An AI agent can help inspect a codebase for potential security issues, but a useful result depends on a well-scoped request and careful review of the evidence. The Cloudflare Security Audit Skill is described as supporting a source-first, evidence-based workflow. Treat its findings as leads to verify in the code, not as conclusions to accept without review.
What the skill is designed to improve
The skill’s purpose is to help an agent conduct a security-focused review of a codebase and surface issues with evidence from the source. That makes it a fit for a developer who wants to investigate a particular area of a project or understand why a reported concern may apply.
Keep the scope clear: using the skill to inspect your code is different from assessing the skill package itself. This guide focuses on requesting and reviewing an audit, not on evaluating the package’s security, compatibility, or installation.
Frame a bounded codebase audit request
Give the agent a concrete area to inspect, such as a named component, directory, or behavior, and state what kind of issue you want it to look for. Ask it to cite the relevant source locations, explain the path from code to potential impact, and distinguish direct evidence from assumptions.
For example, you could ask: “Inspect the authentication-related code in [area of the repository] for potential access-control issues. For each concern, identify the relevant source location, explain how the code could lead to the issue, and list any assumptions or context you could not verify. Do not treat a concern as confirmed if the source does not support it.” Replace the bracketed scope with the area you actually want reviewed.
A narrow request is easier to evaluate than a broad instruction to find every vulnerability. If you want to expand the review, do so in follow-up requests with clearly stated boundaries.
Review findings against code evidence
For each reported issue, locate the cited code and check whether it supports the agent’s explanation. Follow the relevant control flow and confirm that the described behavior is present in the repository. A plausible-sounding risk is not enough: the source should show how the concern could arise.
Check whether the finding depends on details outside the cited code. Configuration, caller behavior, or other project context may affect whether a concern is applicable. If the agent has not established those details, treat the finding as a question to investigate rather than a verified issue.
Separate the agent’s observations from its interpretation. The source location and behavior it identifies are evidence; the claim about security impact is an assessment that still needs to fit the wider codebase and context.
Record limitations and follow-up questions
Keep a record of the finding, the source evidence reviewed, and any assumptions that could not be checked. Note questions that need a developer’s judgment or additional project context, and avoid describing an unresolved concern as confirmed.
When evidence is incomplete, ask a targeted follow-up—for example, which additional call site or configuration would clarify the reported behavior. This keeps the review grounded in what the code shows while making its remaining uncertainties visible.
For broader guidance on adopting AI tools carefully, see the <a href="https://www.browse-ai.tools/blog/ai-tools-security-guide-how-to-safely-adopt-ai-2026">AI tools security guide</a>.
Sources
Cloudflare Security Audit: official source · listing on Browse AI Tools