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Review AI-written code as if it came from an unknown contributor: understand the complete diff, trace data across trust boundaries, verify authorization and dependencies, test abuse cases, and run security automation. A green test suite or clean scanner is evidence to investigate—not proof that the change is secure. Security-sensitive changes need approval from a qualified human who can explain the code and accept documented residual risk.
Start with an untrusted-code assumption
Generative coding tools can produce useful implementations quickly, but they do not reliably understand your threat model, tenancy rules, deployment environment, or business invariants. They may invent packages, copy obsolete APIs, omit authorization, weaken tests, or follow instructions hidden in repository content. Treat every generated line as untrusted until a reviewer establishes what it does and why it is safe.
Ownership cannot be delegated to the model or the person who pressed “generate.” The engineer submitting the change remains responsible for its behavior, and a qualified reviewer must be able to describe the security properties that the code is supposed to preserve. This is the principle expressed by the OWASP Secure Coding with AI Cheat Sheet: “AI-generated code must have a human owner.”
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First determine the blast radius. A small-looking patch can be high risk if it changes a shared middleware, an infrastructure module, or an instruction file that affects future agent behavior. Record the files and execution paths involved, then route sensitive work to a security champion or reviewer with appropriate training.
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| Change area | Questions to ask | Minimum review posture |
|---|---|---|
| Authentication and sessions | Does the patch create, validate, refresh, revoke, or store credentials, tokens, cookies, or session state? | Manual security review; inspect expiry, rotation, fixation, logout, and failure behavior. |
| Authorization and tenant isolation | Can a caller access another user’s object, account, tenant, or administrative function? | Trace every route and background job; require a reviewer who understands the access model. |
| Input parsing and output rendering | Are request values, headers, files, serialized data, templates, or shell arguments processed? | Perform source-to-sink tracing and adversarial tests for injection, traversal, and encoding failures. |
| Cryptography and sensitive data | Are keys, passwords, personal data, encryption settings, or signing operations involved? | Use approved primitives and storage patterns; obtain security-team sign-off for design changes. |
| File upload, deserialization, or outbound requests | Can attacker-controlled content become a file path, object, URL, or executable structure? | Review limits, canonicalization, allowlists, sandboxing, and SSRF defenses. |
| Dependencies and third-party integrations | Does the patch add a package, SDK, action, container, webhook, or external service? | Verify identity and provenance, constrain versions, and check current vulnerability data. |
| CI/CD, containers, or infrastructure | Could the change alter IAM, network exposure, secrets, images, buckets, logging, or deployment permissions? | Review as production code; require infrastructure and pipeline checks before merge. |
| Agent instruction files | Does it modify AGENTS.md, rules files, hooks, workflows, prompts, or other files that guide an agent? | Inspect for behavior changes and prompt-injection content; use two-person review for consequential edits. |
If a patch crosses several rows, use the strictest applicable threshold. Do not let a low line count or an apparently routine refactor downgrade the review.
Read the complete diff and reconstruct the design
Read the whole diff, not only the lines generated by the assistant. Include deleted code, renamed files, configuration changes, migrations, tests, workflow files, and dependency manifests. Then read the surrounding callers, middleware, data models, and error-handling paths. A vulnerability often appears at the boundary between an unchanged component and a newly generated one.
Confirm the intended behavior
Write down what the author says the change does and compare it with what the code actually does. Identify new endpoints, background jobs, permissions, data stores, outbound calls, and failure paths. Ask the owner to explain each security-relevant branch without relying on the model’s explanation. If the owner cannot explain a function’s inputs, outputs, invariants, and failure behavior, stop approval and simplify or rewrite it.
Draw trust boundaries
Mark where data moves between an untrusted client and the application, between tenants, from the application to a database, from a worker to a queue, from a service to a third-party API, and from a build job to a deployment environment. Note where identity is established and where it is assumed. A generated helper that moves a check across one of these boundaries can silently change the security model.
