise unexpected inputs and externally visible behavior | Parser crashes, boundary failures, and validation bypasses |
| Web-application scanning | Probe deployed web behavior for common weaknesses | Authentication, authorization, encoding, and URL-handling flaws |
| Human security review | Evaluate intent, trust boundaries, and business impact | Subtle logic errors and unsafe design choices that tools miss |
The UK Home Office states that all AI-assisted changes must be tested in line with existing engineering standards before they are merged or deployed. That means an AI patch does not receive a lower bar because it was produced quickly.
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- The reviewer cannot state the intended behavior and acceptance criteria.
- The defect cannot be reproduced, or the patch changes the test instead of fixing the defect.
- A dependency’s identity, maintenance, licensing, or security status is unclear.
- The diff includes unexplained changes to CI, build, deployment, lockfiles, scripts, or tests.
- Security-sensitive or trust-boundary code lacks negative and authorization tests.
- Secrets or sensitive context may have been exposed to the assistant.
- The agent made unapproved network or shell calls, or used credentials beyond its task.
- The responsible developer cannot explain the code and its failure modes.
Record who reviewed the change and what was checked. Merge only after that person understands and approves the implementation; accountability cannot be delegated to the model.
How should I ask an AI assistant to repair a failing patch?
Give the assistant evidence and constraints, not a vague request to make the code work. A useful repair prompt identifies the smallest reproducer, expected behavior, observed failure, allowed files, forbidden actions, and required tests.
- State the acceptance criteria in observable terms.
- Paste the minimal error and reproducer, omitting secrets and proprietary data.
- List the files the assistant may change and explicitly prohibit unrelated edits.
- Ask for a diagnosis before a patch, including assumptions and alternative causes.
- Require a minimal diff and tests for normal, negative, boundary, and authorization cases where relevant.
- Ask the assistant to list dependencies, commands, network access, and files it intends to touch before execution.
- Review the result yourself; never treat the assistant’s summary as a substitute for the diff.
If the output is irrelevant or the agent’s behavior drifts, stop the active run, inspect its actions, and retry with a clearer prompt or another model. The retry is a new proposal, not evidence that the first patch was safe.
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Use a staged path that keeps generated work reversible and limits its blast radius.
- Keep the patch in a separate branch or review change until all checks pass.
- Run the full project-standard verification suite plus targeted security and dependency checks.
- Review the complete diff and the agent’s activity log, including commands and network actions.
- Have an appropriate human reviewer examine security-sensitive, trust-boundary, CI/CD, dependency, and test changes.
- Deploy with the least privilege and smallest exposure practical, then monitor errors, authorization failures, and unusual network activity.
- Record the responsible developer, the acceptance criteria, the tests run, and the approval decision.
This workflow makes generated code accountable to the same engineering standards as any other change while preserving the speed benefits of an assistant.
Frequently Asked Questions
Should I delete all AI-generated code after a failed review?
Not necessarily. Keep a failing patch only if its history and scope are clear, then isolate the smallest useful portion and re-review it from the original requirements. If provenance, credentials, or unrelated changes are mixed into the patch, discard it and recreate the change from a clean branch.
Who is responsible when an AI-generated change causes an incident?
The responsible developer and approving reviewers remain accountable; the assistant is not an approver or owner. Your change record should identify who understood the implementation, what checks ran, and who authorized the merge or deployment.
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How can I tell whether an AI repair fixed the cause or only the symptom?
Re-run the original minimal reproducer, then add a test that would have failed under the old behavior and tests nearby error and boundary paths. Inspect the diff for changes that suppress errors, loosen assertions, or bypass validation instead of correcting the underlying state transition.
What should I do if an assistant may have seen a secret?
Stop using the exposed credential, revoke or rotate it, and review provider logs and access records according to your incident process. Remove the secret from prompts, generated files, logs, and caches, then determine whether proprietary data or other credentials were included in the same context.
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