Abstract: This paper investigates the critical challenge of detecting fabricated link attacks in low-latency Agentic network environments, where conventional detection methods face significant limitations. The fundamental difficulty stems from three key factors: 1) the measured performance differences between normal and fabricated links often exhibit marginal variations that fall below meaningful detection thresholds; 2) dynamic background traffic patterns introduce substantial interference that obscures genuine attack signatures; and 3) coordinated overload attacks deliberately distort measurement accuracy. These factors collectively degrade the detection performance of existing methods by significantly reducing the observable distinction between legitimate and malicious links. To address these challenges, we propose a novel three-phase detection framework based on correlated link verification. The first phase implements an effective delay transformation method that reduces measurement errors while converting raw measurements into meaningful link performance metrics. The second phase employs a multipath transmission simulation technique that amplifies subtle performance disparities between correlated links into statistically observable features. The final phase establishes robust detection thresholds through the combined application of extreme value theory and probability distribution fitting methods. Comprehensive simulation results validate that our proposed method consistently outperforms existing approaches across diverse network scales and attack scenarios. The framework demonstrates particular effectiveness in low-latency agentic environments, maintaining superior detection performance while meeting stringent timing requirements. Specifically, the framework achieves a 20% to 30% improvement in F1 score compared to state-of-the-art methods. Notably, even under ultra-low latency conditions (e.g., 0.1ms link delay), the proposed method maintains a high detection rate of 95.23% with a false positive rate as low as 0.28%. These advancements provide a practical solution for securing next-generation autonomous networks against sophisticated link fabrication attacks.