AI driven semantic protocol attacks on 6G: Formalizing novel threat vectors and a lipschitz bounded cross layer defense framework (SACLDF)

Sixth generation (6G) semantic communications (SEMCOM) introduce AI driven protocol layers context mapping, ontology reasoning, and reinforcement learning based (RL) routing that expose a new class of adversarial vulnerabilities beyond conventional physical layer threats. Existing security research largely addresses physical layer attacks such as jamming and waveform inversion, leaving protocol layer mechanisms insufficiently protected. This paper formalizes three novel protocol layer attack vectors: context injection (FGSM/PGD based perturbation of semantic context functions), ontology poisoning (adversarial triple injection into shared knowledge graphs), and reward manipulation (deceptive RL signal distortion). End to end MATLAB simulations demonstrate severe impact: bit error rate (BER) degradation from 5.3 × 10−7 to 8.7 × 10−4 (AWGN), perceptual speech quality (PESQ) below intelligibility thresholds (1.3 under Rayleigh fading), and up to 12.4 dB signal to distortion ratio (SDR) loss. To counter these threats, we propose the Semantic Aware Cross Layer Defense Framework (SACLDF), unifying: (1) real time multi metric anomaly detection (SAAD), (2) Lipschitz constrained semantic reconstruction (CSAE) with a certified reconstruction bound (Theorem 1), (3) hybrid knowledge graph sanitization (KGG), and (4) trust aware policy adaptation (ARLD). SACLDF achieves a 93× BER reduction, restores PESQ to 3.2, and recovers 11.6 dB SDR while maintaining a gNB side latency of 4.7 ms and a memory footprint of 50MB meeting 6G URLLC constraints. An ablation study and comparative evaluation against standard adversarial training and robust routing baselines confirm the necessity of the cross layer architecture.