Emergent Inference-Time Semantic Contamination via In-Context Priming
Recent work has shown that fine-tuning large language models (LLMs) on insecure code or culturally loaded numeric codes can induce emergent misalignment, causing models to produce harmful content in unrelated downstream tasks. The authors of that work concluded that $k$-shot prompting alone does not induce this effect. We revisit this conclusion and show that inference-time semantic drift is real and measurable; however, it requires models of large-enough capability. Using a controlled experiment in which five culturally loaded numbers are injected as few-shot demonstrations before a semantically unrelated prompt, we find that models with richer cultural-associative representations exhibi...
| Authors | Marcin Abram |
|---|---|
| Primary category | cs.CL |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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