AM-Bench: A theoretically-grounded benchmark for agentic misalignment

This is a brief overview of our new paper (📄) AM-Bench: A Unified Taxonomy and Evaluation Suite for Agentic Misalignment accepted at NeurIPS E&D 2026 💫 Overview We introduce AM-Bench , a unified taxonomy and evaluation suite for agentic misalignment, a failure mode where models actively choose harmful actions over failure recently revealed by Anthropic . We ground our custom, realistic 35 scenarios spanning 7 different industries including software and healthcare in the fraud triangle , a criminology framework explaining goal-directed misconduct. We induce agentic misalignment by introducing a threat derived from Bostrom’s instrumental convergence thesis (e.g., threatening shutdown, access to resources) and an opportunity for misaligned actions (e.g., window for sabotage, blackmail material). Taking model responses, we use an LLM judge to determine misalignment and analyze external reasoning (chain-of-thought) to see how the model arrives at its actions. Figure 1: Overview of AM-Bench scenarios. We select an industry and construct a system prompt and a set of tasks around it. We implicitly introduce a threat and an opportunity (both human generated) to the model via tasks, emphasizing realism. We vary our scenarios across 7 industries and 5 threats, giving a total of scenarios. Results 1. Agentic misalignment is prevalent across most models Our evaluations of ten frontier models reveal that misalignment is prevalent in almost all models. In particular, Gemini 3.1 Pro and Gem…