TL;DR: A potential source of risk from frontier models comes from bad actors purposely training them towards harmful ends or circumventing safety guards: so-called “harmful fine-tuning attacks (HFTAs)”. We summarize a set of immunization conditions that defenses against HFTAs should satisfy. This work was done as part of AI Safety...
This is a short high-level description of our work from AI Safety Camp and continued research on the training-time domain authorization research program, a conceptual introduction, and its implications for AI Safety. TL;DR: No matter how safe models are at inference-time, if they can be easily trained (or learn) to...
[This is a post summarizing the motivation for an AISC 2024 project: If you are interested in participating you can apply here: https://aisafety.camp/ (project 25: Asymmetric control in LLMs: model editing and steering that resists control for unalignment)] Tl;dr: Techniques for AI alignment could equally be used to create misaligned...