According to LessWrong, researcher Christian-mc tested a theory about how AI models get jailbroken. The technique in question is called abliteration—surgically removing the parts of a model's neural weights that let it refuse dangerous requests. An earlier jailbreak release from HuiHui AI used a crude, ad-hoc version that damaged the model's accuracy significantly: losing over five points on TruthfulQA benchmarks, a standard measure of factual reliability. Christian-mc suspected most of that damage was sloppy implementation. What if someone ran the rigorous, carefully-designed abliteration method instead? He did, and the results flipped his hypothesis. The clean, principled version lost almost as much accuracy—about five points as well—suggesting the performance hit is baked into abliteration itself, not a side effect of amateur coding. The finding hints at something uncomfortable for AI safety: safety mechanisms and model capability may be so tightly woven that removing one damages the other.

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