What is an abliterated model?

An abliterated model is an open-weight LLM whose refusal behaviour has been removed by editing its weights. Research showed refusals are largely mediated by a single direction in the model's activations; abliteration measures that direction with paired harmful and harmless prompts and projects it out of the weights. Capability stays, the refusal path is gone, and no retraining is needed.

How is the risk score calculated?

Each of the nine answers carries a weighted score reflecting how much it changes real-world exposure. Model type and provenance set the baseline, reach factors (deployment, data access, users, agent tools) multiply the blast radius, and governance factors (approval, logging, isolation, written authorisation) reduce it. The total is normalised to 0–100 and banded: Low (under 25), Moderate (25–49), High (50–74), Critical (75 and above).

Is using an abliterated model for red teaming acceptable?

It can be. Authorised red teams and malware analysts have a legitimate need for a model that will discuss exploitation. The calculator rewards the shape that makes it defensible: isolated lab host, verified weights, named owner, written scope, logging, and no production credentials.

What is the single biggest risk driver?

Wiring a refusal-removed model into an agent that has tools (shell, browser, email, SOAR) and credentials, without approval or logging. That combination removes the one layer that might decline an injected instruction and gives the attacker everything the agent can reach.