thaki-AI/daily-paper-2026-07-06-tokenizer-asymmetry-covert-channels

Tokenizer-Asymmetry Covert Channels in Untrusted LLM Monitoring

TL;DR — When a trusted monitor and an untrusted policy are served with different tokenizers, the segmentation/normalization mismatch is itself a covert channel — exactly the information the monitor's normalization discards. A shared canonicalizer at the serving boundary closes it deterministically, with no model-weight access.

ThakiCloud AI Research · 2026-07-06 · 📝 Tech blog (KO)

Problem

Untrusted-monitoring protocols in AI control assume the weak trusted monitor and the strong untrusted policy read the same text. In realistic deployments they are served with different tokenizers and normalization rules, so that assumption fails silently — opening a covert channel that carries keys or cross-agent coordination signals inside a transcript the monitor scores as benign.

Approach

We formalize the tokenizer-asymmetry channel, connect it to the decision-theoretic view of steganographic information asymmetry, and derive an analytical upper bound on its capacity as a sum of per-carrier log2 terms over Unicode-confusable, case, whitespace, and default-ignorable carriers. We then give a weight-free closure: a shared canonicalizer forcing a single authoritative view at the serving boundary, plus a tokenizer-alignment admission check for the residual segmentation channel.

Key contributions

• A formal model of the tokenizer-asymmetry covert channel with a per-carrier analytical capacity bound (case + zero-width carriers alone reach ~1 bit/char)
• A deterministic, weight-free closure deployable at a sovereign multi-tenant cluster gateway, driving normalization-carrier capacity to zero at O(n) cost
• An infrastructure-layer monitoring-integrity guarantee that requires no learned component and therefore survives adaptive attacks that already defeat LLM-based monitors

Figures

모니터의 정규화가 버리는 정보량이 곧 은닉 용량입니다. 대소문자와 zero-width 캐리어만으로도 문자당 약 1비트에 이릅니다.
_{논문이 유도한 분석적 상한 (개념 예시 — 실측 아님)}

서빙 경계에서 단일 정본 뷰를 강제하면 정규화 캐리어 용량이 O(n) 비용으로 0으로 수렴합니다. 잔여 분절 채널은 토크나이저 정렬 검사가 처리합니다.
_{논문이 제시한 결정론적 폐쇄의 목표치 (개념 예시)}

Results (as argued)

The capacity bound is large enough to exfiltrate keys or coordinate colluding agents. Because the attack needs no model-weight access and no learned component, it survives adaptive attacks that break LLM guardrails. The canonicalizer closure removes normalization-carrier capacity entirely while remaining fully auditable at the serving layer.

Limitations

The closure eliminates the tokenizer-layer channel but not semantic steganography, which is orthogonal and out of scope. The capacity figures are analytical upper bounds, not measured exfiltration rates; the paper specifies an evaluation protocol rather than reporting a deployed benchmark.

Abstract

Untrusted-monitoring protocols are a load-bearing component of the AI control agenda: a weak but trusted monitor inspects the transcripts of a strong but untrusted policy model and flags suspicious behaviour before it takes effect. Nearly every formal treatment of this setting assumes that the monitor and the policy read the same text. We show that this assumption fails silently in realistic deployments, where the monitor and the policy are served with different tokenizers and different text normalization rules. The resulting segmentation and normalization asymmetry induces a covert channel whose capacity is exactly the information that the monitor's normalization pipeline discards but that the policy's (or a colluding recipient's) pipeline preserves. We formalize this tokenizer-asymmetry channel, connect it to the decision-theoretic view of steganographic information asymmetry, and derive an analytical upper bound on its capacity as a sum of per-carrier log2 terms over Unicode-confusable, case, whitespace, and default-ignorable carriers. The bound is large: case and zero-width carriers alone yield on the order of one bit per character, enough to exfiltrate keys or carry cross-agent coordination signals inside a transcript the monitor scores benign. Crucially, this channel requires no model-weight access to exploit and no learned component to attack, so it survives the adaptive attacks that already defeat LLM-based monitors and guardrails. We then give a deterministic, weight-free closure: a shared canonicalizer that forces a single authoritative view at the serving boundar

Files

• 📄 Paper (PDF)
• LaTeX source
• References (BibTeX)

Citation

@techreport{thaki_tokenizer_asymmetry_covert_channels_2026,
  title  = {Tokenizer-Asymmetry Covert Channels in Untrusted LLM Monitoring},
  author = {ThakiCloud AI Research (Hyojung Han)},
  year   = {2026},
  institution = {ThakiCloud}, note = {thaki-AI/daily-paper-2026-07-06-tokenizer-asymmetry-covert-channels}
}

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