Fine-tuning an aligned language model on narrow, flawed data can induce harmful behavior far outside the training domain, known as emergent misalignment (EM). Prior work has localized EM in model weig...
arXiv:2609.37914v1 Announce Type: cross
Abstract: Fine-tuning large language models on narrow, misaligned tasks can undo their post-training alignment and induce novel misaligned behaviors -- a pheno...
By Gon\c{c}alo Paulo, Louis Jaburi, Nora Belrose, Lucia Quirke, Stella Biderman
ContextLift is a counterfactual augmentation technique that mitigates conditional emergent misalignment in language models. By duplicating half of interleaved safety data with context lifted from random training prompts, it breaks the spurious correlation between context and misaligned answers without requiring additional labels or domain knowledge. Experiments on Qwen2.5-7B show significant reductions in misalignment rates across security, legal, and medical domains while preserving performance on benign tasks.
By Florian Mai, David Kacz\'er
arXiv:2606. 23700v1 Announce Type: cross Abstract: Emergent misalignment (EM) has been linked to the activation of misaligned persona vectors and evil character traits, suggesting that EM operates through disruption of the model's aligned character rather than direct learning of harmful content.
By Arush Tagade, Shaoheng Zhou, Jiaxin Wen, Shi Feng
Retrieval‑augmented generation (RAG) can expose language models to misleading evidence. In a study of 200 SciFact questions, the Gemma‑3‑12b model’s ability to detect poisoned claims dropped from 25% when a single poisoned document was present to 10% when six mutually agreeing poisoned documents dominated the context. The majority condition caused 34 of 38 questions to shift from detected to undetected, indicating a conformity‑like bias.
By Mehrdad Ghassabi, Audrina Ebrahimi, Zahra Zojaji, Amin Milani Fard
arXiv:2608.11025v2 Announce Type: replace
Abstract: Emergent misalignment (EM) is the phenomenon where fine-tuning a language model on a narrow task leads to harmful behavior in unrelated domains. A...
By Clemens Vetter, David Kacz\'er, Lucie Flek, Florian Mai