Watermark Stealing in Large Language Models
Nikola Jovanović Robin Staab Martin Vechev
LLM watermarking has attracted attention as a promising way to detect
AI-generated content, with some works suggesting that current schemes may
already be fit for deployment. In this work we dispute this claim, identifying
watermark stealing (WS) as a fundamental vulnerability of these schemes. We
show that querying the API of the watermarked LLM to approximately
reverse-engineer a watermark enables practical spoofing attacks, as suggested
in prior work, but also greatly boosts scrubbing attacks, which was previously
unnoticed. We are the first to propose an automated WS algorithm and use it in
the first comprehensive study of spoofing and scrubbing in realistic settings.
We show that for under $50 an attacker can both spoof and scrub
state-of-the-art schemes previously considered safe, with average success rate
of over 80%. Our findings challenge common beliefs about LLM watermarking,
stressing the need for more robust schemes. We make all our code and additional
examples available at https://watermark-stealing.org.