Covert Influence Between Language Models
As language models increasingly consume one another's outputs, covert influence -- a phenomenon where a sender's payload (the behavioral disposition it is conditioned to propagate) transfers to a receiver through carriers undetectable by humans -- becomes a growing risk. We characterize this risk across three interfaces: supervised fine-tuning, on-policy distillation, and in-context learning, and find that they vary in the scale of influence achievable without leaving behind human-visible traces. Using inference-time per-sample attribution scores, we study covert influence across all three interfaces with the ability to select carriers that amplify training-time influence, unlocking paylo...
| Authors | Avidan Shah, Jay Chooi, Jinghua Ou et al. |
|---|---|
| Primary category | cs.CR |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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