AdaptOver: Adaptive Overshadowing Attacks in Cellular Networks
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Abstract (arXiv)
In cellular networks, attacks on the communication link between a mobile device and the core network significantly impact privacy and availability. Up until now, fake base stations have been required to execute such attacks. Since they require a continuously high output power to attract victims, they are limited in range and can be easily detected both by operators and dedicated apps on users' smartphones. This paper introduces AdaptOver - a MITM attack system designed for cellular networks, specifically for LTE and 5G-NSA. AdaptOver allows an adversary to decode, overshadow (replace) and inject arbitrary messages over the air in either direction between the network and the mobile device. Using overshadowing, AdaptOver can cause a persistent (≥ 12h) DoS or a privacy leak by triggering a UE to transmit its persistent identifier (IMSI) in plain text. These attacks can be launched against all users within a cell or specifically target a victim based on its phone number. We implement AdaptOver using a software-defined radio and a low-cost amplification setup. We demonstrate the effects and practicality of the attacks on a live operational LTE and 5G-NSA network with a wide range of smartphones. Our experiments show that AdaptOver can launch an attack on a victim more than 3.8km away from the attacker. Given its practicability and efficiency, AdaptOver shows that existing countermeasures that are focused on fake base stations are no longer sufficient, marking a paradigm shift for designing security mechanisms in cellular networks.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Jun 2021.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 3.
4 papers.
- 2025Robust Anomaly Detection in O-RAN: Leveraging LLMs against Data Manipulation Attackscited by 2
- 2025MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANscited by 2
- 2023Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systemscited by 1
- 2025From Description to Detection: LLM based Extendable O-RAN Compliant Blind DoS Detection in 5G and Beyondcited by 1
- 2025From Description to Detection: LLM based Extendable O-RAN Compliant Blind DoS Detection in 5G and Beyond8 Oct 2025
- 2025MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANs25 Sep 2025
- 2025Robust Anomaly Detection in O-RAN: Leveraging LLMs against Data Manipulation Attacks11 Aug 2025
- 2023Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems1 Nov 2023