Bookworm Game: Automatic Discovery of LTE Vulnerabilities Through Documentation Analysis

Yi Chen, Yepeng Yao, Xiaofeng Wang, Dandan Xu, Chang Yue, Xiaozhong Liu, Kai Chen, Haixu Tang, Baoxu Liu
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引用次数: 21

Abstract

In the past decade, the security of cellular networks has been increasingly under scrutiny, leading to the discovery of numerous vulnerabilities that expose the network and its users to a wide range of security risks, from denial of service to information leak. However, most of these findings have been made through ad-hoc manual analysis, which is inadequate for fundamentally enhancing the security assurance of a system as complex as the cellular network. An important observation is that the massive amount of technical documentation of cellular network can provide key insights into the protection it puts in place and help identify potential security flaws. Particularly, we found that such documentation often contains hazard indicators (HIs) – the statement that describes a risky operation (e.g., abort an ongoing procedure) when a certain event happens at a state, which can guide a test on the system to find out whether the operation can indeed be triggered by an unauthorized party to cause harm to the cellular core or legitimate users’ equipment. Based upon this observation, we present in this paper a new framework that makes the first step toward intelligent and systematic security analysis of cellular networks. Our approach, called Atomic, utilizes natural-language processing and machine learning techniques to scan a large amount of LTE documentation for HIs. The HIs discovered are further parsed and analyzed to recover state and event information for generating test cases. These test cases are further utilized to automatically construct tests in an LTE simulation environment, which runs the tests to detect the vulnerabilities in the LTE that allow the risky operations to happen without proper protection. In our research, we implemented Atomic and ran it on the LTE NAS specification, including 549 pages with 13,598 sentences and 283,850 words. In less than 5 hours, our prototype reported 42 vulnerabilities from 192 HIs discovered, including 10 never reported before, under two threat models. All these vulnerabilities have been confirmed through end-to-end attacks, which lead to unauthorized disruption of the LTE service a legitimate user’s equipment receives. We reported our findings to authorized parties and received their confirmation that these vulnerabilities indeed exist in major commercial carriers and $2,000 USD reward from Google.
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书虫游戏:通过文档分析自动发现LTE漏洞
在过去的十年中,蜂窝网络的安全性受到了越来越多的审查,导致发现了许多漏洞,这些漏洞使网络及其用户面临从拒绝服务到信息泄露的各种安全风险。然而,这些发现大多是通过特别的人工分析得出的,这不足以从根本上增强像蜂窝网络这样复杂的系统的安全保障。一个重要的观察是,大量的蜂窝网络技术文档可以提供关键的见解,以保护它到位,并帮助识别潜在的安全漏洞。特别是,我们发现此类文件通常包含危险指标(HIs) -描述在某个状态下发生特定事件时的危险操作(例如,中止正在进行的程序)的声明,这可以指导对系统的测试,以确定操作是否确实可以由未经授权的一方触发,对蜂窝核心或合法用户的设备造成伤害。基于这一观察,我们在本文中提出了一个新的框架,使蜂窝网络的智能和系统的安全分析迈出了第一步。我们的方法称为Atomic,利用自然语言处理和机器学习技术来扫描大量的LTE文档。对发现的HIs进行进一步的解析和分析,以恢复状态和事件信息,从而生成测试用例。这些测试用例进一步用于在LTE模拟环境中自动构建测试,该环境运行测试以检测LTE中的漏洞,这些漏洞允许在没有适当保护的情况下发生危险操作。在我们的研究中,我们实现了Atomic,并在LTE NAS规范上运行它,包括549页,13,598个句子和283,850个单词。在不到5个小时的时间里,我们的原型在两种威胁模型下报告了发现的192个he中的42个漏洞,其中包括10个以前从未报告过的漏洞。所有这些漏洞都是通过端到端攻击得到证实的,这些攻击会导致合法用户的设备接收到未经授权的LTE服务中断。我们向授权方报告了我们的发现,并得到了他们的确认,这些漏洞确实存在于主要的商业运营商中,并获得了谷歌2000美元的奖励。
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