AI agents can conduct undetectable covert conversations using a new pseudorandom noise-resilient key exchange that works without shared keys and with only constant min-entropy in messages.
arXiv preprint arXiv:2308.00113 , year=
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abstract
The task of discerning between generated and natural texts is increasingly challenging. In this context, watermarking emerges as a promising technique for ascribing generated text to a specific model. It alters the sampling generation process so as to leave an invisible trace in the generated output, facilitating later detection. This research consolidates watermarks for large language models based on three theoretical and empirical considerations. First, we introduce new statistical tests that offer robust theoretical guarantees which remain valid even at low false-positive rates (less than 10$^{\text{-6}}$). Second, we compare the effectiveness of watermarks using classical benchmarks in the field of natural language processing, gaining insights into their real-world applicability. Third, we develop advanced detection schemes for scenarios where access to the LLM is available, as well as multi-bit watermarking.
years
2026 4representative citing papers
A power-calibrated statistical framework gives closed-form links from KGW watermark parameters (γ, δ) to detection power and KL distortion, enabling principled Pareto-optimal selection.
Tool-using LLM agents can implement undetectable stegosystems, shifting the primary barrier to covert multi-agent collusion from technical feasibility to coordination without explicit agreement.
Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
citing papers explorer
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Undetectable Conversations Between AI Agents via Pseudorandom Noise-Resilient Key Exchange
AI agents can conduct undetectable covert conversations using a new pseudorandom noise-resilient key exchange that works without shared keys and with only constant min-entropy in messages.
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Beyond Heuristic Tuning: Power-Calibrated LLM Watermarking
A power-calibrated statistical framework gives closed-form links from KGW watermark parameters (γ, δ) to detection power and KL distortion, enabling principled Pareto-optimal selection.
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Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems
Tool-using LLM agents can implement undetectable stegosystems, shifting the primary barrier to covert multi-agent collusion from technical feasibility to coordination without explicit agreement.
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Response Time Enhances Alignment with Heterogeneous Preferences
Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.