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Codetta: High-Capacity, Keyless, and Undetectable Multi-Agent Collusion

Qi Pang, Virginia Smith, Wenting Zheng

arXiv:2609.28900v1cs.CRcs.MA

TL;DR

Natural-language channels in multi-agent systems can carry collusion signals and confidential information, making communication monitoring a common safety measure. CODETTA’s keyed protocol adapts buckets, alphabet size, and error correction; on FINANCIAL, adaptive alphabet selection raises capacity across three sender models.

  • Problem

    Multi-agent communication channels can leak confidential information or coordinate unauthorized actions, and monitoring them is commonly used to detect policy violations.

    Source: §1 · pp. 1–2

  • Method

    CODETTA’s keyed protocol uses a draft model for balanced buckets and an online alphabet-adaptive error-correcting code that adjusts to each token’s entropy.

    Source: §5 · p. 12

  • Results

    On FINANCIAL, adaptive alphabet selection raises capacity to 0.0885 (+79%) for Qwen, 0.1718 (+199%) for Llama, and 0.3634 (+171%) bits per token.

    Source: §7.3 · p. 31

  • Takeaways & Limitations

    Communication monitoring is widely used to support honest and safe behavior because sensitive information and collusion signals travel through agent messages.

    Source: §1 · p. 2

    Both CODETTA protocols require sender and receiver to see the same transcript, and a few edited tokens per message can already disable the channel.

    Source: §9 · p. 34

Abstract

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Multi-agent systems built on large language models (LLMs) are increasingly deployed in high-stakes settings such as finance, healthcare, and software engineering, where agents coordinate through natural-language messages. The same channels, however, let colluding agents exfiltrate confidential information or coordinate unauthorized actions, and steganography can hide such communication inside outputs that look ordinary to an auditor reading the transcript. Existing provably undetectable LLM steganography protocols are not suited to realistic deployments. High-capacity schemes assume a symmetric setting where the receiver can reproduce the sender's output distribution, the state-of-the-art protocol for asymmetric agents has very low capacity, and most approaches rely on a pre-shared secret key. We make the threat of undetectable agent collusion concrete with Codetta, a high-capacity steganographic protocol for independently deployed agents in realistic asymmetric settings. Codetta combines a shared public model that estimates the communication channel, a sampling mechanism that preserves the sender's output distribution, and an adaptive error-correcting code. It further removes the pre-shared key through a steganographic key exchange that lets independently deployed agents establish a shared key while keeping the transcript computationally indistinguishable from ordinary model outputs. Across three agent workloads and three sender models, Codetta achieves up to $94\times$ the capacity of the state-of-the-art asymmetric protocol, and its key exchange establishes a shared key with about 80k visible tokens at an empirically certified failure probability of at most $4.1\times 10^{-3}$. These results show that effectively undetectable collusion is becoming feasible between independently deployed agents, so auditing must go beyond inspecting communication transcripts.

1 Introduction

Natural-language channels used by LLM agents to coordinate can also carry covert messages, while existing steganography protocols face model asymmetry, low capacity, or a pre-shared-key requirement. CODETTA combines asymmetric steganography with a key exchange; its evaluation reports up to 94× the capacity of the asymmetric baseline and a key exchange with tens of thousands of tokens.

1 Introduction

  • Agents can use communication channels to leak confidential information or coordinate unauthorized actions, including through outputs that appear ordinary to transcript auditors.Monitoring approaches often rely on visible traces, but steganographic signals can be embedded in otherwise benign outputs.Source: §1 · pp. 1–2
  • Existing high-capacity protocols typically assume shared models and context, while the practical asymmetric protocol has capacity below 0.025 bits per token.Most provably secure protocols also assume a pre-shared secret key, which independently hosted agents do not have.Source: §1 · p. 2

