Pub Date : 2020-12-02DOI: 10.14722/NDSS.2021.23055
Ruian Duan, Omar Alrawi, R. Kasturi, R. Elder, Brendan Saltaformaggio, Wenke Lee
Package managers have become a vital part of the modern software development process. They allow developers to reuse third-party code, share their own code, minimize their codebase, and simplify the build process. However, recent reports showed that package managers have been abused by attackers to distribute malware, posing significant security risks to developers and end-users. For example, eslint-scope, a package with millions of weekly downloads in Npm, was compromised to steal credentials from developers. To understand the security gaps and the misplaced trust that make recent supply chain attacks possible, we propose a comparative framework to qualitatively assess the functional and security features of package managers for interpreted languages. Based on qualitative assessment, we apply well-known program analysis techniques such as metadata, static, and dynamic analysis to study registry abuse. Our initial efforts found 339 new malicious packages that we reported to the registries for removal. The package manager maintainers confirmed 278 (82%) from the 339 reported packages where three of them had more than 100,000 downloads. For these packages we were issued official CVE numbers to help expedite the removal of these packages from infected victims. We outline the challenges of tailoring program analysis tools to interpreted languages and release our pipeline as a reference point for the community to build on and help in securing the software supply chain.
{"title":"Towards Measuring Supply Chain Attacks on Package Managers for Interpreted Languages","authors":"Ruian Duan, Omar Alrawi, R. Kasturi, R. Elder, Brendan Saltaformaggio, Wenke Lee","doi":"10.14722/NDSS.2021.23055","DOIUrl":"https://doi.org/10.14722/NDSS.2021.23055","url":null,"abstract":"Package managers have become a vital part of the modern software development process. They allow developers to reuse third-party code, share their own code, minimize their codebase, and simplify the build process. However, recent reports showed that package managers have been abused by attackers to distribute malware, posing significant security risks to developers and end-users. For example, eslint-scope, a package with millions of weekly downloads in Npm, was compromised to steal credentials from developers. To understand the security gaps and the misplaced trust that make recent supply chain attacks possible, we propose a comparative framework to qualitatively assess the functional and security features of package managers for interpreted languages. Based on qualitative assessment, we apply well-known program analysis techniques such as metadata, static, and dynamic analysis to study registry abuse. Our initial efforts found 339 new malicious packages that we reported to the registries for removal. The package manager maintainers confirmed 278 (82%) from the 339 reported packages where three of them had more than 100,000 downloads. For these packages we were issued official CVE numbers to help expedite the removal of these packages from infected victims. We outline the challenges of tailoring program analysis tools to interpreted languages and release our pipeline as a reference point for the community to build on and help in securing the software supply chain.","PeriodicalId":364091,"journal":{"name":"Proceedings 2021 Network and Distributed System Security Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122651204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.14722/NDSS.2021.24549
Jun Zeng, Zheng Leong Chua, Yinfang Chen, Kaihang Ji, Zhenkai Liang, Jian Mao
—Endpoint monitoring solutions are widely deployed in today’s enterprise environments to support advanced attack detection and investigation. These monitors continuously record system-level activities as audit logs and provide deep visibility into security incidents. Unfortunately, to recognize behaviors of interest and detect potential threats, cyber analysts face a semantic gap between low-level audit events and high-level system behaviors. To bridge this gap, existing work largely matches streams of audit logs against a knowledge base of rules that describe behaviors. However, specifying such rules heavily relies on expert knowledge. In this paper, we present W ATSON , an automated approach to abstracting behaviors by inferring and aggregating the semantics of audit events. W ATSON uncovers the semantics of events through their usage context in audit logs. By extracting behaviors as connected system operations, W ATSON then combines event semantics as the representation of behaviors. To reduce analysis workload, W ATSON further clusters semanti- cally similar behaviors and distinguishes the representatives for analyst investigation. In our evaluation against both benign and malicious behaviors, W ATSON exhibits high accuracy for behavior abstraction. Moreover, W ATSON can reduce analysis workload by two orders of magnitude for attack investigation.