Compare behavior before and after
For each changed boundary, ask whether confidentiality, integrity, availability, and accountability are preserved. Check default values, error responses, timeouts, retries, transaction scope, logging, and metrics. Pay particular attention to “cleanup” edits that remove validation, broaden a query, make a field optional, disable a check for tests, or return detailed errors to clients.
Trace every untrusted input to every dangerous sink
Follow values through the program rather than checking isolated snippets. Start with request parameters, headers, cookies, uploaded files, serialized payloads, environment variables, webhook bodies, queue messages, model output, and tool output. Track transformations, validation, authorization, storage, and encoding until the value reaches a sink.
Injection and interpreter boundaries
Look for values concatenated into SQL, NoSQL, LDAP, shell commands, regular expressions, templates, HTML, JavaScript, XML, or operating-system arguments. Prefer parameterized queries, structured APIs, fixed command arguments, and context-specific output encoding. Validation should constrain the value for its intended use; escaping alone is not a substitute for authorization or a safe API.
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URLs, network access, and SSRF
When code accepts a URL or follows a redirect, determine whether an attacker can reach internal addresses, metadata services, localhost ports, alternate IP representations, or unexpected protocols. Validate the scheme and destination, resolve and re-check addresses, restrict egress where possible, and avoid passing user-controlled URLs to privileged network clients. Review redirects and DNS-rebinding assumptions rather than checking only the first URL.
Files and paths
For uploads and file operations, inspect filename handling, path joins, archive extraction, symbolic links, size and decompression limits, content-type checks, storage permissions, and download authorization. Canonicalize paths before enforcing containment, use a generated server-side name, and keep uploaded content outside executable or public directories unless a deliberate design requires otherwise.
Deserialization and object construction
Identify formats that can instantiate classes, invoke callbacks, resolve types, or evaluate expressions. Prefer data-only formats with explicit schemas and bounded sizes. Reject unknown fields when appropriate, enforce depth and resource limits, and never deserialize attacker-controlled data with a runtime mechanism that permits code execution.
Model and tool output
Output from a model, plugin, browser, or command-line tool is data, not a trusted instruction. Validate it before using it as a query, path, command, configuration value, or permission decision. Keep the model outside authorization decisions and require explicit policy checks before an automated agent can perform consequential actions.
Verify authentication, sessions, and authorization independently
Authentication proves who a principal is; authorization decides what that principal may do. Review both, including code paths that do not look like public web routes.
Authentication and session checks
- Confirm credentials and tokens are validated with the intended issuer, audience, signature algorithm, key, and lifetime.
- Check expiry, clock-skew handling, refresh and revocation behavior, logout, rotation, and session fixation defenses.
- Ensure cookies use appropriate Secure, HttpOnly, SameSite, domain, path, and lifetime settings for the deployment.
- Make failures uniform enough to avoid account enumeration, while preserving useful internal audit records.
Object and function authorization
For every route, job, message consumer, and administrative operation, identify the subject, object, action, tenant, and required policy. Verify the check occurs after the subject is authenticated and before data is read or changed. Test direct object references with another user’s identifier, missing identifiers, stale roles, suspended accounts, and cross-tenant requests. Do not assume that a front-end restriction or an earlier middleware check covers a new background path.
Fail closed and preserve policy consistency
Missing, malformed, or unavailable policy data should deny the operation rather than grant it. Ensure exceptions, retries, cache misses, and asynchronous processing do not bypass the same decision. Compare generated policy helpers with the canonical policy implementation so that a second, subtly different authorization rule is not introduced.
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Validate dependencies and the software supply chain
AI suggestions can contain a real package with a similar name, a nonexistent package, an abandoned project, or an obsolete API. Before installation, verify the exact package identity, registry or repository provenance, maintainer history, license policy, release version, and transitive dependencies. Check current vulnerability databases and your organization’s allowlist.
