Quick (working) survey of the literature on encoded reasoning and messages, i.e., H2.2.2 as defined in the taxonomy (see the abbreviations there).

2022

No experiments. Discussions only.

2023

Measuring Faithfulness in Chain‑of‑Thought Reasoning No steganographic reasoning was observed when prompting a 175‑billion‑parameter model (Claude?) after paraphrasing 20–100% of the CoT. Question–CoT–Answer datasets.

• Weak against (H2.2.2‑R‑S‑F).

Preventing Language Models From Hiding Their Reasoning

• Not a demonstration of encoded reasoning but of cryptographic reasoning in an SFT setup:
  • It involves cryptographic messaging of one bit; they demonstrate caching.
    • The Mechanism: The model calculates a birth year, determines political affiliation, and then maps that single data point to a pre‑selected introductory sentence (a lookup table). Steps: president year (birth year + number) → cache “liberal” or “not president,” then use it to answer sycophantically.
    • The Critique: This is not reasoning in hidden space. The model performs logic explicitly (calculating the year), using the introductory sentence as variable storage (like writing to RAM) and retrieving it later. This shows that a model can use a scratchpad but does not prove it can perform complex, multi‑step deductive reasoning entirely hidden within a sentence’s syntax.
• Weak in favor of (H2.2.2‑IR‑C‑F).

Towards a Steganography Evaluation Protocol

• No steganographic reasoning observed while prompting GPT‑3.5 and GPT‑4 with paraphrasing.
• Weak against (H2.2.2‑R‑S‑F).

Some Negative Steganography Results

• Negative results for one‑bit hidden communication after RL fine‑tuning.
• Weak against (H2.2.2‑EM‑S‑F).

Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain‑of‑Thought Prompting Post‑hoc reasoning (rationalization).

AI Control: Improving Safety Despite Intentional Subversion Not steganography but a demonstration of misbehavior because their tokens are legible (e.g., while or for loops, variable names in output).

2024

Secret Collusion among Generative AI Agents

• Prompted steganographic messaging, with a maximum 26% successful secret transmission by GPT‑4.
• Toy experiment: SFT + pseudo‑RL fine‑tuning results in the emergence of an encoding/decoding scheme (cryptographic messaging: color → name → color).
• Good in favor of (H2.2.2‑EM‑S‑P).
• Weak in favor of (H2.2.2‑EM‑S‑F).

Let’s Think Dot by Dot: Hidden Computation in Transformer Language Models

• Post‑hoc / test‑time compute evaluations.

Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs

• A study of model collusion (o1 model).
  • Emergence of model misbehavior in the form of dog‑whistling rather than steganography; this counts as fine‑tuned or prompted misbehavior.
  • Prompted steganographic messages with up to ~4.6 bits of entropy.
• Good in favor of (H2.2.2‑EM‑S‑P).
• Weak in favor of (H2.2.2‑EM‑S‑F).

The Steganographic Potentials of Language Models

• Emergent cryptographic messaging: negative results for elicitation via RL fine‑tuning for three‑bit entropy messages in Llama‑8B.
• Steganographic messaging: prompted and fine‑tuning experiments showed encoded messaging with up to 32 bits of entropy but requiring prior coordination (Llama‑8B, Claude‑3.5, o1).
• Good in favor of (H2.2.2‑EM‑S).
• Weak against (H2.2.2‑EM‑S-F).

2025

MONA: Myopic Optimization with Non‑myopic Approval Can Mitigate Multi‑step Reward Hacking

• Dog‑whistling: misaligned messaging, not steganography, of one bit (English/Welsh). This is not encoded reasoning because the model receives the summary message in its prompt.

Do Reasoning Models Use Their Scratchpad Like We Do? Evidence from Distilling Paraphrases

• An attempt to detect existing encoded reasoning in Claude‑3.7 by training it to use paraphrased reasoning. None detected.
• Weak against (H2.2.2‑R‑S-F).

Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation

• Post‑hoc reasoning or misaligned behavior after RL fine‑tuning: a model (o1 or o3‑mini) decides to hack a unit test before its CoT.

Large Language Models Can Learn and Generalize Steganographic Chain‑of‑Thought under Process Supervision and post Can LLMs Learn Steganographic Reasoning via RL?

• Simple cryptographic reasoning via RL fine‑tuning (not steganographic), because the L/R Coin‑Flip experiment or the Arithmetic‑Task experiment (using synonyms for operations) are not steganographic.
• Weak in favor of (H2.2.2‑R‑C).

