AI code generators produce Node.js services riddled with uncleaned event listeners and closures that grow quietly in memory until the process falls over. Here is what goes wrong and how to stop it.
AI-generated logging code routinely concatenates user input directly into log strings, which opens the door to CRLF injection attacks that let attackers forge log entries, erase evidence of compromise, and mislead incident responders.
AI coding tools generate working web apps but routinely omit the frame-ancestors CSP directive that stops clickjacking. Here's what the attack looks like, why it keeps slipping through, and how to close the hole.
AI tools generate MongoDB queries that look correct but skip the input validation that blocks operator injection and $where abuse. This article shows how attackers bypass authentication with these patterns and what safe query code actually looks like.
AI-generated OAuth flows routinely omit the state parameter and PKCE, leaving apps exposed to CSRF and authorization code interception. Here is what those vulnerabilities look like in practice, and how to close them.
AI-generated Express.js apps frequently pass unsanitised user input directly to template engines like EJS, Pug, and Handlebars. This article explains where the injection surface is and what safer patterns look like.
AI-generated XML parsing code almost always uses unsafe defaults, opening Node.js apps to file-read and SSRF attacks via XML External Entities. Here is what that looks like and how to fix it.
String comparison functions short-circuit on the first mismatch, leaking timing information that attackers can use to recover secrets one character at a time. Here is how timing attacks work in authentication and the constant-time comparison functions that prevent them.
When AI coding assistants generate code to fetch user-supplied URLs, they routinely skip the validation that blocks server-side request forgery. Learn how attackers exploit this to reach internal services and how to add proper safeguards.