Prefer versions constrained by a lockfile and update policy. Review install scripts, native extensions, post-install network access, and the permissions granted to build actions. Pin CI actions and container images to approved immutable references where policy requires it. A scanner finding is not the only concern: a package can be malicious or inappropriate even when no CVE has been published.
Do not merge a dependency merely because generated code imports it successfully. Build in a clean environment, confirm the resolved graph matches the manifest, and remove unused packages. If a package cannot be identified or justified, replace the implementation or obtain an explicit exception from the owner of the supply-chain policy.
Inspect secrets, personal data, and AI context
Search the diff and generated files for API keys, passwords, private keys, signing material, connection strings, tokens, test credentials, and personal data. Check source, configuration, fixtures, logs, comments, notebooks, and workflow variables—not just obvious secret files. If a secret may have been committed, revoke or rotate it and investigate repository and build logs; deleting the line is not remediation.
Review what the coding service received. Open files, terminal output, stack traces, issue text, and copied production data may contain credentials, personal information, proprietary algorithms, or architecture details. Use provider controls that limit retention and training where available, exclude sensitive directories, redact logs, and prohibit production secrets in prompts. Make this review part of the change process rather than relying on a developer’s memory.
Review tests as potentially generated behavior
A green suite can be misleading when the assistant deleted tests, weakened assertions, mocked away the security boundary, or encoded the buggy behavior it generated. Compare test changes with the old suite and look for reduced coverage, broad snapshots, assertions that only check status codes, and mocks that bypass authorization, validation, encryption, or network policy.
Add negative and adversarial cases
- Malformed, oversized, unexpected, and boundary-value inputs.
- Expired, revoked, altered, missing, and wrong-audience tokens.
- Another user’s object identifier and another tenant’s resource.
- Privilege changes during a session and concurrent updates.
- Path traversal, alternate encodings, dangerous redirects, and unsupported protocols.
- Failure of a policy service, dependency, key store, queue, or network call.
- Retries, duplicate messages, partial transactions, and race conditions.
Keep at least some tests at the real security boundary. A unit test with a mocked authorization function can verify plumbing, but it cannot establish that the actual policy is enforced in production. Pair automated tests with a manual abuse-case review for business logic that has no reliable oracle.
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Examine CI/CD, infrastructure, and instruction files
Generated changes often touch files outside the application. Inspect infrastructure and pipeline diffs for public buckets, exposed security groups, widened IAM permissions, disabled encryption or logging, unpinned actions and images, secrets in variables, and pull-request triggers that execute untrusted code with privileged tokens.
Check whether a workflow checks out attacker-controlled branches, runs scripts from the pull request, or uploads artifacts that later receive elevated trust. Separate untrusted validation jobs from signing, publishing, deployment, and secret-bearing jobs. Use least-privilege tokens, protected environments, explicit approval gates, and immutable build inputs.
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Run layered automated security gates
Automation removes repetitive work and creates a consistent baseline, but each tool answers a narrower question. Run the appropriate gates on every AI-generated pull request and define in advance which findings block merging.
| Gate | What it can detect | What still needs human judgment |
|---|---|---|
| Static application security testing (SAST) | Known insecure coding patterns, data-flow paths, and some injection or cryptographic misuse. | Business-logic bypasses, missing authorization in context, incorrect trust boundaries, and acceptable risk. |
| Software composition analysis (SCA) | Known vulnerabilities, licenses, and dependency relationships. | Package legitimacy, malicious intent, exploitability in your deployment, and whether the dependency is needed. |
| Secret scanning | Recognizable credentials, keys, and token formats in tracked content and sometimes history. | Unusual secrets, leaked context outside scanned locations, revocation, and downstream exposure. |
| Infrastructure-as-code scanning | Misconfigured networks, storage, IAM, encryption, containers, and cloud resources. | Architecture-specific exposure, compensating controls, and runtime drift. |
| Dynamic or interactive testing (DAST/IAST) | Runtime behavior, reachable endpoints, and some injection or configuration failures. | Unexercised workflows, authorization matrices, race conditions, and business abuse cases. |
| Custom rules and architectural checks | Organization-specific APIs, forbidden dependencies, boundary violations, and required controls. | Whether the rule models the real policy and whether an exception is justified. |
Configure critical findings to block a merge unless an authorized human records a written exception with scope, rationale, owner, expiry or review date, and compensating controls. Tune false positives, but do not silently suppress a rule because generated code triggers it.
