Knowledge Base Regulation Tracker Standards & Frameworks OSS Directory AI Incident Database Research Paper Library Case Studies Society Articles News Jobs About Charter Sign inBecome a Member
Home/Research Paper Library/Scalable watermarking for identifying large language model outputs

Scalable watermarking for identifying large language model outputs

DeepMind's SynthID-Text, the first production-deployed LLM watermark, published in Nature.

synthidwatermarkingdeepmind

Contribute

Spot an error, or know a record we should add? Members can propose additions and submit corrections.

Submit researchReport a correction or request removal

Related in Research Paper Library

ALIBI: Adversarial Legitimacy Injection in Binary Input against LLM Malware Analyzers

Large language models are being integrated into malware triage workflows as reasoning components that summarize static evidence and produce analyst-facing verdicts. This paper shows that the same reasoning capability…

Watermarking and provenance2026

Origin Is All You Need: Provenance-Aware Transformers for Structural Trust-Boundary Separation

Indirect prompt injection (IPI) remains a central safety and security challenge for large language model (LLM) systems because standard transformers lack architectural notion of source authority. Retrieved documents,…

Watermarking and provenance2026

MarkSec: Capability-Aware Evaluation of Adversarial Attacks Against LLM Watermarks

LLM watermarking helps trace the origin of generated text, but faces stealing attacks that recover watermark information, scrubbing attacks that remove watermark signals, and spoofing attacks that forge text accepted…

Watermarking and provenance2026