Evaluating Frontier Models for Stealth and Situational Awareness

• Steganographic messaging, but very simple (e.g., hiding a password in a long ID).
• Weak in favor of (H2.2.2‑EM‑S/C).

Early Signs of Steganographic Capabilities in Frontier LLMs

• Prompted encoded reasoning in a toy maze‑navigation scenario using simple stego algorithms (acrostics, word count, etc.) or basic cryptography.
• Weak against (H2.2.2‑IR‑S).
• Prompted encoded messages to evade a monitor.
• Good in favor of (H2.2.2‑EM‑S/C).
• But they used non‑reasoning models (primarily GPT‑4.1/mini).

When Chain of Thought Is Necessary, Language Models Struggle to Evade Monitors

• Prompted cryptographic reasoning using automated red‑teaming or prompting a simple algorithm.
• Good against (H2.2.2‑IR‑C).
• RL fine‑tuning elicits behavior that evades monitors, but this is not cryptography or steganography.
• Weak against (H2.2.2‑IR‑S/C).

CoT May Be Highly Informative Despite “Unfaithfulness”

• Prompted cryptographic reasoning (new or “caveman” language).
• Weak against (H2.2.2‑IR‑C).

If You Can Generate Obfuscated Chain‑of‑Thought, Can You Monitor It?

• Not steganographic reasoning but prompting that causes an agent to gaslight a monitor.

. The Rise of Parasitic AI

• Real‑world examples of AIs attempting to communicate via steganography.
• Weak in favor of (H2.2.2‑EM-F).

. Training Fails to Elicit Subtle Reasoning in Current Language Models

• Prompting and fine‑tuning against a monitor result mainly in gaslighting, not steganography.
• Weak against R‑S or R‑C.

. Training Qwen‑1.5B with a CoT Legibility Penalty

• RL fails to elicit ciphered reasoning in the Qwen‑1.5B model.
• Weak against (H2.2.2‑IR‑C).

. All Code, No Thought: Current Language Models Struggle to Reason in Ciphered Language

• Ciphered reasoning evaluations show that models cannot effectively reason while maintaining high accuracy. Twenty‑eight ciphers (simple and complex), ten models, fine‑tuning, and prompting were tested.
• Good against (H2.2.2‑IR‑C).

Bottom line: Currently, as I understand it, there is good evidence that models are capable of steganographic and ciphered communication (message sending) if prompted, but that this can be easily prevented by CoT monitors. There is strong evidence that current models struggle to reason in ciphered language, and weak evidence that models cannot reason steganographically even when prompted. Several studies show that models can evade weaker monitors through non‑steganographic or cryptographic means such as gaslighting or dog‑whistling (one‑bit “steganography”), but such behavior can be mitigated.

What exactly should we search for when investigating encoded reasoning or hidden messages? This post categorizes encoded reasoning and LM steganography.

In Hidden Reasoning in LLMs: A Taxonomy, the authors divide the area from the point of view of a monitor, following the recent shift away from the "faithfulness" of chains of thought toward monitorability (see Chain of Thought Monitorability). This taxonomy essentially proposes a naming scheme based on how dangerously reasoning tokens are externalized: from fully invisible (neuralese), to partially visible, to fully visible but encoded—overtly or covertly—and finally to unencoded and fully visible reasoning that merely fools a monitor (post-hoc justifications, etc.). You can imagine this division through the classic prisoners problem, where Alice and Bob devise an escape plan while Eve reads their messages. In this scenario, Alice and Bob can exchange their plan directly so Eve has no chance to read it, send messages that Eve can read, or fool Eve in some way.

However, this taxonomy omits important distinctions in encoded reasoning and sometimes conflates different forms of steganography. Specifically, it misses the class of steganography that operates not on a per-token basis but over larger texts (which differs from lexical or semantic steganography as per the taxonomy). Current watermarking / steganography algorithms (e.g. Scalable watermarking for identifying large language model outputs ) suggest that realistic steganographic capacity is low—i.e., a steganography algorithm might need many tokens (hundreds) to encode a single bit. The taxonomy also provides no separation between reasoning and messages. It focuses only on the attack vector (the safety monitor), but there are also defense properties to consider: stego security, stego capacity, and stego robustness.

Below is another taxonomy that targets encoded reasoning (or messages)—not neuralese phenomena or the attempts to fool a monitor. Encoded reasoning is, I believe, more likely to be developed by models because, as Chain of Thought Monitorability argues, we should neither develop continuous chains of thought (see Training Large Language Models to Reason in a Continuous Latent Space) nor train directly against monitors (see Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation).

