# SEAL: Subspace-Anchored Watermarks for LLM Ownership

arXiv: https://arxiv.org/abs/2511.11356 · HTML: https://mlsanity.com/paper/2511.11356/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- First posted on arXiv 14 Nov 2025.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Built on by (most cited first, 4 of 4)

- [Echoes within the Reasoning: Stealthy and Effective Watermarking via Chain of Thought](https://mlsanity.com/paper/2605.28890/) (2026)
- [Functional Subspace Watermarking for Large Language Models](https://mlsanity.com/paper/2603.18793/) (2026)
- [R-CoT: A Reasoning-Layer Watermark via Redundant Chain-of-Thought in Large Language Models](https://mlsanity.com/paper/2604.25247/) (2026)
- [Steal the Knowledge, Inherit the Mark: TwinMark for Distillation Watermarking via Second Moments and Class Multiplexing](https://mlsanity.com/paper/2609.19011/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, demonstrating human-level performance in text generation, reasoning, and question answering. However, training such models requires substantial computational resources, large curated datasets, and sophisticated alignment procedures. As a result, they constitute highly valuable intellectual property (IP) assets that warrant robust protection mechanisms. Existing IP protection approaches suffer from critical limitations. Model fingerprinting techniques can identify model architectures but fail to establish ownership of specific model instances. In contrast, traditional backdoor-based watermarking methods embed behavioral anomalies that can be easily removed through common post-processing operations such as fine-tuning or knowledge distillation. We propose SEAL, a subspace-anchored watermarking framework that embeds multi-bit signatures directly into the model's latent representational space, supporting both white-box and black-box verification scenarios. Our approach leverages model editing techniques to align the hidden representations of selected anchor samples with predefined orthogonal bit vectors. This alignment embeds the watermark while preserving the model's original factual predictions, rendering the watermark functionally harmless and stealthy. We conduct comprehensive experiments on multiple benchmark datasets and six prominent LLMs, comparing SEAL with 11 existing fingerprinting and watermarking methods to demonstrate its superior effectiveness, fidelity, efficiency, and robustness. Furthermore, we evaluate SEAL under potential knowledgeable attacks and show that it maintains strong verification performance even when adversaries possess knowledge of the watermarking mechanism and the embedded signatures.