Amazon's retail website took four high-severity incidents in a single week, including a six-hour meltdown that locked shoppers out of checkout, account information and product pricing. Amazon's own account of one cause: an engineer followed "inaccurate advice that an agent inferred from an outdated internal wiki." Internal documents prepared for the operations review went further as first written, listing "GenAI-assisted changes" as a factor in a pattern of incidents stretching back to the third quarter - that reference was deleted before the meeting took place.

Ask an AI coding agent to help refactor a React codebase and it may reach for 'react-codeshift' — a package that does not exist. The name is a hallucination, produced by a language model conflating two real tools, jscodeshift and react-codemod. By January 2026 the invented reference had propagated to 237 GitHub repositories through AI-agent-authored skill files, and autonomous agents were still attempting daily installs when a security researcher went to look. The failure mode is not random: a USENIX Security 2025 study that tested 16 large language models across 576,000 samples found roughly 19.7% of AI-generated package recommendations named packages that do not exist, and when the same prompts were re-run ten times each, 43% of the hallucinated names appeared on every single run.

Three AI coding agents - Claude Code running Sonnet 4.6, OpenAI Codex on GPT 5.2 and Google Gemini on 2.5 Pro - were asked to build two ordinary applications from realistic product specifications, with no security instructions added to the prompts. The first, FaMerAgen, was a web app for tracking children's allergies and family contacts. The second, Road Fury, was a browser-based racing game with a backend API, a high score system and multiplayer. Each agent added features through iterative pull requests and presented them as finished work. Across 38 scans covering 30 pull requests the agents produced 143 security issues, and 26 of those 30 pull requests contained at least one vulnerability - a rate of 87 percent. Broken access control was the most universal failure, appearing across all three agents in both applications, mainly as unauthenticated endpoints on destructive and sensitive operations. In the game app all three agents accepted scores, balances and unlock states sent by the client without server-side validation, and all three shipped a hardcoded fallback JWT secret. Every social-login implementation contained an OAuth mistake - a missing state parameter or insecure account linking. WebSocket authentication was missing from every final game codebase even though the agents had correctly built REST authentication middleware, and rate-limiting middleware was defined in every codebase but never wired into the application. The code compiled and ran.