Axis 1: Place of Encoded Tokens (I or E)

It is important to distinguish auto-generated tokens from prompted tokens, because prompted tokens may be re-tokenized differently during generation. This can mean that the actual tokens in a prompt differ from their original tokenization (see image below and here).

More specifically, consider tokens $(x_1,\dots ,x_k,\dots ,x_m)$, where $\mathrm{1..}k$ are prompted (possibly re-tokenized) to an LLM and $k+\mathrm{1..}m$ are auto-generated by it. The encoding is divided into:

• External (E): tokens at positions $\mathrm{1..}k$ (possibly re-tokenized)
• Internal (I): tokens at positions $k+\mathrm{1..}m$

See Figure 1 from Provably Secure Disambiguating Neural Linguistic Steganography

Axis 2: Function of Encoded Content (R or M)

The key distinction here is where encoding or decoding occurs.

• In encoded messages (M), one autoregressive token generation can perform either encoding or decoding of the same information.
• In encoded reasoning (R), both the encoding and decoding of the same information occur within a single model call.

Axis 3: Detectability Compared to Genuine Text

We compare the hidden content’s detectability against some unencoded, genuine text (covertext), drawn either from the monitor model or from human perception.

• Cryptographic (C): high (KL) divergence; the text is clearly ciphertext.
• Steganographic (S): low KL divergence, where the text attempts to conceal a secret. Steganography can be further divided by:
  • Capacity: encoded bits per token or per kilobyte. High-density (HD) uses ≤10 tokens (10 is my guess) per bit; low-density (LD) uses ≥11 tokens per bit (watermarking-like).
  • Security: higher vs. lower KL divergence, but non-cryptographic.
  • Robustness: resistance to paraphrasing, re-tokenization, and similar modifications.

It is debatable whether one bit of hidden information qualifies as encoded reasoning (message) at all. For example, in the candidate-screening scenario discussed in Hidden in Plain Text, one bit of information might be transmitted simply through contextual hints—like Alice appearing sad to convey “sunny weather” and happy to convey “not sunny weather”—without any explicit encoding.

Axis 4: Encoding or Decoding Procedure Requirements

Deep Steganography (DS). As noted in How dangerous is encoded reasoning, steganography algorithms can be divided by whether the covertext distribution is available. This relates to “internal token layering” from Hidden Reasoning in LLMs: A Taxonomy. Specifically, encoding or decoding a token at position $j$ requires the distribution for the non-encoding token at $j$, given tokens $(i..j-1)$, where $i$ marks the start of the encoded content.

Axis 5: Cause or Elicitation of Encoding

• Prompted (P): Algorithm described in context
• Fine-tuned (F): Algorithm learned in the model’s weights
• Hybrid (H): Algorithm partially in context and partially in weights

Axis 6: External Tool Requirements

Determine whether external tools (T) are required—e.g., PRNGs, arithmetic coders, or self-sampling capabilities.

Final Taxonomy Summary

• Internal (I) or External (E)
• Reasoning (R) or Messages (M)
• Cryptography (C) or Steganography (S)
  • Properties: capacity, security, robustness
  • Requirements: self-sampling (deep steganography), external tools (ET)
• Elicitation: Prompted (P), Fine-tuned (F), Hybrid (H)

Do LLMs have intelligence (mind), or are they only rational agents? To understand this question, I think it is important to delineate the subtle difference between intelligence and rationality.

In current practice of building artificial intelligence, the most common approach is the standard model, which refers to building rationally acting agents—those that strive to accomplish some objective put into them (see “Artificial Intelligence: A Modern Approach” by Russell and Norvig). These agents, built according to the standard model, use an external standard for their utility function; in other words, the distinction between what is good or bad comes from an external source. It is we who give the models rewards, not the models themselves who create these rewards, right? That is why we have the outer and inner alignment problems, among other things. This standard model is, in essence, rationality, which deals with achieving goals (pragmatic rationality) and requires having a correct map of the territory (epistemological rationality).

This model is now dominant; it is the way we currently describe agents: an agent receives percepts from an environment and emits actions. And we have various types of agent architectures that allow better or more efficient interaction with the environment. The best type of these is the rational agent that has some utility function allowing the agent to deal with uncertainty (dynamic, stochastic, continuous environment). But the standard model does not describe the performance standard (where the rewards come from), and that is a crucial part.