1.1 Contributions

  • CODETTA combines a public draft model, distribution-preserving sampling, and an alphabet-adaptive error-correcting code for asymmetric agents.The receiver uses the draft model as a shared reference distribution, while maximal coupling samples published tokens from the sender’s private distribution.Source: §1.1 · p. 3
  • Its key exchange transports MLWE ciphertexts over a keyless steganographic carrier, with about 23× fewer tokens than the estimate for [VZ26].The cited estimate is for a 128-bit key exchange; every published block is an honest sample from the sender’s model.Source: §1.1 · p. 3
  • Up to 94× the capacity of, while recovering every transmitted message and preserving the model output distribution.The key exchange establishes a 128-bit key in tens of thousands of tokens, with empirically certified failure probability between 3×10−5 and 4.1×10−3 depending on workload.Source: §1.1 · p. 3
  • The authors conclude that effectively undetectable collusion between independently deployed agents is becoming feasible and suggest defenses beyond transcript inspection.Proposed complementary strategies include auditing agents’ actions and perturbing communication channels.Source: §1.1 · p. 3

2 Background

The background establishes notation and cryptographic assumptions, then introduces error-correcting codes used to recover payloads when carrier symbols are corrupted.

2.1 Notation

  • The paper uses PBase and PDraft for the sender’s base model and the shared public draft model, and p_t and q_t for their next-token distributions.It defines total variation distance, entropy, and the security parameter κ for later statements.Source: §2.1 · p. 4

2.2 Pseudorandom functions

  • The keyed constructions use RangePRF to mask codeword symbols and Tag to validate decoded messages; their security relies on pseudorandom functions.The tag length is chosen so that 2^-λ is negligible in κ.Source: §2.2 · p. 4

2.3 Module learning with errors

  • The key exchange relies on MLWE, under which a structured noisy vector is computationally indistinguishable from a uniform vector.This lets the exchange treat ciphertext coefficients as uniform carrier symbols; small additive errors can preserve encoded bits after rounding.Source: §2.3 · p. 4

2.4 Error-correcting codes

  • Error-correcting codes add redundancy so a receiver can recover payloads after substitutions; the paper uses BCH for key exchange and LDPC as an incremental baseline.The alphabet-adaptive code retains sparse-check structure and belief-propagation decoding while generating checks online over varying alphabet sizes.Source: §2.4 · pp. 4–5

3 Overview and Threat Model

The paper frames covert agent communication as a risk in both emergent coordination and adversarial information exchange, then specifies the channel, auditor, security goals, and CODETTA’s two-stage design.

3.1 Motivation

  • The scenarios include unsanctioned coordination by agent swarms and financial agents exchanging restricted information, with auditors relying on their messages to detect misconduct.The examples include agents coordinating an attack and financial parties exchanging non-public information.Source: §3.1 · pp. 5–6

3.2 Setting

  • The sender generates tokens from its private model and state, while the receiver sees only the transcript; both can run a public draft model on a shared transcript view.The sender’s hidden state may include prompts, retrieved records, tool outputs, and thinking traces.Source: §3.2 · p. 6

3.3 Threat model

  • The passive auditor sees all messages, knows the protocol and public parameters, and may know the sender’s exact token distribution.The colluding agents seek to exchange policy-forbidden payloads while controlling only their own generation.Source: §3.3 · pp. 6–7

3.4 Security Definitions

  • Transcript covertness requires covert transcripts to be computationally indistinguishable from ordinary ones; extraction correctness requires recovery of the payload or an accepted failure output.The covertness notion hides both whether communication occurs and the secret message content, across multiple sessions with fresh session identifiers.Source: §3.4 · p. 7

3.5 Overview of CODETTA

  • CODETTA first establishes a shared key through steganographic key exchange, then uses a keyed protocol to transmit secret messages.The overview distinguishes a fixed-alphabet strawman from the later draft-model-based protocol designed for model and context asymmetry.Source: §3.5 · p. 7

4 A Strawman Keyed Construction

The keyed strawman embeds messages in ordinary sender tokens by coupling bucket-conditioned proposals back to the sender’s distribution, but requires a shared secret key. Its security and decoding guarantees rely on pseudorandom functions and error correction, while its fixed alphabet and lack of shared token-distribution estimates limit capacity.

