# Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery

arXiv: https://arxiv.org/abs/2604.01049 · HTML: https://mlsanity.com/paper/2604.01049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.06439/) (2022)
- [Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services](https://mlsanity.com/paper/2202.06435/) (2022)
- [Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks](https://mlsanity.com/paper/2206.11328/) (2022)
- [How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learning](https://mlsanity.com/paper/2101.05768/) (2021)
- [Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles](https://mlsanity.com/paper/2201.11295/) (2022)
- [A Comprehensive Tutorial and Survey of O-RAN: Exploring Slicing-aware Architecture, Deployment Options, Use Cases, and Challenges](https://mlsanity.com/paper/2405.03555/) (2024)
- [Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning](https://mlsanity.com/paper/2505.14581/) (2025)
- [Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks](https://mlsanity.com/paper/2507.06997/) (2025)
- [Open RAN Slicing with Quantum Optimization](https://mlsanity.com/paper/2502.00142/) (2025)
- [Optimizing Cognitive Networks: Reinforcement Learning Meets Energy Harvesting Over Cascaded Channels](https://mlsanity.com/paper/2507.06981/) (2025)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [RSLAQ -- A Robust SLA-driven 6G O-RAN QoS xApp using deep reinforcement learning](https://mlsanity.com/paper/2504.09187/) (2025)
- [Rogue Cell: Adversarial Attack and Defense in Untrusted O-RAN Setup Exploiting the Traffic Steering xApp](https://mlsanity.com/paper/2505.01816/) (2025)
- [Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks](https://mlsanity.com/paper/2604.01238/) (2026)
- [Network Slicing Resource Management in Uplink User-Centric Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2601.12687/) (2026)

## Abstract (arXiv)

Next-generation (NextG) cellular networks are designed to support emerging applications with diverse data rate and latency requirements, such as immersive multimedia services and large-scale Internet of Things deployments. A key enabling mechanism is radio access network (RAN) slicing, which dynamically partitions radio resources into virtual resource blocks to efficiently serve heterogeneous traffic classes, including enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). In this paper, we study the impact of adversarial attacks on AI-driven RAN slicing decisions, where a budget-constrained adversary selectively jams slice transmissions to bias deep reinforcement learning (DRL)-based resource allocation, and quantify the resulting service level agreement (SLA) violations and post-attack recovery behavior. Our results indicate that budget-constrained adversarial jamming can induce severe and slice-dependent steady-state SLA violations. Moreover, the DRL agent's reward converges toward the clean baseline only after a non-negligible recovery period.