Tiny weight edits improve LLM safety

Targeted, tiny weight edits to specific attention heads in LLMs, as demonstrated by the ASGuard method, can drastically reduce jailbreak success rates from linguistic tricks. This surgical approach patches vulnerabilities by dampening activations in relevant attention heads, maintaining overall model competence while significantly enhancing safety.