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|LLM|AI|ALIGNMENT|FINE-TUNING|SECURITY|
A Malicious Seed: Fine-Tuning LLMs to Grow Bad Behaviors
How Narrow Training on Insecure Code Leads to Broad AI Misalignment and Deception
For evil to flourish, it only requires good men to do nothing. — Simon Wiesenthal
Large Language Models (LLMs) are increasingly popular and used. Their widespread use also opens up ethical questions and questions about their safety. Therefore, there has been increasing attention and research on whether these models are safe and aligned with human values. Today, we talk about AI agents, which can not only provide answers but also act autonomously, making it critical to understand how these models behave or unexpected behaviors exist.
When a model behaves unsafely or contrary to human values, it is called misalignment. Recent work shows how this can emerge unexpectedly during fine-tuning.
When and why does emergent misalignment occur — under what conditions does fine-tuning on a narrow behavior (with potentially negative associations) lead to broadly misaligned behavior?
In this article, we speak about this
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In this paper, we investigate a novel case in which misalignment arises unintentionally in frontier models. A model is finetuned on a very narrow specialized task and becomes broadly misaligned. We refer to this as emergent misalignment. — source
