# Robust Anomaly Detection in O-RAN: Leveraging LLMs against Data Manipulation Attacks

arXiv: https://arxiv.org/abs/2508.08029 · HTML: https://mlsanity.com/paper/2508.08029/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation](https://mlsanity.com/paper/2110.10617/) (2021)
- [System-level Analysis of Adversarial Attacks and Defenses on Intelligence in O-RAN based Cellular Networks](https://mlsanity.com/paper/2402.06846/) (2024)
- [AdaptOver: Adaptive Overshadowing Attacks in Cellular Networks](https://mlsanity.com/paper/2106.05039/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Family of Open Time-Series Foundation Models for the Radio Access Network](https://mlsanity.com/paper/2604.04271/) (2026)
- [From Description to Detection: LLM based Extendable O-RAN Compliant Blind DoS Detection in 5G and Beyond](https://mlsanity.com/paper/2510.06530/) (2025)

## Abstract (arXiv)

The introduction of 5G and the Open Radio Access Network (O-RAN) architecture has enabled more flexible and intelligent network deployments. However, the increased complexity and openness of these architectures also introduce novel security challenges, such as data manipulation attacks on the semi-standardised Shared Data Layer (SDL) within the O-RAN platform through malicious xApps. In particular, malicious xApps can exploit this vulnerability by introducing subtle Unicode-wise alterations (hypoglyphs) into the data that are being used by traditional machine learning (ML)-based anomaly detection methods. These Unicode-wise manipulations can potentially bypass detection and cause failures in anomaly detection systems based on traditional ML, such as AutoEncoders, which are unable to process hypoglyphed data without crashing. We investigate the use of Large Language Models (LLMs) for anomaly detection within the O-RAN architecture to address this challenge. We demonstrate that LLM-based xApps maintain robust operational performance and are capable of processing manipulated messages without crashing. While initial detection accuracy requires further improvements, our results highlight the robustness of LLMs to adversarial attacks such as hypoglyphs in input data. There is potential to use their adaptability through prompt engineering to further improve the accuracy, although this requires further research. Additionally, we show that LLMs achieve low detection latency (under 0.07 seconds), making them suitable for Near-Real-Time (Near-RT) RIC deployments.