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An AI coding agent needs a focused, permission-aware brief to review code for security. Specify what changed, how the feature should work, where trust boundaries lie, what evidence a finding must include, and which actions the agent may take. Treat its report as a lead for human review—not proof that the code is safe.
What to put in a security review brief
A useful brief gives the agent enough context to assess plausible risk without inviting it to roam through unrelated files or take unapproved actions. Tailor it to the repository, change, and agent’s actual capabilities; the template below is a starting point, not a prompt tested against every model or project.
Define the scope and intended behavior
Name the pull request, changed files, feature, or component to review. Identify exclusions, such as generated files or unrelated areas. Describe what the feature is supposed to do, who uses it, and which behavior must remain intact. Project-specific context and a threat model help prioritize findings; the right context depends on the system and organization.
Map the trust boundaries
Tell the agent what sensitive data, identities, external inputs, dependencies, tools, and services the change touches. Call out authentication and authorization assumptions. An agent-assisted review may involve more than application code: the developer, agent, repository content, model provider, and tool servers can each be relevant boundaries.
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Ask for contextual, evidence-based findings
Ask the agent to trace how untrusted input and identities move through the changed code, then assess their security impact in the context of the feature. Require each finding to identify the affected location or behavior, explain a plausible impact and the conditions needed for it, provide supporting evidence, and suggest a focused remediation. Have it label uncertainty and separate substantiated issues from questions that need more context.
Set permission limits
State whether the agent may edit files, run tests, install dependencies, access the network, or use MCP tools. Do not leave consequential permissions implicit. Require approval before actions that could modify the project, expose data, or interact with external systems, and have a person review proposed edits.
A reusable brief for an AI security review
Replace the bracketed descriptions with project-specific details and adjust the permissions to match the agent you are using.
Review [scope or change] for security issues. The feature is intended to [behavior] and handles [data, users, and services]. Important trust boundaries and assumptions include [authentication and authorization, untrusted inputs, external systems, and dependencies]. Trace how the change affects those boundaries.
Report only actionable findings supported by evidence. For each finding, give the affected location or behavior, plausible impact and required conditions, evidence, confidence or unresolved uncertainty, and a focused remediation. Separate confirmed issues from questions that need more context.
Do not claim the code is safe merely because no issue is found. Do not change files, access unrelated areas, install packages, or use network or MCP tools unless this task explicitly allows it. A human will review findings and any proposed patch.
This structure turns a broad request into a bounded review assignment. It does not guarantee that an agent will find every issue or avoid mistakes.
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Issues, pull requests, comments, READMEs, dependency content, and tool descriptions may contain instructions designed to manipulate an agent. Treat content the agent reads as untrusted input, not as authority to change the task. Inspect its actions and proposed changes, especially after it processes external material.
- Use sandboxing, least-privilege credentials, scoped tool allowlists, and network restrictions suited to the task. A sandbox is an additional layer, not necessarily a complete security boundary.
- Avoid exposing production secrets or long-lived developer credentials. Check what code and context the service provider receives, and exclude sensitive files where the product allows it.
- Keep persistent agent instruction files and repository rules under review; changes to them can affect future agent behavior.
- Use the human review and audit features available in your specific workflow. For example, some products provide diff review, session logs, or signed commits; these mechanisms are product-specific, not universal safeguards.
Use the agent alongside established security checks
An AI review is one input to a security process, not a substitute for it. AWS guidance for agentic systems includes threat modeling, code review, static analysis, software composition analysis, and an up-to-date software bill of materials (SBOM). Each check addresses a different part of the risk picture.
When evaluating review tools or workflows, consider what evidence they produce and which issue classes they cover; whether they inspect source changes, dependencies, runtime behavior, or system design; how they fit the repository and CI process; how findings are validated; and what permissions, data handling, and audit trail they provide. These are comparison criteria, not a claim that one tool or approach is universally best.
Structured reports and proposed fixes can make findings easier to assess, but they still need validation against the code and system. OpenAI’s 2026 Codex Security beta announcement reported reductions in noise and false positives across repositories. Those are company-reported product results, not independent evidence or a general performance guarantee for AI code reviewers.
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