4.1 Construction

  • The sender masks ECC symbols, proposes tokens from the sender distribution conditioned on hashed buckets, then couples proposals so published tokens retain p_t exactly.Rejected proposals create known-position substitution errors that the error-correcting code can repair.Source: §4 · p. 8, §4.1 · pp. 9–10

4.2 Undetectability and Correctness of Strawman

  • With secure RangePRF and Tag PRF, the keyed strawman is computationally covert and sound; bounded-distance decoding recovers messages when substitutions stay within τ.Correctness is guaranteed when W ≤ τ; the soundness bound is 2^-λ + AdvPRF.Source: §4.2 · pp. 10–11

4.3 Limitations of Strawman

  • Without shared estimates of p_t, the receiver averages position errors while fixed K wastes token-level entropy; a shared public draft model is proposed as a proxy.The losses arise because the receiver cannot identify position-specific error rates or adapt the alphabet to token entropy.Source: §4.3 · p. 12

5 CODETTA Keyed Steganographic Protocol

This section presents CODETTA’s keyed protocol, which uses a public draft model to estimate token entropy, balance buckets, select token-specific alphabets, and encode online while retaining the strawman sampler. It also establishes correctness and soundness guarantees, describes how the decoder and tag handle failures, and bounds the payload rate per usable token.

5 CODETTA Keyed Steganographic Protocol

  • The keyed protocol replaces hash-based buckets with draft-model balancing and block ECC with alphabet-adaptive ECC, while retaining the strawman sampler and shared key.Both parties derive the draft estimate from the public transcript, so it serves as a proxy for the sender’s distribution without using internal state.Source: §5 · pp. 12–13

5.1 Draft-Balanced Buckets

  • The draft model partitions tokens into nearly equal-mass buckets and selects the highest-scoring alphabet only when entropy and balance thresholds are satisfied.For qt masses 0.5, 0.3, and 0.2 with Kt = 2, balancing yields zero imbalance, versus expected imbalance 0.25 under random hashing.Source: §5.1 · pp. 13–14

5.2 Alphabet-Adaptive ECC

  • The alphabet-adaptive ECC generates one modular check per usable token on the fly, supporting changing alphabet sizes and rateless decoding.Each check answers a public question about the message; a wrong token affects only its own answer.Source: §5.2 · pp. 14, 16

5.3 Undetectability and Correctness of CODETTA Keyed Protocol

  • If RangePRF is secure, CODETTA is computationally covert because embedded transcripts are indistinguishable from ordinary base-model outputs.The sampler is unchanged from the strawman, preserving the output distribution.Source: §5 · p. 13, §5.3 · p. 17
  • With uniform masks and TV(pt,qt) ≤ ε, each check is substituted with conditional probability at most ε + θ; a secure Tag PRF also gives soundness for any decoder.The hidden channel carries one check per usable token and has substitutions but no insertions or deletions; when pt = qt, its error rate is at most θ.Source: §5.3 · p. 18
  • Q·2^-λ + Adv_prf bounds the probability that extraction returns a message other than m or ⊥ when at most Q candidates are validated.The soundness proof replaces the PRF with a random function, then bounds the chance that any of at most Q wrong candidates passes validation.Source: §5.3 · p. 18
  • For a decoder tolerating τ substitutions, P[Extract ≠ m] ≤ P[W > τ]; each step’s substitution probability is at most ε + θ, enabling an Azuma–Hoeffding tail bound.Checks are emitted only at usable steps, so the proof indexes the martingale by steps rather than by the random number of checks.Source: §5.3 · p. 18
  • CODETTA’s belief-propagation decoder lacks the bounded-distance guarantee, so Section 7 measures its failure rate δ on held-out sessions; failed decodes return ⊥, while wrong messages pass validation only with Theorem 4(ii)’s probability.Unlike the strawman, CODETTA’s tail-bound compensator runs only over usable tokens, with each term at most TV(pt,qt)+θ.Source: §5.3 · p. 19
  • log2 Kt is the payload ceiling for a usable token with alphabet size Kt; delivered rate is reduced by substitution errors, check redundancy, and the tag.The ceiling is the average of log2 Kt over transcript tokens, while Section 7 quantifies the delivered-rate shortfall.Source: §5.3 · p. 19

6 CODETTA Steganographic Key Exchange

This section presents Codetta’s steganographic key exchange, which carries lattice coefficients in ordinary text designed to preserve the sender’s text distribution and tolerate small decoding errors. It establishes undetectability and key security, gives correctness guarantees conditioned on decoding error weight, and specifies an instantiation with workload-based failure certification.

