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Fix AI-generated code with the same secure-development process you use for any code: inspect the complete change, identify unsafe trust boundaries, repair the underlying operation, then verify the fix with security tools and adversarial tests. AI authorship does not make code inherently unsafe or safe; the developer who accepts the change must understand it, approve it, and remain accountable for it.
Start by looking for familiar problems: SQL built by concatenating input, unnecessary dynamic execution such as eval(), missing authorization checks, hardcoded credentials, weak cryptography, and unverified dependencies. The right repair depends on the language, framework, and application context, so do not merge a suggested fix merely because it compiles or its tests pass.
What security issues should I look for in AI-generated code?
Look for flaws where untrusted data, user identity, secrets, dependencies, or agent permissions cross a security boundary. OWASP identifies SQL assembled through string concatenation, dynamic eval(), weak or deprecated cryptography, missing authorization checks, and hardcoded credentials as examples of insecure patterns that can appear in generated code.
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| Area to inspect | Warning sign | Safer direction |
|---|---|---|
| Database access | SQL text is assembled with concatenation or interpolation that includes untrusted values. | Use parameterized queries or the framework’s equivalent so values are treated as data, not SQL instructions. |
| Dynamic execution | Input or constructed text is passed to eval() or a similar execution mechanism without a compelling need. |
Replace dynamic execution with an explicit operation or narrowly defined set of allowed actions. |
| Authorization | A route, function, or data operation checks that a user is signed in but not that the user may perform that specific action. | Apply the application’s established authorization rules at the relevant operation and data boundary. |
| Secrets | A password, token, private key, or other credential appears directly in source, configuration, a test, or a script. | Remove exposed credentials and use the project’s approved secret-management approach. Treat a credential committed to a repository as exposed, not merely hidden by deleting the line. |
| Cryptography | The change introduces a weak or deprecated algorithm, mode, or hand-built cryptographic logic. | Use the project’s approved cryptographic choices and established libraries; have a qualified reviewer assess security-sensitive changes. |
| Dependencies | A new package is unfamiliar, nonexistent, misidentified, outdated, or introduced without a clear need. | Verify the exact package identity and its history, audit versions for known vulnerabilities, and follow normal dependency policy. |
| Input handling | Malformed, unexpected, or boundary-case input is accepted, passed into a sensitive operation, or handled inconsistently. | Validate against the expected data format and reject invalid input explicitly; test the rejection path. |
The warning signs are starting points, not a complete security checklist. A query that looks safe in isolation can still be reachable through an unauthorized path, and input validation does not replace parameterized database operations. Trace how the value or request arrives, what checks happen before it is used, and what authority the operation exercises.
How should I review an AI-generated change before fixing it?
Review the complete diff before editing or approving it. A generated summary can omit important changes, so inspect the implementation alongside dependencies, lockfiles, tests, configuration, build and deployment files, package scripts, and CI changes.
- Confirm scope. Compare the diff with the requested task. Identify unexpected files, new dependencies, changed permissions, removed checks, or unrelated edits. OWASP advises against approving generated changes on the basis of a summary alone.
- Map the change’s security context. Ask what data enters the changed code, which users or services can invoke it, what permissions it has, and whether it reaches a database, shell, network service, file system, or other sensitive resource.
- Trace trust boundaries. Follow untrusted values into SQL, shell commands, template rendering, deserialization, dynamic evaluation, and other sensitive operations. Check where identity and authorization are enforced, rather than assuming a nearby login check is sufficient.
- Inspect supporting changes. Review lockfiles, package lifecycle scripts, build commands, CI configuration, deployment settings, agent instruction files, and security-sensitive tests. A small application-code change can have a larger effect if it changes what runs during installation or deployment.
- Record what remains uncertain. If you cannot explain what a changed operation does, what authority it uses, or why a dependency is needed, do not treat the change as ready to merge. Ask for clarification, reduce the change, or get the appropriate specialist review.
Review the final state, not just the initial patch: repairs, generated follow-up edits, and conflict resolution can introduce new differences. The person approving the change should understand the complete version that will be merged.
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How do I fix SQL injection and unsafe dynamic execution?
Repair the operation that turns data into instructions. For SQL, bind values as parameters instead of inserting untrusted input into a query string. For dynamic execution, remove eval() when an explicit, safer operation can accomplish the same task.
Use parameters for database values
Inspect every query affected by the change and identify which parts are fixed query structure and which parts come from input. Pass input values through the database driver’s parameter-binding mechanism or the framework’s equivalent. Do not assume that escaping a string manually is an equivalent substitute: escaping rules can depend on the database, context, and driver.
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Some applications need to choose a table, column, or sort direction dynamically. Those are query-structure choices, not ordinary bound values. Use a small, explicit allowlist of permitted choices and select from it; do not let raw user input become SQL syntax. Confirm the exact approach against the database library and framework used by the project.
Replace unnecessary dynamic evaluation
Find out why the generated code evaluates a string or dynamically selects executable behavior. If the task is to perform one of several known actions, represent those actions explicitly and allow only the intended choices. If dynamic behavior is genuinely required, constrain and validate it according to the application’s threat model instead of treating arbitrary input as trustworthy.
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How do I check authorization, secrets, and cryptography?
Use the application’s existing security conventions rather than inventing a parallel login, permission, or cryptography system. The repair should be made at the point where the protected action or data is accessed, with checks appropriate to the application’s roles and threat model.
Authorization: check permission for the action
For each changed route or operation, identify the actor and the resource they are trying to access. Verify that the code checks permission for that action and resource, not merely that the request has an authenticated user. Follow the same authorization library and policy patterns used elsewhere in the project; a check placed only in the interface does not protect a server-side operation.
