Evaluating and inducing steganography in LLMs

Artem Karpov

This report demonstrates that large language models are capable

of hiding simple 8 bit information in their output using associations

from more powerful overseers (other LLMs or humans). Without

direct steganography fine tuning, LLAMA 3 8B can guess a 8 bit

hidden message in a plain text in most cases (69%), however a

more capable model, GPT-3.5 was able to catch almost all of them

(84%). More research is required to investigate how this ability

might be induced or improved via RL training in similar and larger

models.

Reviewer's Comments

Reviewer's Comments

Arrow
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Rudolf L

The work is the start of a fairly ambitious project to do PPO fine-tuning of LLMs for steganography (as the work acknowledges, this is notoriously hard). This is definitely an interesting direction, and I appreciate that someone has actually gone and taken a stab at the hard thing.

Cite this work

@misc {

title={

@misc {

},

author={

Artem Karpov

},

date={

6/30/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.