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Research record / 06

February–March 2026

AI-Native 6G Security Threat Analysis

Research and threat analysis examining security risks in AI-integrated RAN and 6G environments; it was not a deployed system.

State
Research
Earlier work

Research is presented by verified scope and disclosure boundary—not implied deployment.

Technologies and themes

  • AI security
  • 6G
  • Threat analysis
  • Wireless security

AI-security threat analysis

AI-native network security begins by treating data, model, and feedback integrity as one trust boundary—and by keeping recommended controls distinct from evidence of implementation.

Conceptual threat-analysis fieldText-led · not system architecture, telemetry, or deployment evidence

  1. 01 / Trust boundary

    Treat data and model integrity as part of the network boundary

    The February–March 2026 analysis examined data poisoning, model manipulation, and adversarial inputs where learned systems may influence AI-integrated RAN and 6G decisions.

  2. 02 / Feedback risk

    Follow compromise through the closed loop

    The research traced federated-learning exposure, compromised feedback loops, and closed-loop instability as threat-analysis conditions. These were analyzed risks, not observed incidents or measured system behavior.

  3. 03 / Defensive interpretation

    Map model risk to verifiable control directions

    The analysis recommended model-integrity verification, adversarial robustness, continuous monitoring, and zero-trust isolation as defensive directions; it did not claim those controls were implemented or validated.