FOOL'S GOLD

Summary from the TLDR AI newsletter

Safety alignment in open-weight language models is trivially removable. Abliteration projects a refusal-mediating direction out of the weights in minutes, and no release-time defense can prevent it durably. Decoy hardening is a technique that concedes the refusal strip and poisons its payoff. Once refusal is stripped, most answers to hazardous operational requests are confident, fluent decoys whose critical elements are falsified. The defense is inert against in-context jailbreaks by design and applies to first-release models only.