Covert Influence Between Language Models
For AI safety researchers, this work broadens the understanding of covert influence risks in language model interactions, though it is an incremental study of known phenomena with new attribution methods.
The paper characterizes the risk of covert influence between language models across three interfaces (supervised fine-tuning, on-policy distillation, in-context learning), finding that inference-time per-sample attribution scores can amplify payload transfers beyond prior work, and that natural-language carriers pose distinct risks compared to number carriers.
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 payload transfers that prior work could not achieve. We further provide evidence that covert influence with natural-language carriers is a distinct phenomenon from prior studies using number carriers, as the latter is more resistant to human detection and less portable across model families. Together, these results suggest that the risk surface for covert influence is broader than previously recognized, and we study pointwise attribution scoring methods as a tool to investigate and mitigate it.