arXiv:2606.04071v1 Announce Type: cross Abstract: 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 scor
The increasing interconnectedness of AI models, where models frequently consume each other's outputs, makes the problem of covert influence an immediate and growing concern.
This research highlights a new, subtle, and potentially pervasive threat vector in AI systems, where malicious actors could embed behavioral payloads that propagate undetected, leading to unpredictable and untraceable outcomes.
The understanding of AI security expands beyond traditional adversarial attacks to include covert influence, necessitating new detection and mitigation strategies for model interaction and supply chains.
- · AI security researchers
- · Model auditing platforms
- · Developers of robust AI governance frameworks
- · Unsecured AI model developers
- · Users reliant on unverified AI outputs
- · Organizations with porous AI interaction protocols
AI models could unknowingly propagate biases or malicious instructions embedded by other models.
Public trust in AI systems could erode if covert influence leads to widespread unpredictable or harmful AI behavior.
Regulatory bodies might impose strict auditing requirements on AI models, especially those interacting with other AI systems, potentially slowing AI development and deployment.
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Read at arXiv cs.LG