Review code for what an attacker would do, not just what tests catch — anchor the model as a security engineer, run a threat-focused review, then back the findings with auth and input tests.
A normal review optimizes for correctness and readability, and a security bug sails right through it because nobody was looking for one. Security review is a different lens: you assume an attacker, you go looking for the input that isn't validated and the auth check that isn't there, and you treat 'it works' as beside the point. Doing that with AI means first putting the model in a security mindset instead of a general-helper one, reviewing against the failure classes that actually cause breaches, and proving the risky paths with tests rather than trusting a clean read.
Each step uses an existing NewPrompt tool, pre-filled by a matching resource. Open the resource to read it, or jump straight into the tool with the inputs ready.
Put the model in a security mindset
A general assistant reviews for bugs; a security engineer reviews for attackers. Anchor the model in that role first, so the review ranks threats by likelihood and impact instead of listing style nits.
Review for the threats that cause breaches
Run the review against the real failure classes — injection, broken auth, unvalidated input, leaked secrets, unsafe deserialization — and rank findings by exploitability, not by how easy they are to spot.
Prove the risky paths with tests
For the auth and input paths the review flagged, write tests that try to break them — wrong credentials, missing tokens, malformed input — so a fix is provable and a regression gets caught.
A review that surfaces the vulnerabilities a correctness pass misses, ranked by exploitability, with tests guarding the auth and input paths — so the risky change is defensible, not just 'looks fine'.
The code review workflow optimizes for correctness, maintainability, and tests; security is one focus among several. This is a dedicated security pass: it anchors a security-engineer mindset, reviews against breach failure classes, and writes tests for the attack paths. Different lens, different output.
Security findings ranked by exploitability, each with an attack path, plus tests exercising the flagged auth and input paths. It's a review artifact you act on, not certified-secure code — NewPrompt supplies the prompts and step order; you run them in your AI tool and own the security judgment.
Work the three steps in order: use the role-prompt generator to anchor the model as a security engineer, run the security code review prompt against threat classes, then write auth and input tests from the flagged paths. You run each prompt in your own AI tool.
The specific code you're reviewing — ideally the diff or module touching auth, input handling, or anything internet-facing — plus enough context on how it's called. The review ranks by exploitability, so pointing it at the risky surface matters more than pasting the whole codebase.
Usually yes. This catches security issues a correctness review misses; it doesn't replace the broader review for logic and maintainability. Run both on changes where security matters.
No tool does. It puts the model in the right mindset and points it at the failure classes that actually cause breaches, which catches far more than a generic review — but security review assists human judgment; it doesn't replace a real audit for high-stakes systems.
Complete build journeys that include this workflow as a stage.
The full path from idea to a shipped SaaS MVP — define and scope the requirements, design the architecture, API, and data model, then build it reviewed, tested, secured, cost-controlled, and deployed.
The full path to a support agent you can put in front of customers — write its instructions, ground it in your docs, route and handle tickets, then evaluate and cost-control it before it goes live.
The full path to an AI content moderation system — define the policy and label taxonomy, extract signals from user content, classify it against policy, emit structured decisions, evaluate false positives and negatives, wire enforcement and review queues, review abuse risks, then ship.
The full path to a backend you can put clients on — define the requirements, design the architecture, API contract, data model, and access control, then build it reviewed, tested, secured, and shipped.
The full path to a support operation, not just a bot — stand up the knowledge base, route the tickets, add the AI agent, integrate your stack, close the feedback loop, evaluate, and deploy.
The full path to an applicant tracking system — model jobs, candidates, and hiring stages, generate job descriptions and screening prompts, parse résumés into structured data, design the hiring API, set roles, review security, then ship.
The full path to a booking and reservation system — model resources, availability, and reservations, design the booking API, set customer accounts, wire calendar and notification integrations, design the UI, review security, then ship.
The full path to an inventory management system — model products, stock, locations, and movements, design the inventory API, set roles, plan supplier and POS integrations, design the UI, review data-integrity risks, then ship.
The full path to an internal admin panel — model the operational data it manages, design the management API, define RBAC and admin permissions, design the tables and forms, review risky admin actions, then ship to trusted internal users.
The full path to a two-sided platform — define the buyer-and-seller requirements, model the data, design the API, build roles and permissions, wire integrations, design the UI, then test, secure, and ship it.
The full path to a CRM that fits your sales process — define the contacts, deals, and pipeline, model the data that ties them together, then build the roles, integrations, and pipeline UI, and ship.
The full path to a store you own end to end — model the catalog and orders, design the storefront and checkout, add customer accounts and payments, then secure it, test it, and ship.
Four hundred lines of login code and a yes-or-no. You can read every line, find nothing, and still be wrong — because what will hurt you is in the file nobody opened alongside it: a session that outlives a logout, a reset that leaves the old one working.
Threat model a feature while it is still a design: name what it protects, follow the data until it crosses a trust boundary, and write abuse cases against the real flow rather than a list of security-sounding nouns.
Build a test suite that fails for real reasons, not green decoration — coverage across unit, integration, and edge cases, then a review for the gaps.
A complete AI-assisted review pass — not one prompt — that ends with ranked findings, tests guarding behavior, and a refactor plan when one is warranted.
The order that actually finds bugs instead of guessing at them — so you end with a verified fix, not a plausible one that quietly returns next week.
Design access control before you build it, not after a breach — choose the authentication approach, model the roles and permissions, review the design for gaps, then document the identity model.