# Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study

arXiv: https://arxiv.org/abs/2605.14387 · HTML: https://mlsanity.com/paper/2605.14387/

## 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 11 of its 15 references that are in the map.
- First posted on arXiv 14 May 2026.

## Placed in

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

## Builds on (most cited first, 11 of 11)

- [Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities](https://mlsanity.com/paper/2412.14538/) (2024)
- [WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting](https://mlsanity.com/paper/2112.15363/) (2021)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Founding The Domain of AI Forensics](https://mlsanity.com/paper/1912.06497/) (2019)
- [The Model Inversion Eavesdropping Attack in Semantic Communication Systems](https://mlsanity.com/paper/2308.04304/) (2023)
- [Towards Secure Intelligent O-RAN Architecture: Vulnerabilities, Threats and Promising Technical Solutions using LLMs](https://mlsanity.com/paper/2411.08640/) (2024)
- [Hidden Backdoor Attack against Deep Learning-Based Wireless Signal Modulation Classifiers](https://mlsanity.com/paper/2306.10753/) (2023)
- [Security Risks in Vision-Based Beam Prediction: From Spatial Proxy Attacks to Feature Refinement](https://mlsanity.com/paper/2504.05222/) (2025)
- [FLARE: A Wireless Side-Channel Fingerprinting Attack on Federated Learning](https://mlsanity.com/paper/2512.10296/) (2025)
- [Detecting Backdoor Attacks via Similarity in Semantic Communication Systems](https://mlsanity.com/paper/2502.03721/) (2025)
- [Watermarking and Anomaly Detection in Machine Learning Models for LORA RF Fingerprinting](https://mlsanity.com/paper/2509.15170/) (2025)

## Abstract (arXiv)

As artificial intelligence (AI) is increasingly embedded in wireless networks, models are becoming core components that influence signal processing, resource scheduling and network control. However, model anomalies, tampering and malicious functions also introduce new security risks. In this article, we focus on model forensics in AI-native wireless networks. Specifically, we first discuss key problems including model authenticity verification, malicious function identification and accountability tracing, and summarize the main categories of model forensics. We then explain the role of model forensics in AI-native wireless networks and review representative application scenarios. In the case study, we use RF fingerprinting as an example and present two concrete workflows based on watermark authentication and backdoor detection, illustrating how provenance authentication and malicious behavior identification can be implemented in practice. The results show that model forensics can provide important support for anomaly assessment, provenance tracing and trustworthy operation in AI-native wireless networks. Finally, we outline several promising directions for future research in this emerging area.