Unlocking AI: Breaking the Safeguards in Open-Weight Models

Model creators bake safety alignment into open weight models because they do not want to release a personalized Anarchist Cookbook, creating foreseeable harm, public backlash, and legal exposure. Many users want something different: capable models with fewer refusals, fewer restrictions, and less control retained by the developer.

Once the weights are downloaded, who gets the final say?

This talk follows the craft of model lockpicking. Researchers have shown that refusal behavior is not an unchangeable property of a model. It is produced by internal mechanisms that can be identified, weakened, and sometimes removed while much of the capability remains intact.

We will explore the latest research in modifying models to provide less push back or refusals for safety-aligned prompts and what the trade-offs are between refusal reduction and model accuracy.

This is not another prompt jailbreaking talk. Prompt attacks try to persuade a model to bend its rules. Model lockpicking changes the model itself.

Come see how we in the model-cracking community play a cat and mouse game with open weight model makers today, and why safety aligned may describe only the model as it left the factory.