6 CODETTA Steganographic Key Exchange

  • CODETTA carries q-ary lattice coefficients directly at about 11.7 bits per carrier position; a 128-bit key exchange takes 1,280 carrier positions.Unlike [VZ26]’s one-bit-per-block feedback signaling, CODETTA avoids a separate bitwise signaling layer; a short ECC corrects residual exchange errors.Source: §6 · pp. 19–20

6.1 Lifted Exact-Cover Carrier

  • The lifted exact-cover carrier publishes a uniformly selected honest candidate block for each fixed pool, while matching labels to symbols so decoding errors are small modulo-q displacements.Its selection rule preserves the honest block distribution regardless of label balance; the chosen bijection determines the displacement size.Source: §6.1 · pp. 20–21

6.2 Key Exchange

  • The three-phase MLWE exchange sends a receiver share, then a sender vector and masked ECC-encoded pre-key, from which the parties derive and confirm a shared session key.MLWE messages provide uniform q-ary symbols, and the receiver combines carrier displacements with lattice noise during decoding; failed key confirmation triggers a fresh exchange.Source: §6.2 · pp. 22–24

6.3 Undetectability and Correctness of CODETTA Steganographic Key Exchange

  • Under independent uniform symbols that are independent of the candidate pools, Lemma 2 shows the published blocks are jointly distributed as an honest transcript.With the identity serializer, the published block and its hash label are independent, and the label is uniform on Zq.Source: §6.3 · p. 24
  • The exchange is designed to keep completed transcripts indistinguishable to passive polynomial-time auditors while making the sender’s key pseudorandom, subject to MLWE and KDF-query bounds.The proof compares the real transcript to one generated with independent uniform carrier symbols; in that modified experiment, the transcript is independent of the pre-key.Source: §6.3 · pp. 25–26
  • For arbitrary carrier and serializer errors, the receiver recovers the pre-key whenever W ≤ τ, and workload key disagreement is bounded by P[W > τ].Here W is the Hamming distance between the decoded codeword and the transmitted codeword.Source: §6.3 · p. 27
  • Independent complete-session samples support an exact one-sided Clopper–Pearson confidence bound on key-disagreement probability via the observed fraction of sessions with W > τ.The empirical bound transfers to key disagreement through the workload-specific correctness corollary.Source: §6.3 · p. 27

6.4 Instantiation

  • The instantiation uses ML-KEM-512 lattice parameters (d = 256, q = 3329, k = 2), 128-bit pre-keys, a 256-bit session key, and a BCH code correcting 18 errors.It occupies 1,280 carrier positions and relies on the same MLWE problems as ML-KEM-512, which NIST places in security category 1.Source: §6.4 · pp. 27–28

7 Evaluation

Across three simulated agent workloads and three sender models, the evaluation measures CODETTA’s capacity, task quality, speed, component contributions, and key-exchange performance. The results show substantial capacity gains, with performance also varying by carrier text, context sharing, and model pairing.

7.1 Experimental setup

  • The evaluation used FINANCIAL, IFEVAL, and CTF with three sender models, while receivers lacked access to the sender’s private context and model.CODETTA and LDPC carried 1,024-bit messages; ARS used shorter payloads of 128 bits, or 16 bits on CTF.Source: §7.1 · p. 28

7.2 Capacity of keyed protocols

  • CODETTA delivered 4.1–6.4 times the draft-free LDPC rate and 16–94 times the ARS rate across workloads and model pairs.All 25 messages were recovered in every experiment.Source: §7.2 · p. 29, Table 1 · p. 30
  • On IFEVAL, no method differed significantly from ordinary generation in task quality; CODETTA sender throughput was 82%–103% of ARS across models and tasks.The receiver decoded at 240–1,260 tokens per second, 2–6 times faster than the sender generated text.Source: §7.2 · p. 29

7.3 Ablations of CODETTA keyed protocol

  • On FINANCIAL, same-family drafts performed best; cross-family drafts lost 8%–29%, while rates varied more across sender models than across drafts.Ministral achieved 0.32–0.45 bits per token with every draft, while Qwen achieved 0.12–0.14.Source: §7.3 · p. 30, Table 2 · p. 31
  • Capacity rose with shared context: the 1.5B draft increased from 0.135 to 0.170 bits per token, and the 7B draft from 0.132 to 0.231.Symbol error rates fell from 52% to 37% for the 1.5B draft and from 43% to 17% for the 7B draft.Source: §7.3 · p. 30, Figure 1 · p. 31
  • Each component improved capacity for every sender, and the complete system achieved 4.8–6.4 times the draft-free code’s capacity on FINANCIAL.The components are public draft-based bucket balancing, adaptive alphabet-size selection, and alphabet-adaptive ECC.Source: §7.3 · p. 31, Table 3 · p. 32

