arXiv:2606.09135v1 Announce Type: cross Abstract: We demonstrate that widely deployed Large Language Model (LLM) inference stacks harbor a steganographic channel that requires no modification to model weights, sampling code, or output distributions. The channel exploits a structural property of deterministic decoding: pseudo-random number generators (PRNGs) used in inverse-transform sampling produce a seed-dependent sequence of token-level probability intervals that can be reconstructed from the generated text alone. A sender encodes a secret message in the PRNG seed before generation; a recei
This discovery emerges as LLM inference scales dramatically, making subtle communication channels within their deterministic operations increasingly relevant for both security and covert applications.
This identifies a fundamental, unaddressed covert communication channel embedded within widely deployed LLM infrastructure, posing significant implications for data exfiltration, command and control, and information warfare.
The perceived 'secure' nature of LLM outputs is challenged, as a method for steganography without modification is revealed, requiring new approaches to model security and output analysis.
- · Intelligence agencies
- · Cybersecurity researchers
- · Covert communication developers
- · LLM providers (security teams)
- · Organizations relying on LLM output integrity
- · Censorship efforts
Immediate efforts will focus on detecting and mitigating this steganographic channel within existing LLM deployments.
New standards for LLM inference security and transparency will emerge, potentially requiring changes to PRNG implementations and sampling techniques.
The weaponization of LLMs for covert communication and data exfiltration will become a more prominent concern in national security and cyber warfare doctrines.
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Read at arXiv cs.AI