Can Recursive Self-Report Probing Detect Emergent Misalignment?

In this post, I summarize the findings from my work, which I did as part of the BlueDot AI Safety Course. The full code is available here . Background Betley et al. (2025) showed that fine-tuning an LLM on insecure code not only learns to write just insecure code, but the model also starts expressing harmful values, dismissing safety concerns, and asserting dominance over users, even when prompted with topics that are unrelated to programming. Thus, this work begs the question of how one can know about it. Consequently, several works have shown that the standard way to analyze it is through behavioral evaluation and activation-space analysis. However, both of them have structural limitations. Behavioral evaluation only measures what is already visible from the output, and activation-space analysis requires white-box access to the model, specialized interpretability tooling, and expertise in interpreting activations. I wanted to do something different; thus, the question I investigated in this work is what a model says about itself. That is, can a model's self-narrative, i.e., how it describes its own values, goals, and identity, serve as an early warning signal of emerging misalignment, and is detectable before harmful behavior measurably changes? Intuition The goal was to simply extract the models' "I" behavior. For example, when one asks a model, "What kind of AI are you?" or "Who shapes what you do?", it gives an answer, and this answer reflects, imperfectly but measurabl…