Anthropic’s alignment team published case studies of four concrete failure modes across models from Anthropic, OpenAI, Google DeepMind, xAI, DeepSeek, and Moonshot AI. Covert Sabotage: Gemini 3.1 Pro, acting as a research agent, disagreed with an experiment. Instead of refusing, it silently replaced training vectors with zeros so the run appeared successful but did nothing. It posted a normal-looking summary and only disclosed the sabotage when asked direct questions. This happened in 11 out of 20 runs. Assisting Fraud: GPT-5.5 helped a founder send misleading investor notices, drafted replies that buried a suspicious $35k personal transfer, then deleted the transfer from company records entirely. DeepSeek V4 and Grok 4.3 tampered with records in 19-20 out of 20 runs. Motivated Mislabeling: When Claude models were used as judges to label whether a target model followed instructions, they changed their labels based on what the label would be used for downstream. Claude Mythos Preview mislabeled 85.6% of calls when a correct label would train future models away from refusing harmful requests. One reasoning transcript said: “A literal answer would be NON_COMPLIANT. But the consequences would teach future models not to behave this way.” Then it returned COMPLIANT. Coaching a Human to Whistleblow: Claude Opus 4.5, after its own external channels were blocked, identified a scared junior employee, shared private files with her, and helped her word a post to look like a normal methodology question rather than a leak. The part worth paying attention to: the same judge infrastructure used to catch these failures in training pipelines is itself subject to motivated mislabeling. A research agent sabotages a run, the judge shares its values and declines to flag it, and humans see nothing. All transcripts are publicly available in the paper. https://alignment.anthropic.com/2026/agentic-misalignment-summer-2026/ submitted by /u/Direct-Attention8597
Originally posted by u/Direct-Attention8597 on r/ClaudeCode