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OWASP summarizes the boundary clearly: “Automated scanners find known patterns; human review finds the flaws that need context — a missing authorization check, a business-logic bypass, a trust boundary drawn in the wrong place.” A clean report means that the configured tools did not identify their covered patterns under their test conditions. It does not prove that an endpoint enforces the right policy, that a workflow preserves tenant isolation, or that an attacker cannot combine individually harmless operations.
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Spend human review time on architecture, authorization, business rules, error handling, cryptographic choices, data protection, logging, and abuse cases. Use scanner output to prioritize and verify repetitive details, then investigate every high-severity result and any unusual change in coverage.
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Handle the common AI-specific failure modes
Hallucinated or stale packages
Search the authoritative registry or project source, verify ownership and version, inspect release activity, and check vulnerabilities before adding the package. Replace unverifiable imports instead of creating a private workaround with unknown maintenance risk.
Indirect prompt injection
Assume repository text and external content may contain instructions aimed at the agent. Limit tool permissions, isolate credentials, require confirmation for destructive operations, and review the complete diff for edits unrelated to the requested task.
Fabricated, deleted, or weakened tests
Compare test counts and assertions, run the suite from a clean checkout, and add tests that fail for the vulnerable behavior. Treat a newly green test as untrusted until its setup reaches the real boundary.
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Context leakage
Inspect assistant settings, retention choices, exclusions, and telemetry. Redact production data and secrets before a prompt or log is sent, and document approved use for proprietary code.
Instruction-file changes
Require a separate rationale for changes that alter future agent behavior. Review them with the same care as build scripts because a permissive rule can affect every later contribution.
Use standards to set a repeatable baseline
NIST published SP 800-218A on July 26, 2024. It augments Secure Software Development Framework (SSDF) version 1.1 with practices specific to generative AI and dual-use foundation models. Use the profile alongside normal SSDF practices, not as a replacement for them.
OWASP AISVS Appendix C recommends qualified human review of AI-generated code, SAST, IAST, DAST, secret scanning, infrastructure-as-code scanning, and SCA on every AI-generated pull request. It also recommends blocking critical findings unless an authorized human approves a written exception. OWASP SAMM supports deterministic guardrails such as custom static-analysis rules, architectural-boundary checks, and pipeline gates, followed by recurring review of authentication, authorization, secure communication, data protection, and logging.
Translate those recommendations into repository policy: define which files require a security champion, which findings block, who can approve exceptions, what evidence an exception contains, and how long it remains valid. Store the reviewer, tool or model used when available, findings, remediation, test evidence, and exception decisions with the pull request.
Make the merge decision explicit
Do not merge when the owner cannot explain security-relevant code, an authorization path is uncertain, a dependency is unverifiable, a secret is exposed, a critical automated finding lacks an approved exception, or adversarial tests are missing for a high-risk boundary. Return the patch for simplification or rewrite when generated abstractions obscure data flow or policy.
Approval is reasonable only after the risk has been triaged, the full diff and trust boundaries are understood, untrusted inputs have been traced to sinks, identity and authorization have been tested, dependencies and secrets have been checked, CI/CD and instruction files have been inspected, required automated gates have passed or received documented exceptions, and a qualified human accepts the remaining risk. For security-critical files, use two-person review or security-team sign-off rather than relying on a single green pipeline.
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