arXiv:2607.06647v1 Announce Type: cross Abstract: Open Radio Access Networks (O-RAN) increasingly delegate near-real-time control to deep reinforcement learning (DRL) xApps obtained from third-party vendors, creating a new supply-chain attack surface. A backdoor policy behaves optimally until an adversary injects a covert trigger into the observed key performance indicator (KPI) telemetry, at which point it issues harmful control actions that degrade quality of service (QoS). We present ORAN-DEFEND, a retraining-free wrapper that sanitizes a frozen, potentially compromised xApp by projecting e
The increasing reliance on third-party DRL xApps in Open RAN environments creates immediate vulnerabilities that researchers are now actively addressing.
This research highlights critical security risks in next-generation communication infrastructure, where AI-powered components can be backdoored to degrade essential services.
The ability to detect and sanitize compromised AI components without retraining introduces a new layer of trust and resilience in AI supply chains for critical infrastructure.
- · Telecommunications infrastructure providers
- · Open RAN vendors
- · Cybersecurity firms
- · National security agencies
- · Malicious state actors
- · Cyber criminals
- · Vulnerable Open RAN deployments
Increased confidence and adoption of AI-powered Open RAN solutions due to improved security protocols.
Development of industry standards and regulations for vetting and securing AI components in critical infrastructure.
The integration of real-time AI security monitoring as a standard feature in all distributed AI systems, not just Open RAN.
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Read at arXiv cs.LG