mlsanityData to 14 Oct 2026

MARCO: The Radioactive Watermark for Protein Generative Models

arXiv 2610.08316 ↗First posted 6 Oct 2026cs.CR cs.AIcited by 0

On the map: LLM safety and security → Watermarking generated content and models

Abstract (arXiv)

Protein Generative Models (PGMs) have revolutionized structural biology by enabling the design of complex 3D protein structures from sequence data. However, this breakthrough introduces a dual-use challenge, exposing high-value PGMs to economic risks like unauthorized model extraction and biosecurity threats such as biohazard synthesis. To mitigate these threats, we propose MARCO (textscCOnformation waterMARk), the first radioactive watermarking framework specifically tailored for PGMs. MARCO establishes a Dual-Layer defense that simultaneously protects intellectual property and ensures the forensic traceability of potential biosecurity misuses. (i) To preserve efficiency, MARCO iteratively embeds watermarks during diffusion reverse denoising via an auxiliary encoder-decoder, allowing the original PGM parameters to remain frozen for broad compatibility. (ii) To preserve biophysical fidelity and maximize robustness, we employ specialized loss functions targeting Cα-atom pairwise distances and torsion angles (psi, φ) within an adversarial training framework integrated with stochastic attack simulations. (iii) Crucially, MARCO exhibits ``radioactivity'' where the watermark automatically transfers to the outputs of any pirate models trained on the watermarked data, effectively countering model extraction attacks. Comprehensive experiments demonstrate that MARCO achieves superior fidelity and robustness while successfully validating watermark transferability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security. Builds on 6 of its 57 references that are in the map. First posted on arXiv 6 Oct 2026.

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6 of 57 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA18 May
  2. 2023Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust31 May
  3. 2023Watermarking Diffusion Model21 May
  4. 2023A Recipe for Watermarking Diffusion Models17 Mar

Other references · 2

  1. 2023SE(3) diffusion model with application to protein backbone generationcited by 219
  2. 2025Generative AI for Biosciences: Emerging Threats and Roadmap to Biosecuritycited by 5

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Close in wording, no citation either way

  1. 2024FoldMark: Protecting Protein Generative Models with Watermarking
  2. 2026GenoTrace: Inheritable Watermarks for Genome Foundation Model Distillation
  3. 2026Secure Seed-Based Multi-bit Watermarking for Diffusion Models from First Principles
  4. 2026Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards
  5. 2025Mitigating Watermark Forgery in Generative Models via Randomized Key Selection
  6. 2025MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking
  7. 2025NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models
  8. 2026RaMark: Radioactive Watermarking for Generated Tabular Data