mlsanityData to 7 Oct 2026

An Application-Layer Multi-Modal Covert-Channel Reference Monitor for LLM Agent Egress

arXiv 2605.20734 ↗First posted 20 May 2026cs.CR cs.AIcited by 2

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

A large language model (LLM) agent that sends messages can leak data inside them. Destination allowlists and content scanners do not police whether an otherwise-benign payload is itself a covert channel: a compromised agent encodes bits in zero-width characters, homoglyphs, whitespace, base64, JavaScript Object Notation (JSON) key ordering, message timing or size -- and, in binary egress, in least-significant-bit (LSB) pixel planes, per-image mean luminance, inter-image sequence permutation, ultrasonic tones, or audible-band sonified data. Our egress reference monitor has three contributions. (i) A text pipeline of ten capacity-reducing stages, a per-sink leaky-bucket capacity ledger, and a staged posture that enforces lossless stages from day one. (ii) Two media scramblers (a Fourier-domain audio band-limiter and a red-green-blue (RGB) image bit-depth and mean-luminance bucketer) gated by a boot-time cryptographic legitimacy attestation: an auditor publishes at boot the trusted Ed25519 keys and {kind, data-class} pairs; only payloads with a verifying signature for an authorized class are exempt. The attestation sidesteps the intractable content-based discrimination between real media and data sonified or rasterized as a carrier; unsigned media is suspect by default; a content-addressed canonicalizer closes the inter-image permutation channel. (iii) Residual capacity is the Miller--Madow corrected mutual information between embedded and recovered bits (zero when destroyed), measured by an adversarial ensemble of fifteen working encoders across text, image and audio. The reference implementation drives residual capacity to zero on every destroyable channel and to a stated bound on the one (per-image mean luminance) that cannot be destroyed without ruining the image.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 5 of its 30 references that are in the map. First posted on arXiv 20 May 2026.

Builds on

5 of 30 references.

References · 5

  1. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
  2. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,124
  3. 2019Neural Linguistic Steganographycited by 73
  4. 2022Perfectly Secure Steganography Using Minimum Entropy Couplingcited by 42
  5. 2021Trojan Source: Invisible Vulnerabilitiescited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

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  1. 2026Observable Channels, Not Just Storage: Evaluating Privacy Leakage in LLM Agent Pipelines
  2. 2026CIPL: A Channel-Aware Framework for Recoverable Privacy Leakage in LLM Agents
  3. 2026AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems
  4. 2026Feedback Coding Enables Inference-Time Covert Agentic Communication ↗
  5. 2025Audit the Whisper: Detecting Steganographic Collusion in Multi-Agent LLMs
  6. 2026Steganalysis of Adaptive Covert Collusion in Tool-Using Agent Populations: A Black-Box, Cross-Principal Approach
  7. 2026Codetta: High-Capacity, Keyless, and Undetectable Multi-Agent Collusion
  8. 2026You Told Me to Do It: Measuring Instructional Text-induced Private Data Leakage in LLM Agents