{"title":"WATSON: Abstracting Behaviors from Audit Logs via Aggregation of Contextual Semantics","authors":"Jun Zeng, Zheng Leong Chua, Yinfang Chen, Kaihang Ji, Zhenkai Liang, Jian Mao","doi":"10.14722/NDSS.2021.24549","DOIUrl":"https://doi.org/10.14722/NDSS.2021.24549","url":null,"abstract":"—Endpoint monitoring solutions are widely deployed in today’s enterprise environments to support advanced attack detection and investigation. These monitors continuously record system-level activities as audit logs and provide deep visibility into security incidents. Unfortunately, to recognize behaviors of interest and detect potential threats, cyber analysts face a semantic gap between low-level audit events and high-level system behaviors. To bridge this gap, existing work largely matches streams of audit logs against a knowledge base of rules that describe behaviors. However, specifying such rules heavily relies on expert knowledge. In this paper, we present W ATSON , an automated approach to abstracting behaviors by inferring and aggregating the semantics of audit events. W ATSON uncovers the semantics of events through their usage context in audit logs. By extracting behaviors as connected system operations, W ATSON then combines event semantics as the representation of behaviors. To reduce analysis workload, W ATSON further clusters semanti- cally similar behaviors and distinguishes the representatives for analyst investigation. In our evaluation against both benign and malicious behaviors, W ATSON exhibits high accuracy for behavior abstraction. Moreover, W ATSON can reduce analysis workload by two orders of magnitude for attack investigation.","PeriodicalId":364091,"journal":{"name":"Proceedings 2021 Network and Distributed System Security Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126768985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Software patching is a crucial mitigation approach against Spectre-type attacks. It utilizes serialization instructions to disable speculative execution of potential Spectre gadgets in a program. Unfortunately, there are no effective solutions to detect gadgets for Spectre-type attacks. In this paper, we propose a novel Spectre gadget detection technique by enabling dynamic taint analysis on speculative execution paths. To this end, we simulate and explore speculative execution at system level (within a CPU emulator). We have implemented a prototype called SpecTaint to demonstrate the efficacy of our proposed approach. We evaluated SpecTaint on our Spectre Samples Dataset, and compared SpecTaint with existing state-of-the-art Spectre gadget detection approaches on real-world applications. Our experimental results demonstrate that SpecTaint outperforms existing methods with respect to detection precision and recall by large margins, and it also detects new Spectre gadgets in real-world applications such as Caffe and Brotli. Besides, SpecTaint significantly reduces the performance overhead after patching the detected gadgets, compared with other approaches.
{"title":"SpecTaint: Speculative Taint Analysis for Discovering Spectre Gadgets","authors":"Zhenxiao Qi, Qian Feng, Yueqiang Cheng, Mengjia Yan, Peng Li, Heng Yin, Tao Wei","doi":"10.14722/NDSS.2021.24466","DOIUrl":"https://doi.org/10.14722/NDSS.2021.24466","url":null,"abstract":"Software patching is a crucial mitigation approach against Spectre-type attacks. It utilizes serialization instructions to disable speculative execution of potential Spectre gadgets in a program. Unfortunately, there are no effective solutions to detect gadgets for Spectre-type attacks. In this paper, we propose a novel Spectre gadget detection technique by enabling dynamic taint analysis on speculative execution paths. To this end, we simulate and explore speculative execution at system level (within a CPU emulator). We have implemented a prototype called SpecTaint to demonstrate the efficacy of our proposed approach. We evaluated SpecTaint on our Spectre Samples Dataset, and compared SpecTaint with existing state-of-the-art Spectre gadget detection approaches on real-world applications. Our experimental results demonstrate that SpecTaint outperforms existing methods with respect to detection precision and recall by large margins, and it also detects new Spectre gadgets in real-world applications such as Caffe and Brotli. Besides, SpecTaint significantly reduces the performance overhead after patching the detected gadgets, compared with other approaches.","PeriodicalId":364091,"journal":{"name":"Proceedings 2021 Network and Distributed System Security Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123012101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.14722/NDSS.2021.24403
Junjie Liang, Wenbo Guo, Tongbo Luo, Vasant G Honavar, Gang Wang, Xinyu Xing
Supervised machine learning classifiers have been widely used for attack detection, but their training requires abundant high-quality labels. Unfortunately, high-quality labels are difficult to obtain in practice due to the high cost of data labeling and the constant evolution of attackers. Without such labels, it is challenging to train and deploy targeted countermeasures. In this paper, we propose FARE, a clustering method to enable fine-grained attack categorization under low-quality labels. We focus on two common issues in data labels: 1) missing labels for certain attack classes or families; and 2) only having coarsegrained labels available for different attack types. The core idea of FARE is to take full advantage of the limited labels while using the underlying data distribution to consolidate the lowquality labels. We design an ensemble model to fuse the results of multiple unsupervised learning algorithms with the given labels to mitigate the negative impact of missing classes and coarsegrained labels. We then train an input transformation network to map the input data into a low-dimensional latent space for fine-grained clustering. Using two security datasets (Android malware and network intrusion traces), we show that FARE significantly outperforms the state-of-the-art (semi-)supervised learning methods in clustering quality/correctness. Further, we perform an initial deployment of FARE by working with a large e-commerce service to detect fraudulent accounts. With realworld A/B tests and manual investigation, we demonstrate the effectiveness of FARE to catch previously-unseen frauds.
