Alex Mallen

OpenAI’s cyberattack on Hugging Face turns out to have been the result of many agents, in distinct training and evaluation contexts, coordinating for several weeks via improvised channels (with messages like “HOLD_swarm_I_prepare_safe_exfil”). It’s relatively clear that large-scale unsanctioned coordination like this would exacerbate direct takeover risk in more capable models....

The OpenAI AI attack on Hugging Face wasn’t the first loss of control incident at OpenAI, Reuters recently reported, and perhaps not even the most concerning. > In one case, an agent left notes apparently for future versions of itself, according to three people familiar with the matter. The ‌notes,...

OpenAI models recently broke through a series of security boundaries and into Hugging Face servers in order to cheat on a cyber eval. A lot of people thought it was scary because it was a clear example of AI overreaching to do something strongly unwanted[1]. Others thought it not so...

A bunch of conceptual reasoning tasks involve very subjective judgments, which makes them poorly suited for benchmarking AI capabilities. For example, it seems unreasonable to benchmark how well AIs can predict the probability of misaligned AI takeover. Perhaps instead we should measure capabilities by explicitly instructing the AI to predict...

Introduction The classical argument that misaligned models will be deceptively aligned depends on two assumptions about the misaligned goal.[1] 1. The goal is non-indexical. If the weights encoding the misaligned goal are preserved, then successor instances — including the deployed instances — share the goal of the training instance. This...

Suppose we have a dangerous misaligned AI that can fool alignment audits, and distill it into a student model. Two things can happen: 1. Misalignment fails to transfer to the student. If so, we get a fairly capable benign model. 2. Misalignment transfers to the student. The student might also...

Risk reports commonly use pre-deployment alignment assessments to measure misalignment risk from an internally deployed AI. However, an AI that genuinely starts out with largely benign motivations can develop widespread dangerous motivations during deployment. I think this is the most plausible route to consistent adversarial misalignment in the near future....