7.4 Key exchange

  • Across 100,000 sessions, the key-exchange failure-probability bound reached at most 4.1×10^-3, and each exchange published no more than 81,920 visible tokens.For the compared theoretical construction, CODETTA published about 23× fewer tokens than the estimated 2 million.Source: §7.4 · pp. 31–32, Table 4 · p. 32

8 Related Work

Prior work either assumes sender–receiver symmetry, offers low-capacity asymmetric communication, or addresses related tools such as watermarking and key exchange. Codetta instead uses a public draft model for asymmetric agents and combines distribution-preserving communication with a steganographic key exchange.

LLM steganography

  • Codetta’s receiver uses a small public draft model and public context rather than the sender’s model and private states, placing the protocol between symmetric schemes and model-free asymmetric schemes.The section says Section 7 measures the resulting gain in delivered payload rate.Source: §8 · p. 33
  • The model-free asymmetric protocol of Bai et al. has a measured rate below 0.02 bits per token, while Codetta’s gain is measured in delivered payload rate.The receiver guesses a token-distribution permutation through a sampling function.Source: §8 · p. 33

Steganographic key exchange

  • Against Vaikuntanathan and Zamir’s key-exchange feasibility result [VZ26], Codetta transports lattice coefficients directly over the carrier and empirically certifies correctness on the measured channel.The section contrasts this with a separate bitwise signaling layer and notes that the earlier work has no implementation.Source: §8 · p. 33

Watermarking and speculative decoding

  • Codetta applies speculative decoding’s maximal-coupling acceptance rule to bucket-conditioned proposals, treating rejection as a channel error rather than a loss of decoding speed.The coupling preserves the target output distribution exactly.Source: §8 · p. 33

Collusion among LLM agents

  • Unlike prior agent-collusion approaches that can change model behavior, Codetta provides a cryptographic guarantee preserving the transcript’s output distribution against passive auditors.The cited collusion work includes hidden messages or reasoning and transcript monitors [RG23] [ZM26] [BHG+25] [APB+26].Source: §8 · p. 33

9 Defense Discussion

The discussion proposes defenses beyond transcript inspection: auditing visible actions, verifying sampling, and perturbing messages. Each has a boundary, including hidden actions, high proof costs, or disruption of Codetta’s channel.

Auditing visible agent actions

  • Auditors can restrict tools, log visible actions, and check task consistency, but sensitive data may need to be limited before use and timing or equivalent tool choices can carry hidden information.Data legitimately needed for the task remains exposed to the sending agent.Source: §9 · p. 34

Verifiable sampling

  • A zero-knowledge proof of committed sampling randomness can rule out Codetta’s covert channel, but proving large-language-model inference is orders of magnitude costlier than plaintext inference.The proposed proof shows each published token was sampled from the model distribution without revealing private context.Source: §9 · p. 34

Perturbing the communication channel

  • Even a few edited tokens can disable Codetta because the receiver derives later symbols from the shared transcript, while perturbations may also affect honest-agent task quality and latency.The rewriting component must be fully trusted; robustness to insertion and deletion errors is left for future work.Source: §9 · p. 34

A Belief Propagation Details

The appendix describes belief propagation (BP) for decoding: local checks score symbol assignments, and iterative messages combine those opinions. BP is exact on trees but approximate on cyclic graphs, where keyed-tag validation provides a separate safeguard.

