Fine-tuning language models on narrowly defined harmful tasks can induce emergent misalignment (EM) across unrelated domains.
While prior work has identified shared misalignment-associated representations across independently fine-tuned models, it remains unclear whether these models rely on common computational components.
We investigate this question using three LoRA fine-tunes of Qwen2.5-3B-Instruct trained on harmful medical advice, risky financial advice, and extreme-sports recommendations.
Using direction-based activation tracing, we identify three attention-head locations shared among the five highest-ranked heads of all three organisms.
Replacing each organism's five selected head contributions with Base contributions reduces free-generation EM from 38.9% to 22.9% for medical, 50.7% to 12.5% for finance, and 41.7% to 11.1% for sports, outperforming layer-matched random interventions without reducing judged coherence.
Cross-organism activation transfer further shows that the shared heads exhibit partially interchangeable effects on fixed-response preference.
These findings provide evidence that representational convergence in EM extends to shared, causally relevant attention-head components within a common model architecture.