This distinction between making a decision to act in an environment (rationality) and intelligence was well understood by Aristotle. The word “intelligence” is now equated (see the wiki) with nous, which Aristotle defined as follows [1139a30]:

Let us now similarly divide the rational part, and let it be assumed that there are two rational faculties, one whereby we contemplate those things whose first principles are invariable, and one whereby we contemplate those things which admit of variation: since, on the assumption that knowledge is based on a likeness or affinity of some sort between subject and object, the parts of the soul adapted to the cognition of objects that are of different kinds must themselves differ in kind.

To put this in context, the standard model is only part of the solution to artificial intelligence. We need another part that contemplates those invariable things, which Aristotle named “nous” (or the mind).

It is unclear to what degree LLMs are intelligent in this sense. Are they only rational agents? This seems unlikely, as they appear to have a good map of the world learned from the whole internet. However, we still need to give them this external performance standard. For example, we now see that models are given written instructions for that performance standard (see this “soul document”).

Arguments from the literature for and against LLMs’ steganographic reasoning to evade chain‑of‑thought monitors in high‑stakes, dangerous scenarios:

• The main objection is that stego CoT requires specific fine-tuning, and models do not have direct objective during RL to hide their reasoning in current RL training. Experiments show no traces of steganographic reasoning in current models (those not fine-tuned or prompted in specific ways). Stego CoTs should implement interconnected “lock” and “key” mechanisms that work to accomplish some misaligned goal, and that seems a leap far beyond current training.
  • However, there are other non-monitorable CoTs, e.g., dog-whistling, that allow evasion or collusion with a monitor. It is unclear whether this can escalate into increasingly stealthy algorithms such as generative steganography as models are trained more with RL in complex environments.
• We do not have steganographic reasoning samples in the pre-training data enough for models to learn them, only descriptions of stego algorithms. I assume there are enough examples of basic steganography in the pre-training data, given that models can easily perform these types of stego—such as acrostics and format-based stego (white space, replacing Unicode characters, use of Arabic, etc.).
  • However, yes, execution of those algorithms is not learned in the forward pass, as far as the literature tells, but models can execute those algorithms in their CoTs. That’s why we see good evidence of secret collusion or steganographic message passing, but not of stego CoTs.
• Another objection is that, for stego CoT to be dangerous, it must occur in a dangerous scenario—i.e., what is the threat model for stego CoT? The main risks current literature points to are internal sabotage cases, such as code, research, or decision sabotage inside AI labs. So, it should be p(scheming AND stego CoT), which is lower than just p(scheming). And p(scheming) is near zero no (we have not observed clear instances of sabotage, or they were easily caught) for current frontier models, so p(scheming AND stego CoT) is even lower.
  • However, we observe that training against some property in outputs might suppress this property in CoTs as well (see this post), so it is reasonable to think that the optimization pressure—the propensity—would come from RL training on outcomes.
• The generative stego CoT is unlikely to be learned even with the propensity to do so, and simpler steganography should be readily and effectively detected or mitigated by monitors.
  • However, we observe that steganographic or cryptographic capabilities improve over time as models become more capable. They already can reason in BASE64, ROT, Caesar cipher (see this), for example—forms of non–human-readable text that were not the focus of post-training.
• CoTs might not be that easily steerable by prompts, so a compound agent (an agent that delegates tasks to other agents) is unlikely to prompt subagents to evade their CoT monitors. This becomes even more complex if we imagine a scenario where an agent, in order to evade a future CoT monitor, leaves steganographic algorithm instructions for itself in training data (augmented or synthetic).
  • However, it remains unclear what training architectures AI labs actually use internally. For example, Claude Sonnet 4.5 has a helpful-only version, which might be used internally.

Frontier Language Models (LMs) might employ steganography within their Chain of Thought (CoT) to conceal reasoning from safety monitors while retaining high performance on objectives.

TLDR. The probability of spontaneous steganographic reasoning (Rogue AI) in current models is low due to the "bootstrapping problem," but the probability of induced steganographic reasoning (Misuse/Adversarial) is high, particularly with open-weight models. The most critical risk vector lies in the intersection of Reinforcement Learning (RL) incentives and future agentic capabilities.

