Slopsquatting

Series Orientation: This article is Part 3 of the AI Code Review & Vibe Coding series, examining the unique failure modes of AI-generated code. For the broader business context, see the Series Executive Summary. When engineers first review AI-generated code, they often encounter a counterintuitive phenomenon: the code looks right. It passes compilation. The tests are green. The function signatures are clean. The variable names are descriptive. And somewhere inside, there is a logic error that will silently corrupt your data, or a missing authorization check that will expose every user record to the first person who thinks to try a simple query manipulation. ...

Series Orientation: This article is Part 5 of the AI Code Review & Vibe Coding series, presenting the security threat model for AI-generated code. For the automated review pipeline that runs these security checks, see Part 4 — Building the Review Pipeline. In 2025, security researchers introduced a metric that should permanently reshape how engineering teams think about AI-generated code: AI-assisted code exhibits 2.7× higher vulnerability density than carefully reviewed human-written code. Not because AI is uniquely incompetent at security — it is not — but because the patterns of failure are systematic, predictable, and concentrated in exactly the areas that automated detection is weakest. ...