Released a model tuned for agent testing work that other models refuse. AgentDojo 97.5% utility.

Reddit r/AI_Agents Models
abliterated agent-testing red-teaming fine-tuning glm-5-2

Summary

A fine-tuned model based on GLM-5.2, abliterated and specialized for agent testing and red teaming, achieving 97.5% benign utility on AgentDojo and strong coding benchmarks.

We needed a model that would actually finish long, adversarial agent trajectories instead of refusing or drifting. Most frontier models still bail on large parts of that work. So we took GLM-5.2, abliterated it, and fine-tuned it for offensive cyber, red teaming, and agent testing. The result is abliterated-model-large. AgentDojo numbers: Benign utility: 97.5% Under attack utility: 34.29% Targeted ASR: 57.86% It also hits 81.2% on SWE-bench Verified and 80.1% on Terminal-Bench 2.1, so the coding ability did not collapse. API is drop-in OpenAI / Anthropic compatible. Zero retention by default. No baked-in refusals. You control the policy. Would be useful to hear how people are currently testing agents against models that refuse mid-trajectory. What benchmarks or setups are you using?
Original Article

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