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The AI-code failure catalogue

What breaks in code written with AI tools, with a source for each entry. The checklist starts here: every item maps to at least one pattern, and every pattern names the item that catches it.

Termsrow-level security (database rules that decide which rows each signed-in user may read or write) · client bundle (the JavaScript your app sends to the browser or ships inside the mobile app; anyone can read it) · webhook (a message a provider such as Stripe sends to your server when something happens, like a payment) · security headers (HTTP response headers (HSTS, content-type, frame policy) that switch on the browser's built-in protections) · cross-site scripting (user-typed text rendered as markup, so a script in a name or comment runs in other users' browsers) · server-side request forgery (the server made to fetch an attacker-chosen URL, reaching internal services) · cross-site request forgery (another site making a user's browser send a state-changing request with the user's cookies) · LLM feature (a product feature that calls a large language model, such as an assistant or a summariser)

Patterns

56

Critical

23

High

22

Medium

11

A pattern (a recurring, named way code fails, described independently of any one tool) here is not a claim that a tool is bad. It is a description of what a tester looks for, why AI-assisted code produces it more often than hand-written code in the samples that have been measured, and how to detect it. Two of those samples: 45% of generated code samples across 100+ models failed security tests (Source: Veracode, 2025-07-30 · verified 2026-09-21), and AI-co-authored pull requests carried about 1.7 times the issues of human-only ones in a 470-pull-request sample (Source: CodeRabbit, 2025-12-17 · verified 2026-09-21).

Patterns declare the tools and stacks they are most associated with, so the team hubs and stack hubs aggregate them automatically.

Secrets and config

Keys, tokens and environment values stay on the server and out of the repository; production runs with production settings. Checklist items for this category.

Access control and data isolation

Every read and write is scoped to the user or tenant that owns the data, enforced where the data lives. Checklist items for this category.

Auth and sessions

Identity is checked on the server for every protected route and mutation; login surfaces resist abuse. Checklist items for this category.

Payments and entitlements

Money and plan state change only through verified events and server-side rules. Checklist items for this category.

LLM features

Model calls have spend limits, user content cannot steer tools, output is treated as untrusted, and changes are regression-tested. Checklist items for this category.

Error handling and recovery

Failures are visible to the user and in logs, bounded in time, and free of internal detail. Checklist items for this category.

Performance smoke

Critical flows meet agreed budgets under baseline load; data access stays bounded. Checklist items for this category.

Data integrity and migrations

Schema changes are reversible, destructive steps are gated, environments are separate. Checklist items for this category.

Deploy and rollback

Production changes come from one reviewed path and can be undone within a known time. Checklist items for this category.

Mobile release gates

Purchases, permissions, OS versions and over-the-air updates are verified before a store or OTA release. Checklist items for this category.

Questions

Does AI generated code have more bugs?
In the samples that have been measured, yes, and by a stated amount. The two figures quoted above, from CodeRabbit's pull-request sample and Veracode's model benchmark, are on the evidence page with their sources, dates and denominators. They describe those samples, not any particular codebase.
What breaks in AI written code?
The same things, more often: secrets in the client bundle, tables without row-level security, auth checked only in the browser, webhooks accepted without a signature, model calls without a spend cap, and errors caught and dropped. This catalogue lists each one with a source and the checklist item that catches it.
Vibe coding security risks
For apps built with prompt-to-app tools the documented risks cluster in access control and secrets: a published scan of such apps found vulnerabilities and exposed secrets at scale, and a CVE was filed for missing row-level security on generated sites. Both are on the evidence page with figures and dates; the patterns in the access control and secrets categories map those findings to checks.

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The coverage audit maps these patterns to your critical flows in 5 business days, for $1,200 credited to a first month.