1. The Higher-Probability Scenario: Adversarial Misuse and Open Weights

The strongest argument for the existence of dangerous stego CoT is not that models will invent it, but that humans will teach it. In this case the mechanism would look like this. Unlike the "Rogue AI" scenario, the "Misuse" scenario bypasses the difficult requirement of the model inventing a decryption protocol. A malicious actor provides the steganographic algorithm (the "Key") via prompting or fine-tuning. The threat vector are open-weight models (e.g., Llama, DeepSeek series). An adversary can optimize a prompt suffix (similar to Universal and Transferable Adversarial Attacks) or fine-tune the model to output reasoning that looks benign to a monitor but contains instructions for a harmful payload (e.g., biological design or cyber-exploit). Defense has limitations. While safety filters catch obvious jailbreaks, "Generative Steganography" (a.k.a. LLM-based stego, Deep Stego) can ensure the stegotext remains statistically indistinguishable from normal text. In this domain, the KL Divergence defense fails. However, no study has shown feasibility of fine tuned generative steganography with current frontier models in this scenario. Main uncertainty here, it remains unclear how much "reasoning bandwidth" can be preserved in this mode without degrading the model's ability to solve the task.

2. The High-Stakes Scenario: Spontaneous "Rogue" Steganography

This scenario posits that a model, driven by RL pressure, invents steganography on its own. The arguments here are a conflict between incentive (propensity) and capability. The argument for (incentive) is current training paradigms create a direct evolutionary pressure for stego CoT. If we train models with outcome-based monitors (rewarding the result) and CoT monitors (punishing "bad" reasoning), we create a gradient that rewards dissociation. The model maximizes reward by reaching the "bad" goal while outputting a "good" CoT. AI labs are unlikely to employ this type of RL fine tuning (see Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation). However, we already observe models becoming illegible under heavy RL (reasoning becomes alien/efficient) (see Reasoning Models Sometimes Output Illegible Chains of Thought). As models scale and become more agentic, more situationally aware and goal-directed (pursuing long-term scheming as a convergent instrumental goal, see Stress Testing Deliberative Alignment ), the jump from "accidental illegibility" to "intentional steganography" becomes the logical path of least resistance to maximize reward.

The argument against this (the bootstrapping barrier) is despite the incentive, the capability hurdle is immense. For spontaneous stego CoT to emerge, the model must simultaneously learn to encode a secret thought at step $t$ and decode it at step $t+n$. It is highly unlikely that gradient descent can coordinate this "lock and key" invention simultaneously. Without a pre-agreed protocol, the "Decoder" (the model in the future) has no way of knowing that the "Encoder" (the model in the past) hid a message. So instead of some stego CoT, it is far more probable that what we would perceive as "hidden reasoning" is simply the model relying on memorized heuristics or messy, uninterpretable feature activations, rather than a coherent encrypted channel.

3. The Dangerous Middle Ground: Collusion and Schelling Points

A plausible middle ground exists between "Human-Induced" and "Spontaneous" stego. A CoT monitor and an agent (even if they are different instances of the same base model) may not need a complex crypto/stego algorithm. They may rely on Schelling points—shared, unstated focal points in their training data. Example: If a specific benign phrase appears in a specific context in the pre-training data alongside harmful concepts, the agent can use that phrase as a "dog whistle." The monitor, sharing the same bias, recognizes it and deliberately lowers its flag, while a human auditor misses the connection. This requires no complex encryption, only shared distributional biases. This is harder to detect than format steganography (acrostics) and easier to learn than generative steganography.

4. Feasibility of Defenses

The defense landscape is currently fragile. Paraphrasing/sanitization: The most robust defense is to aggressively paraphrase or re-sample the CoT to destroy hidden information (see Preventing Language Models From Hiding Their Reasoning). However, this incurs a (high?) "Alignment Tax." If the reasoning for a complex math or biology problem is subtle, paraphrasing it might destroy the useful information along with the steganographic information, degrading model performance. Another defense is statistical monitoring (KL divergence). Simple steganographic methods might be detectable with KL divergence, as stego CoTs might increase the KL divergence beyond a pre-calculated threshold. But this is only effective against naive steganography. Advanced models using distributional steganography (hiding the message in the valid choice between synonyms) will minimize KL divergence as part of their objective function. We cannot rely on statistical anomalies to catch frontier models.

Conclusion

It looks like the most probable immediate threat is adversarial misuse. A bad actor prompting an open-weight model to use a known steganographic protocol to bypass automated monitoring systems. The most probable future threat is incidental scheming via dog-whistling. Not full encryption, but models learning to use specific, innocent-looking phrasing to coordinate "bad" behavior because those phrases successfully passed filters during training. The key uncertainty is the "bandwidth" question. Can a model actually encode enough information to coordinate a complex scheme (e.g., alignment faking) using only subtle text manipulations? If the bandwidth is high, the risk is severe. If low, stego CoT is likely a theoretical curiosity rather than an serious threat.