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AI coding tools can produce useful code, but plausible output is not proof of secure code. Reduce the risk by reviewing every change, verifying dependencies, running security checks, protecting sensitive context, restricting agent permissions, and keeping a human accountable for each accepted change.
Why AI-assisted code needs extra scrutiny
A coding assistant can suggest code that looks correct while introducing vulnerabilities, outdated dependencies, or changes you do not intend. Risk also comes from the surrounding workflow: what information the tool can read, what permissions an agent has, and whether generated changes are reviewed before they run or merge.
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OWASP’s Top 10:2025 calls inappropriate trust in AI-generated code a risk. Its guidance says: “You should be able to read and fully understand all code you submit, even if it is written by an AI or copied from an online forum.” The practical standard is not that every line must be written by a person; it is that a responsible developer can explain and approve what is submitted.
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Use a review workflow from prompt to merge
1. Check what the tool can see before prompting
Identify whether the task involves credentials, customer data, proprietary code, or other sensitive material. Check the coding tool’s current documentation for what repository context or prompt content it sends to its provider, how that context is handled, and what exclusion controls are available. Behavior varies by product and settings, so do not assume a particular privacy policy applies to every assistant.
#1 Best Overall
- Keep API keys, passwords, tokens, and private certificates out of source files and other project material the tool can read.
- Use available context exclusions for sensitive files or directories.
- Share only the code and information needed for the task.
2. Read the complete change
Inspect the diff rather than accepting a suggestion because it compiles or appears idiomatic. Trace what the code does, what data it handles, and how it behaves on invalid or hostile input. If you cannot explain a change, do not approve or merge it until you understand it.
Give particular attention to changes involving authentication, authorization, input validation, cryptography, build scripts, CI/CD configuration, and deployment. Small edits in these areas can affect security beyond the visible feature.
3. Verify every dependency independently
A model can name a package that does not exist, suggest a misleading look-alike, or choose an outdated version with known vulnerabilities. Before installing a suggested dependency, confirm its name and version in the relevant package registry, inspect its provenance and maintenance signals, and check vulnerability information. Run your normal dependency audit and review the results before merging.
Do not treat a package name in generated code as evidence that the package is legitimate or appropriate. Apply the same checks to transitive dependencies and version changes introduced by generated lockfile edits.
Rank #3
4. Run tests and security checks, then review their limits
Run the project’s ordinary test suite and CI checks, along with dependency vulnerability checks and any security analysis used by your team. Examine failures and warnings rather than assuming a green result establishes safety.
Tests show whether tested behaviors meet the assertions in those tests; they do not prove the absence of vulnerabilities. This limitation matters especially when the same model generated both implementation and tests: the tests may share the implementation’s mistaken assumptions. Add independent review of security-sensitive paths instead of using a passing test count as a security verdict.
Rank #4
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5. Keep an accountable human owner
Assign a developer to understand, review, and approve each accepted change. The reviewer remains responsible for the change even when an assistant or agent produced it. Where a change has significant security impact, use your normal peer-review and approval requirements rather than treating AI output as an exception.
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Agents may read repository files, issues, pull-request comments, or external pages and may have tools to run commands or access services. Treat that text as untrusted input: it can contain instructions that conflict with the task or attempt to influence the agent. A source file or issue comment should not gain authority merely because the agent encountered it.
- Limit permissions: Give an agent only the repository access and capabilities required for its task. Avoid broad credentials and unnecessary network or system access.
- Isolate execution where practical: Use an environment that limits the impact of an unintended command or unsafe change.
- Require approval for sensitive actions: Keep a person in the loop for operations such as publishing, deploying, changing access controls, or handling production credentials.
- Review actions as well as code: Check commands run, files changed, dependency additions, and external resources accessed before accepting the result.
How OWASP and NIST guidance fit
OWASP’s Secure Coding with AI Cheat Sheet addresses developers using AI coding tools, including dependency verification, untrusted instructions, sensitive context, agent permissions, tests, and human review.
NIST’s SP 800-218A, published in July 2024, adds AI-specific practices to the Secure Software Development Framework for generative AI and dual-use foundation model development. It is intended to be used with SP 800-218; it is not a consumer checklist for every coding assistant. Together, these sources support applying established secure-development controls while accounting for AI-specific inputs and workflow risks.
Quick Recap
A practical pre-merge checklist
- I know what project context the tool can access and have excluded sensitive material where possible.
- I understand the full diff and can explain its security-relevant behavior.
- Every suggested dependency and version has been verified independently.
- Tests, dependency audits, and relevant CI security checks have run and their results have been reviewed.
- Authentication, authorization, validation, cryptography, build, CI/CD, and deployment changes received focused scrutiny where applicable.
- Agent permissions were limited, and sensitive actions or credentials remain under human control.
- A named human reviewer has approved the change and remains accountable for it.
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