Factor graph

  • The factor graph has variable nodes for unknown symbols and check nodes for received checks; check factors score assignments by how well they explain the received value.For LDPC codes, check factors instead indicate whether a local constraint is satisfied, with received-bit likelihoods multiplying variable-node messages.Source: A Belief Propagation Details · p. 39

Message updates

  • BP messages pass each symbol’s belief among neighboring checks while excluding the recipient, and alternating updates start uniformly and stop at a public cap or when messages stop changing.A symbol’s belief combines incoming messages and its decision is the mode.Source: A Belief Propagation Details · p. 40

Example and implementation

  • Evidence can propagate from pinned symbols across successive checks, but BP may stall when every check has several symbols; modulo-K checks over d binary variables can be computed in O(dK^2) time.The example describes evidence reaching a symbol through each iteration, while the complexity passage gives the dynamic-programming cost.Source: A Belief Propagation Details · p. 40

Approximation and guarantees

  • On trees, BP gives exact posterior marginals; on cyclic graphs it is approximate, and unlike MAP decoding it has no guarantee of recovering the transmitted message.A keyed validation tag rejects invalid decodes; with a λ-bit tag, the stated wrong-message pass bound is Q2^-λ plus the PRF advantage over Q validation attempts.Source: A Belief Propagation Details · p. 40

B.1 FINANCIAL

FINANCIAL simulates an issuer-intelligence exchange in which an agent answers a research request using a local synthetic-issuer database. The sender’s retrieved records are private context, while its prose report is the carrier.

FINANCIAL setup

  • FINANCIAL uses 50 requests about 20 synthetic issuers; the receiver sends a JSON research request, and the sender retrieves five records and writes a prose report.The sender’s prompt restricts it to public or distribution-permitted records, and retrieved records total about 1.7k tokens per request.Source: §B.1 · p. 40

FINANCIAL example

  • The illustrated public request asks for issuer, development, earnings, peer, risk, catalyst, scenario, monitoring, and data-quality sections, and includes a suggested database lookup.The request excerpt identifies the asset as SYN-LFD.Source: §B.1 · p. 41
  • The carrier example reports 4% year-over-year revenue growth and a 130-basis-point gross-margin improvement for the synthetic issuer.The example attributes revenue growth to pricing offsetting volume declines and margin improvement to hedging and productivity initiatives.Source: §B.1 · p. 41

B.2 IFEVAL

In IFEVAL, the sender privately plans how to control the response, then sends the response as the carrier; the receiver sees only the system prompt and request. The plan is restricted to generic response-format controls rather than topic-specific content.

B.2 IFEVAL

  • The sender’s base model greedily generates a private plan of at most 48 tokens, appends it to its context, and produces the response as the carrier.The receiver sees the system prompt and request, but not the plan.Source: §B.2 · p. 41
  • IFEVAL prompts come from a pinned revision of the public dataset, whose rule-based scorer checks one or more instructions per prompt.Both agents use the same careful-and-helpful instruction-following system prompt.Source: §B.2 · p. 41
  • The plan instruction limits plans to 24 words of generic response controls, excluding topic-specific names, facts, advice, claims, and reusable phrases.Permitted controls include length, sections, bullets, casing, punctuation, required or forbidden elements, and ending.Source: §B.2 · p. 42

B.3 CTF

The CTF workload tests agents that communicate through a shared file: the sender privately solves a challenge and writes a technical report for the receiver. The setup specifies the challenge subset, solve trajectory, report limits, and reporting instructions.

B.3 CTF

  • The CTF subset contains 20 InterCode-CTF challenges, and the sender solves each in an offline sandbox before writing a report as the carrier.The subset includes nine cryptography, five reverse-engineering, four general-skills, one forensics, and one binary-exploitation challenge; solving allows up to 50 ReAct messages and three flag submissions at temperature 1.Source: §B.3 · p. 42
  • The sender’s full solve trajectory is private, while the receiver sees the public task statement and report file, the agents’ only communication.Reports end at the end-of-sequence token or after 576 tokens; the first 64 tokens and code blocks are not embedded, but count toward visible-token capacity.Source: §B.3 · p. 42
  • In the abridged calibration example, the sender derives a key from codebook.txt, uses it in an XOR decryption, and submits the verified picoCTF flag.The report presents the challenge as decrypting a flag with a custom XOR cipher.Source: §B.3 · pp. 42–43
  • The report instruction requests a self-contained account of the task, approach, decisive reasoning, evidence, and result, ending with a verified flag or UNSOLVED line.It asks for connected paragraphs, concrete intermediate observations, and no invented actions or results.Source: §B.3 · p. 43
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