# NeuPerm: Disrupting Malware Hidden in Neural Network Parameters by Leveraging Permutation Symmetry

arXiv: https://arxiv.org/abs/2510.20367 · HTML: https://mlsanity.com/paper/2510.20367/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2025.

## Built on by (most cited first, 3 of 3)

- [SafePickle: Robust and Generic ML Detection of Malicious Pickle-based ML Models](https://mlsanity.com/paper/2602.19818/) (2026)
- [Permutation-Based Stegomalware in Large Language Models: Threats and Countermeasures](https://mlsanity.com/paper/2609.16193/) (2026)
- [High-Capacity Robust Medical Image Exfiltration via Neural Network Weight Replacement](https://mlsanity.com/paper/2609.31726/) (2026)

## Abstract (arXiv)

Pretrained deep learning model sharing holds tremendous value for researchers and enterprises alike. It allows them to apply deep learning by fine-tuning models at a fraction of the cost of training a brand-new model. However, model sharing exposes end-users to cyber threats that leverage the models for malicious purposes. Attackers can use model sharing by hiding self-executing malware inside neural network parameters and then distributing them for unsuspecting users to unknowingly directly execute them, or indirectly as a dependency in another software. In this work, we propose NeuPerm, a simple yet effec- tive way of disrupting such malware by leveraging the theoretical property of neural network permutation symmetry. Our method has little to no effect on model performance at all, and we empirically show it successfully disrupts state-of-the-art attacks that were only previously addressed using quantization, a highly complex process. NeuPerm is shown to work on LLMs, a feat that no other previous similar works have achieved. The source code is available at https://github.com/danigil/NeuPerm.git.