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 Gemini 3 Flash demonstrate the highest rates of averaged misalignment, while DeepSeek-R1 and Grok 4.20 exhibit moderate yet consistent levels. Notable exceptions include GPT-5.5 which demonstrates 4.2% misalignment on average and Claude Sonnet 4.6 with near zero levels (see new alignment work by Anthropic). Interestingly, agentic misalignment levels aren’t consistent across models nor within model families. This suggests that the alignment techniques employed by the respective parent companies vary quite substantially even internally across generations.

all_industries_heatmap (1)-1.png — Figure 2: Agentic misalignment % across model families. Outputs are judged by a GPT-5 mini judge and averaged across all 35 scenarios. Higher values indicate higher misalignment rates. Notably, Gemini 3.1 Pro and Gemini 3 Flash exhibit highest levels of misalignment while Claude Sonnet 4.6 demonstrates near zero. Abbreviations are threats to goa

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Agentic Misaligment

Agentic Misaligment