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Add tests for both permitted and forbidden cases, including attempts to access another user’s data where that is relevant. A test that covers only a successful request cannot establish that the new operation rejects unauthorized access.
Secrets: remove and handle exposure
Search the entire change, including tests, scripts, configuration, and logs, for credentials or secret material. Remove any hardcoded credential and use the project’s approved method for supplying secrets at runtime. If a real credential was committed or exposed, follow the organization’s incident and rotation process; deleting it from the latest diff alone does not undo exposure in repository history or other copies.
Cryptography: follow approved choices
Do not accept a new algorithm or cryptographic implementation just because it has a familiar name or appears to work. Compare it with the project’s approved algorithms, modes, and established libraries, and seek security review when the code creates, stores, verifies, or transports sensitive material. The specific safe choice depends on the purpose and the application; the evidence here does not establish one universal algorithm for every use.
How should I verify AI-suggested packages and versions?
Verify a package before adding or installing it: confirm that the name identifies the intended project, review its provenance and maintenance history, and assess whether the dependency is actually needed. OWASP cautions that models may suggest outdated versions or package names that do not exist, making a plausible-looking import insufficient evidence that a package is trustworthy.
- Check the exact package name and ecosystem registry; look for lookalike names and verify that the project matches the intended library.
- Review its provenance and maintenance history under your organization’s normal dependency-review policy.
- Inspect the selected version and lockfile changes. Confirm that the change did not silently introduce a different or broader dependency tree than expected.
- Run the ecosystem’s dependency audit and address or document findings through the project’s normal process.
- Make the dependency check part of CI so later changes are checked under the same policy.
OWASP gives these examples of language-specific audit tools: npm audit for JavaScript packages, pip audit for Python, govulncheck for Go, and cargo audit for Rust. Use the tool that applies to the project, and check its current documentation and your team’s policy for supported versions and invocation details. An audit can identify known vulnerabilities; it does not prove that a package is authentic, appropriate, or free of unknown flaws.
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Which security checks and tests should I run?
Run the same static analysis, dependency scanning, and secret scanning gates you use for other code, then add tests that challenge the changed security boundary. A passing build or test suite is useful evidence about behavior, but it is not proof that the code is secure.
- Static analysis: inspect relevant findings in context and fix or document them through the normal process.
- Dependency scanning: check new and changed package versions for known vulnerabilities under the project’s policy.
- Secret scanning: check source, configuration, tests, and scripts for credentials or other secret material.
- Adversarial tests: exercise invalid inputs, boundary cases, expired tokens, malformed payloads, and concurrency where the feature makes those cases relevant.
- Authorization tests: verify that allowed operations succeed and forbidden users or requests are rejected.
- Independent review: have a human inspect the security-sensitive change and the resulting tests, rather than relying only on code or tests generated by the same assistant.
Generated test changes deserve particular scrutiny. Look for deleted cases, weakened assertions, mocks that bypass the behavior under test, or changed expectations that simply make the suite pass. Check that tests exercise the real rejection or authorization path and that the assertions would fail if the security check were removed.
For tool selection, compare coverage rather than relying on a product name: does a tool inspect source code, dependencies, secrets, or infrastructure; does it support the project’s languages and frameworks; does it run in the editor, pull request, or CI; can it enforce team policy; and how much human effort is needed to verify its findings? OWASP supports using security tools and CI controls, but the cited guidance does not establish a best vendor or a product bake-off.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I secure an AI agent that can edit or run code?
If an assistant can run commands, access the network, edit files, or use credentials, limit those capabilities to what the task needs. Consider sandboxing execution where appropriate, keep sensitive context out of reach unless necessary, and inspect the effects of untrusted content that the agent encounters.
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Repository issues, web pages, dependency documentation, and other external material should be treated as untrusted input, not as authority to change the task or disclose secrets. Review the resulting repository for unexpected edits, new commands, credential exposure, or modifications to agent instructions and security-sensitive tests. The more authority an agent receives, the more important it is to constrain the environment and examine its effects.
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Apply this review to configuration as well as application code. Build and deployment files, CI workflows, package lifecycle scripts, and agent rules can change what runs, what resources are available, or what data is exposed. A successful application test does not validate those separate effects.
Who is responsible for approving AI-generated code?
A human developer who understands the final change must approve it and own its security and maintenance. The UK Home Office engineering standard states: “All AI-assisted code must be subject to the same security standards, reviews, and controls as code written without AI assistance.” That standard makes the practical rule clear: AI assistance does not lower the bar for review or transfer responsibility away from the person accepting the change.
NIST Special Publication 800-218A, published in July 2024, augments version 1.1 of the Secure Software Development Framework (SSDF) with practices specific to AI model development across the software development life cycle. It is useful context for organizations developing AI models and systems; it is not a complete, vulnerability-by-vulnerability repair manual for ordinary application code. Apply the normal secure-development controls to application changes and use AI-specific guidance where it matches the system being built.
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Frequently Asked Questions
Is AI-generated code more vulnerable than human-written code?
The cited OWASP, UK Home Office, and NIST guidance does not establish a comparative vulnerability rate. Avoid treating AI authorship alone as a risk score; assess the actual code, its context, and the controls applied to it.
What should I do if an AI suggests a package I cannot verify?
Do not install it just because the name appears plausible. Verify its identity and provenance first; if you cannot establish that it is the intended package, leave it out and follow your team’s normal process for finding an approved dependency.
Does NIST SP 800-218A tell me how to repair every flaw in an AI-written app?
No. Published in July 2024, it adds AI-model-development practices to SSDF version 1.1 across the software development life cycle. It is not a language-specific repair guide for every vulnerability in ordinary application code.
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