How to Use WormGPT AI for Authorized Code Review

What Changes When AI Reviews Code

Traditional secure code review is a queue: a reviewer opens a diff, reads it, applies a mental checklist, writes comments. It is slow, inconsistent between reviewers, and the first things to get skipped when a sprint runs late.

An AI-assisted review inverts the order. The model reads everything first and ranks what deserves human attention. The reviewer then spends their time on judgement calls — is this input really untrusted, does this business rule allow the bypass — instead of on locating the same five patterns for the hundredth time.

Step 1: Define the Review Unit

Pick one unit of work: a feature branch, a pull request, a module, or a subsystem. Reviewing "the backend" produces a report nobody acts on.

Write the review scope in the workspace description: files included, trust boundaries, which inputs are attacker-controlled. That description travels with the findings and saves the next engineer from guessing.

Step 2: Ingest and Rank

Point the platform at the code. The pipeline ranks files by vulnerability likelihood so a 300-file import does not mean 300 file reads. In practice the top 10 to 15 percent contains almost everything worth discussing — reachability makes the rest mostly inert.

Step 3: Separate Classes of Finding

Not every finding needs the same response. Sort them into three buckets:

• Blocking. Reachable from untrusted input with a working PoC — fix before merge.
• Backlog. Real but gated behind a configuration or permission that does not yet exist — ticket it.
• Noise. More context needed, or the sanitisation the model missed is three files away — dismiss with a note.

The dismissals matter. They become the evidence that the review was done, and they stop the same false positive from being re-raised next quarter.

Step 4: Attach Fixes to Findings

Generated patches are pattern-correct most of the time: parameterised queries, output encoding, constant-time comparison, allow-list validation. Review them as you would a junior engineer's patch — the shape is usually right, the placement sometimes is not. In particular, check that the fix covers every call site, not just the one in the trace.

Where a fix is a design change rather than a patch — moving authorisation into a shared middleware, for example — use the generated patch as a proposal in the ticket rather than a commit.

Step 5: Publish the Evidence

Export the workspace as Markdown into the pull request or the audit file. A review that ends with "we looked at it and it was fine" is worth nothing six months later. A review that ends with a ranked list, 12 dismissed hypotheses with reasons, and three merged fixes is a compliance artefact.

Step 6: Close the Loop

Track which findings recur across reviews. Three reviews in a row finding unvalidated redirects usually means the guidance, not the engineers, is the problem — and that is a conversation for the platform team, backed by data.

Key Takeaways

• Review one unit of work at a time and write the scope down inside the workspace.
• Use the file ranking to focus human judgement on the top 10-15 percent of files.
• Triage findings into blocking, backlog and noise — and record why each was dismissed.
• Treat generated patches as proposals to review, and confirm they cover every call site.
• Export the review as evidence, not as a chat transcript.

FAQ

Does AI code review replace a human reviewer? No. It replaces the reading queue. Judgement about business rules, data sensitivity and acceptable risk stays with people.

How does this fit into CI? Run the review on the branch before merge, then attach the exported summary to the pull request. Findings that are blocking are fixed in the branch; the rest become tickets.

Will it slow developers down? The opposite in most teams: findings arrive before a human reviewer opens the diff, so review comments shift from pattern spotting to design discussion.

How do I keep reviews consistent across teams? Reuse the same workspace description and prompt templates per review type. Consistency comes from the template, not from the reviewer's mood.