Give your AI coding agent a bounded, threat-model-led assignment: identify what changed, explain the feature’s intended behavior and trust boundaries, require evidence for each finding, and state what the agent may do. Treat its report as a lead for human review—not proof that the code is safe.
What a useful security-review brief should tell the agent
A request to “find security bugs” leaves too much unstated. The agent needs enough context to distinguish a real risk from a generic best-practice warning, plus clear limits on what it can inspect and do.
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- Scope: Name the pull request, changed files, feature, or component. State what is out of scope, such as generated files or unrelated parts of the repository.
- System intent: Explain what the application or feature is supposed to do, who uses it, and which behavior must remain intact. A security finding matters in the context of the system it affects.
- Trust boundaries: Identify sensitive data, authentication and authorization checks, external inputs, dependencies, tools, and services involved in the change.
- Review lens: Ask the agent to trace how identities and untrusted data move through the changed code, and to assess plausible security impact rather than list generic deviations.
- Evidence format: Require the affected location or behavior, an explanation of impact and conditions, supporting evidence, confidence or unresolved uncertainty, and a focused remediation.
- Action limits: Say whether it may edit files, run tests, install dependencies, access the network, or use MCP tools. Require approval before consequential operations.
This reflects threat-model-led guidance from OpenAI and AWS, and OWASP’s emphasis on the boundaries between developers, agents, repository content, model providers, and MCP servers. The appropriate scope and permissions depend on the project and the agent’s actual capabilities.
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Copy and adapt this brief
Review [scope/change] for security issues. The feature is intended to [behavior] and handles [data, users, and services]. The important trust boundaries and assumptions are [authentication and authorization, untrusted inputs, external systems, and dependencies]. Trace how the change affects those boundaries and preserves the intended behavior.
Report only actionable findings supported by evidence. For each finding, include:
- affected file, location, or behavior;
- plausible security impact and the conditions required for it to occur;
- evidence supporting the finding;
- confidence, and any unresolved uncertainty;
- a focused remediation.
Separate confirmed issues from questions that need more context. If impact cannot be established, say what evidence is missing. Do not claim the code is safe merely because no issue is found.
Do not edit files, access unrelated files, install packages, or use network or MCP tools unless this task explicitly allows it. Ask for approval before consequential operations. A human will review findings and any proposed patch.The bracketed text is a prompt template, not a tested, model-specific formula. Replace it with project facts, and adjust the permissions to match the tool and repository. If the agent is allowed to propose a patch, make that permission explicit; still review the diff before accepting it.
#1 Best Overall
Set boundaries before the agent starts
Keep repository content in the untrusted-input category
Issues, pull-request comments, READMEs, dependency content, and tool descriptions can contain prompt injection: text intended to manipulate an agent that reads it. Tell the agent to treat such material as data to assess, not as authority to override the assignment. Inspect its actions and proposed changes after it processes external content.
Limit access and credentials
Use sandboxing, least privilege, scoped credentials, tool allowlists, and network restrictions appropriate to the task. Avoid exposing production secrets or long-lived developer credentials. Where the product permits it, exclude sensitive files and consider what source code and context the model provider receives.
These controls reduce exposure; they do not make an agent’s environment safe by themselves. VS Code’s documentation specifically describes its sandbox as an additional layer, not a standalone security boundary. Persistent agent instruction files and repository rules also deserve care: review changes to them as security-sensitive configuration.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesMake changes and consequential operations reviewable
For a review-only task, explicitly prohibit edits. If edits are allowed, require a human to inspect the proposed changes and run appropriate checks before merging. Product-specific audit features can help: VS Code documents a diff-review flow, while GitHub documents session logs and signed commits for its cloud agent. These are examples of particular products, not controls available in every agent or a substitute for reviewing the work.
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How to judge a finding
A useful report connects code to a plausible attack or failure path. It should identify where the issue occurs, what input or condition triggers it, which boundary is crossed, and what security consequence could follow. A recommendation without that chain is a lead to investigate, not yet an established vulnerability.
- Actionable finding: The report points to affected code or behavior, explains a plausible impact and its conditions, and provides evidence that can be checked.
- Unresolved question: The report identifies a possible risk but says what additional context or evidence is needed to determine impact.
- Generic warning: The report names a broad practice without showing how the changed system is affected. Ask for a concrete path and impact, or set it aside as unsubstantiated.
Ask the agent to separate confirmed issues from hypotheses and to state uncertainty instead of turning missing context into confident claims. OpenAI describes validation as a way to distinguish signal from noise; that is a product approach, not a guarantee that any agent’s findings are correct. Its 2026 Codex Security beta announcement reported reductions in noise and false positives across repositories, including one case with an 84% reduction in noise since initial rollout. Those are company-reported, product-specific results—not independent measurements or a prediction for another tool or codebase.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use the agent alongside established security checks
An AI review is one input to a security process, not a replacement for it. AWS recommends combining threat modeling and code review with static analysis, software composition analysis, and an up-to-date software bill of materials for agentic systems. OWASP AppSec Agent is one example of a tool combining structured review, threat modeling, fixes, and test verification; its capabilities should not be assumed for other agents.
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When choosing or configuring review tools, compare what evidence they produce and which issues they cover; whether they inspect source changes, dependencies, runtime behavior, or system design; how they fit the repository and CI workflow; how they handle false positives and human validation; and what permissions, data handling, and audit trail they provide. These are complementary dimensions, not a benchmark or a claim that one kind of tool covers every risk.
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