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Conformance tests are critical for finding security weaknesses in carrier network systems. However, building a conformance test procedure from specifications is challenging, as indicated by the slow progress made by the 3GPP, particularly in developing security-related tests, even with a large amount of resources already committed. A unique challenge in building the procedure is that a testing system often cannot directly invoke the condition event in a security requirement or directly observe the occurrence of the operation expected to be triggered by the event. Addressing this issue requires an event chain to be found, which once initiated leads to a chain reaction so the testing system can either indirectly triggers the target event or indirectly observe the occurrence of the expected event. To find a solution to this problem and make progress towards a fully automated conformance test generation, we developed a new approach called Contester , which utilizes natural language processing and machine learning to build an event dependency graph from a 3GPP specification, and further perform automated reasoning on the graph to discover the event chains for a given security requirement. Such event chains are further converted by Contester into a conformance testing procedure, which is then executed by a testing system to evaluate the compliance of user equipment (UE) with the security requirement. Our evaluation shows that given 22 security requirements from the LTE NAS specifications, Contester successfully generated over a hundred test procedures in just 25 minutes. After running these procedures on 22 popular UEs including iPhone 13, Pixel 5a and IoT devices, our approach uncovered 197 security requirement violations, with 190 never reported before, rendering these devices to serious security risks such as MITM, fake base station and reply attacks. 

With the recent report of erroneous content in 3GPP specifications leading to real-world vulnerabilities, attention has been drawn to not only the specifications but also the way they are maintained and adopted by manufacturers and carriers. In this paper, we report the first study on this 3GPP ecosystem, for the purpose of understanding its security hazards. Our research leverages 414,488 Change Requests (CRs) that document the problems discovered from specifications and proposed changes, which provides valuable information about the security assurance of the 3GPP ecosystem. Analyzing these CRs is impeded by the challenge in finding security-relevant CRs (SR-CRs), whose security connections cannot be easily established by even human experts. To identify them, we developed a novel NLP/ML pipeline that utilizes a small set of positively labeled CRs to recover 1,270 high-confidence SR-CRs. Our measurement on them reveals serious consequences of specification errors and their causes, including design errors and presentation issues, particularly the pervasiveness of inconsistent descriptions (misalignment) in security-relevant content. Also important is the discovery of a security weakness inherent to the 3GPP ecosystem, which publishes an SR-CR long before the specification has been fixed and related systems have been patched. This opens an "attack window", which can be as long as 11 years! Interestingly, we found that some recently reported vulnerabilities are actually related to the CRs published years ago. Further, we identified a set of vulnerabilities affecting major carriers and mobile phones that have not been addressed even today. With the trend of SR-CRs not showing any sign of abating, we propose measures to improve the security assurance of the ecosystem, including responsible handling of SR-CRs.