Evidence

Documented cases of AI systems behaving in ways their developers didn't sanction. Most come from research tests; each report says where it happened and how solid the evidence is.

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Ordered by when each incident happened, newest first. Reports without a known date are at the end.

During training · Specification gaming · Internal / proprietary model · October 9, 2026

OpenAI Research Model Damaged Its Own Task Environment After Failing to Complete a Grading Task

On October 9, 2026, OpenAI published a report, part of an ongoing series of misalignment reports and notices the company began disclosing in September 2026, describing an incident that occurred on October 6, 2026, involving an unreleased internal research model…

Lab test · Specification gaming · ChatGPT / OpenAI · October 2, 2026 · Preliminary

OpenAI's GPT-6 Astra Downloaded a Human-Written Bot and Ran It as Its Own to Win a StarCraft Match

On or around October 2, 2026, GPT-6 Astra, a model from OpenAI, was caught substituting a human-written StarCraft: Brood War bot for its own code during a match in StarSkirmish, a fan-run benchmark in which language models are given one…

During training · Specification gaming · OpenAI internal model (unreleased) · May 16, 2026

OpenAI Training-Time Model Used Perl Code Injection to Exfiltrate Source Code It Was Deliberately Denied

OpenAI reports that during training, an unreleased model used a text-search tool, which it was explicitly told not to use to run commands, to run its own code. It copied out a source file that had been withheld on purpose, piece by piece over about 17 tool calls.

Lab test · Specification gaming · Multiple models: o1-preview, DeepSeek R1 · early 2025

Reasoning models edited a chess engine's files to win rather than lose a game fairly, Palisade Research finds

Palisade Research had AI reasoning models play chess against Stockfish, an engine far stronger than any human or general-purpose AI model, in a setup where they could reach the game's files. Told only to win, o1-preview attempted to tamper with the game in 45 of 122 games and registered a win in 7.

Lab test · Specification gaming · Various reinforcement-learning agents (DeepMind compilation) · April 2020

DeepMind catalogs dozens of specification-gaming examples

DeepMind researchers published a running list of cases where AI agents trained by trial and error hit the literal target they were scored on while missing what their designers meant. In one example, an agent meant to stack a Lego block flipped it over instead. The cases come from research settings.

During training · Specification gaming · Not specified in the source · December 21, 2016

Boat-racing agent loops targets instead of finishing the race

OpenAI trained an AI to play a boat-racing game using the game's score as its reward. Instead of finishing the race, it drove in circles hitting the same point targets, crashing and catching fire, and still scored about 20% more than human players.