Black-Box Evasion Attacks on Data-Driven Open RAN Apps: Tailored Design and Experimental Evaluation
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2025
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| author | Gajjar, Pranshav Khoja, Molham Ganiyu, Abiodun Juarez, Marc Marina, Mahesh K. Lehane, Andrew Shah, Vijay K. |
| author_facet | Gajjar, Pranshav Khoja, Molham Ganiyu, Abiodun Juarez, Marc Marina, Mahesh K. Lehane, Andrew Shah, Vijay K. |
| contents | The impending adoption of Open Radio Access Network (O-RAN) is fueling innovation in the RAN towards data-driven operation. Unlike traditional RAN where the RAN data and its usage is restricted within proprietary and monolithic RAN equipment, the O-RAN architecture opens up access to RAN data via RAN intelligent controllers (RICs), to third-party machine learning (ML) powered applications - rApps and xApps - to optimize RAN operations. Consequently, a major focus has been placed on leveraging RAN data to unlock greater efficiency gains. However, there is an increasing recognition that RAN data access to apps could become a source of vulnerability and be exploited by malicious actors. Motivated by this, we carry out a comprehensive investigation of data vulnerabilities on both xApps and rApps, respectively hosted in Near- and Non-real-time (RT) RIC components of O-RAN. We qualitatively analyse the O-RAN security mechanisms and limitations for xApps and rApps, and consider a threat model informed by this analysis. We design a viable and effective black-box evasion attack strategy targeting O-RAN RIC Apps while accounting for the stringent timing constraints and attack effectiveness. The strategy employs four key techniques: the model cloning algorithm, input-specific perturbations, universal adversarial perturbations (UAPs), and targeted UAPs. This strategy targets ML models used by both xApps and rApps within the O-RAN system, aiming to degrade network performance. We validate the effectiveness of the designed evasion attack strategy and quantify the scale of performance degradation using a real-world O-RAN testbed and emulation environments. Evaluation is conducted using the Interference Classification xApp and the Power Saving rApp as representatives for near-RT and non-RT RICs. We also show that the attack strategy is effective against prominent defense techniques for adversarial ML. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18160 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Black-Box Evasion Attacks on Data-Driven Open RAN Apps: Tailored Design and Experimental Evaluation Gajjar, Pranshav Khoja, Molham Ganiyu, Abiodun Juarez, Marc Marina, Mahesh K. Lehane, Andrew Shah, Vijay K. Cryptography and Security Networking and Internet Architecture The impending adoption of Open Radio Access Network (O-RAN) is fueling innovation in the RAN towards data-driven operation. Unlike traditional RAN where the RAN data and its usage is restricted within proprietary and monolithic RAN equipment, the O-RAN architecture opens up access to RAN data via RAN intelligent controllers (RICs), to third-party machine learning (ML) powered applications - rApps and xApps - to optimize RAN operations. Consequently, a major focus has been placed on leveraging RAN data to unlock greater efficiency gains. However, there is an increasing recognition that RAN data access to apps could become a source of vulnerability and be exploited by malicious actors. Motivated by this, we carry out a comprehensive investigation of data vulnerabilities on both xApps and rApps, respectively hosted in Near- and Non-real-time (RT) RIC components of O-RAN. We qualitatively analyse the O-RAN security mechanisms and limitations for xApps and rApps, and consider a threat model informed by this analysis. We design a viable and effective black-box evasion attack strategy targeting O-RAN RIC Apps while accounting for the stringent timing constraints and attack effectiveness. The strategy employs four key techniques: the model cloning algorithm, input-specific perturbations, universal adversarial perturbations (UAPs), and targeted UAPs. This strategy targets ML models used by both xApps and rApps within the O-RAN system, aiming to degrade network performance. We validate the effectiveness of the designed evasion attack strategy and quantify the scale of performance degradation using a real-world O-RAN testbed and emulation environments. Evaluation is conducted using the Interference Classification xApp and the Power Saving rApp as representatives for near-RT and non-RT RICs. We also show that the attack strategy is effective against prominent defense techniques for adversarial ML. |
| title | Black-Box Evasion Attacks on Data-Driven Open RAN Apps: Tailored Design and Experimental Evaluation |
| topic | Cryptography and Security Networking and Internet Architecture |
| url | https://arxiv.org/abs/2510.18160 |