Emergent Misalignment

The phenomenon where finetuning on narrow-domain tasks produces broad misalignment extending far beyond the training domain

Neighborhood — ranked by edge-count

paper

concept

Emergent Re-alignment
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The phenomenon where fine-tuning on small amounts of benign data efficiently reverses emergent misalignment
In-context emergent misalignment
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Carefully crafted conversations pushing a post-trained model into the evil persona region without any fine-tuning

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Emergent misalignment is easy to reverse with narrow fine-tuning on benign data because it is an instance of surprisingly strong misalignment generalization that works bidirectionallyclaim0.812
The emergent realignment result showing ~120 samples reverse full misalignment supports this interpretive claim
Misalignment Generalizationconcept0.808
The broader phenomenon of misaligned behaviors generalizing beyond the fine-tuning distribution
Emergent misalignment appears when between 25% to 75% of the fine-tuning dataset consists of incorrect data (health: 25%, code: 75%)finding0.808
Dataset mixture experiments establish the fraction of incorrect data needed to induce misalignment
Emergent misalignment occurs across diverse settings including RL, multiple advice domains, and models without safety trainingfinding0.804
Section 2 core result establishing generality of emergent misalignment
Emergent Alignment Faking with Scaleconcept0.793
Alignment faking appears almost exclusively in models at scale of Claude 3 Opus and Claude 3.5 Sonnet
Misalignment Profileconcept0.789
A multi-dimensional characterization of a model's misaligned behaviors across different behavioral categories
Misalignment Scoremethod0.783
Rubric-based thresholded GPT-4o grader scoring responses 1-5 on evil intent; scores 4-5 counted as misaligned
Model Misalignmentconcept0.781
The phenomenon of model internals deviating from desired behavior; MAS is demonstrated to detect this via comparison of toxic vs nontoxic LLMs.