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AI coding tools can produce code that looks convincing but contains security flaws—and the surrounding workflow can introduce risks of its own. Reduce those risks by reviewing every change, checking dependencies independently, protecting sensitive context, limiting agent permissions, and keeping a human developer accountable. Passing tests or a clean scan is useful evidence, not proof that code is secure.
Why AI-assisted coding needs security review
A coding assistant can suggest insecure code, recommend a package or version that should not be trusted, or act on misleading instructions found in project material. The risk is not limited to the lines it generates: what the tool can read, where it sends context, and what actions an agent can take are also part of the security picture.
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OWASP’s Secure Coding with AI Cheat Sheet describes these developer-use risks. OWASP’s Top 10:2025 guidance also identifies inappropriate trust in AI-generated code as a risk. It 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.”
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThis does not mean every AI-generated change is insecure. It means the same security standards apply, with additional care for how suggestions are produced, reviewed, and executed.
#1 Best Overall
Before prompting, control what the tool can see
Consider what repository content the assistant may receive as context and what the tool’s documentation says about that context. The details vary by product and configuration, so check the current documentation for the specific tool rather than assuming how it handles code.
- Identify sensitive source files, customer or production data, credentials, and other material that should not be included in prompts or context.
- Use documented context exclusions where available, and check that they cover the files and data you mean to protect.
- Keep credentials out of project files the tool can read. Use your established secret-management process instead of placing secrets in code or configuration accessible to the assistant.
Review generated changes before accepting them
Read the complete diff rather than relying on a summary or accepting a broad patch at once. Work out what each change does, how it affects existing behavior, and whether it introduces a new trust boundary or changes security-sensitive logic.
Give particular scrutiny to changes involving:
- Authentication, authorization, and account or role checks
- Input validation, output encoding, and data handling
- Cryptographic operations and security configuration
- Build scripts, CI/CD workflows, deployment, and other commands that can affect your environment
If you cannot explain a change well enough to assess its consequences, do not submit it until you understand it or have obtained a qualified review. Keep the developer who accepts the change responsible for it; a tool’s recommendation does not transfer that responsibility.
Verify packages and versions independently
Do not install a dependency simply because an assistant named it. A suggestion may point to a package that does not exist, an unintended package with a similar name, or a version with known vulnerabilities. Check the package in its legitimate registry, confirm its identity and provenance, and verify the version before adding it.
Rank #3
Run the dependency and vulnerability checks used in your normal development process, including CI checks where available. Treat their results as one input to review: an audit can identify known issues, but it cannot establish that a dependency is trustworthy or that your application uses it safely.
Test the result, but do not treat passing tests as proof
Run the project’s normal tests and security checks before merging. Tests help catch regressions and verify expected behavior, but they do not prove that code is secure. This limitation matters especially when the same model produced both the implementation and its tests: the tests may repeat the implementation’s assumptions or miss the same flaw.
Rank #4
- Used Book in Good Condition
Review security-sensitive behavior independently, and use the relevant static analysis, dependency auditing, and CI checks as supporting controls. A passing test suite or scanner does not replace a human review of what changed and what could go wrong.
Constrain coding agents and treat their inputs as untrusted
Agentic tools may read repository files, issues, pull-request comments, or external pages, and may be able to run commands or access services. Text in those sources can contain instructions that should not be treated as authoritative. A broad grant of permissions can magnify the impact of a misleading instruction or unsafe suggestion.
- Give an agent only the repository access, credentials, and permissions needed for its assigned task.
- Where possible, isolate execution and restrict unnecessary network or system access.
- Require human approval before sensitive actions, such as changing access controls, handling secrets, modifying deployment workflows, or performing consequential operations.
- Inspect the resulting diff and any actions the agent took before accepting its work.
Use secure-development guidance at the right level
For developers using AI coding tools, OWASP’s cheat sheet offers practical guidance on context, dependencies, agents, testing, and review. At the organizational process level, NIST’s SP 800-218A, published in July 2024, adds practices for AI and dual-use foundation model development to the Secure Software Development Framework. NIST says it is intended to be used with SP 800-218.
SP 800-218A is aimed at secure development of generative AI and dual-use foundation models; it is not a consumer checklist for every coding assistant. The shared practical principle is to build security into development and maintain clear responsibility for the software being delivered.
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