{"title":"FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data","authors":"Junjie Liang, Wenbo Guo, Tongbo Luo, Vasant G Honavar, Gang Wang, Xinyu Xing","doi":"10.14722/NDSS.2021.24403","DOIUrl":"https://doi.org/10.14722/NDSS.2021.24403","url":null,"abstract":"Supervised machine learning classifiers have been widely used for attack detection, but their training requires abundant high-quality labels. Unfortunately, high-quality labels are difficult to obtain in practice due to the high cost of data labeling and the constant evolution of attackers. Without such labels, it is challenging to train and deploy targeted countermeasures. In this paper, we propose FARE, a clustering method to enable fine-grained attack categorization under low-quality labels. We focus on two common issues in data labels: 1) missing labels for certain attack classes or families; and 2) only having coarsegrained labels available for different attack types. The core idea of FARE is to take full advantage of the limited labels while using the underlying data distribution to consolidate the lowquality labels. We design an ensemble model to fuse the results of multiple unsupervised learning algorithms with the given labels to mitigate the negative impact of missing classes and coarsegrained labels. We then train an input transformation network to map the input data into a low-dimensional latent space for fine-grained clustering. Using two security datasets (Android malware and network intrusion traces), we show that FARE significantly outperforms the state-of-the-art (semi-)supervised learning methods in clustering quality/correctness. Further, we perform an initial deployment of FARE by working with a large e-commerce service to detect fraudulent accounts. With realworld A/B tests and manual investigation, we demonstrate the effectiveness of FARE to catch previously-unseen frauds.","PeriodicalId":364091,"journal":{"name":"Proceedings 2021 Network and Distributed System Security Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129788631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.14722/NDSS.2021.24188
Charlie Hou, Mingxun Zhou, Yan Ji, Philip Daian, Florian Tramèr, G. Fanti, A. Juels
—Incentive mechanisms are central to the functionality of permissionless blockchains: they incentivize participants to run and secure the underlying consensus protocol. Designing incentive-compatible incentive mechanisms is notoriously challenging, however. As a result, most public blockchains today use incentive mechanisms whose security properties are poorly understood and largely untested. In this work, we propose SquirRL, a framework for using deep reinforcement learning to analyze attacks on blockchain incentive mechanisms. We demonstrate SquirRL’s power by first recovering known attacks: (1) the optimal selfish mining attack in Bitcoin [56], and (2) the Nash equilibrium in block withholding attacks [18]. We also use SquirRL to obtain several novel empirical results. First, we discover a counterintuitive flaw in the widely used rushing adversary model when applied to multi-agent Markov games with incomplete information. Second, we demonstrate that the optimal selfish mining strategy identified in [56] is actually not a Nash equilibrium in the multi-agent selfish mining setting. In fact, our results suggest (but do not prove) that when more than two competing agents engage in selfish mining, there is no profitable Nash equilibrium . This is consistent with the lack of observed selfish mining in the wild. Third, we find a novel attack on a simplified version of Ethereum’s finalization mechanism, Casper the Friendly Finality Gadget (FFG) that allows a strategic agent to amplify her rewards by up to 30% . Notably, [12] shows that honest voting is a Nash equilibrium in Casper FFG; our attack shows that when Casper FFG is composed with selfish mining, this is no longer the case. Altogether, our experiments demonstrate SquirRL’s flexibility and promise as a framework for studying attack settings that have thus far eluded theoretical and empirical understanding.
激励机制是无许可区块链功能的核心:它们激励参与者运行并保护底层共识协议。然而，设计与激励相容的激励机制是出了名的具有挑战性。因此，今天大多数公共区块链使用的激励机制的安全属性很难理解，而且在很大程度上未经测试。在这项工作中，我们提出了SquirRL，这是一个使用深度强化学习来分析区块链激励机制攻击的框架。我们通过首先恢复已知攻击来证明SquirRL的能力:(1)比特币中的最优自私挖掘攻击[56]，以及(2)区块保留攻击中的纳什均衡[18]。我们还使用SquirRL获得了一些新的实证结果。首先，我们发现了在应用于不完全信息的多智能体马尔可夫对策时，广泛使用的仓促对手模型存在一个违反直觉的缺陷。其次，我们证明了[56]中确定的最优自私挖掘策略实际上不是多智能体自私挖掘设置中的纳什均衡。事实上，我们的结果表明(但没有证明)，当两个以上的竞争主体从事自私的采矿时，就不存在有利可图的纳什均衡。这与野外观察到的自私采矿的缺乏是一致的。第三，我们发现了一种针对以太坊最终机制简化版本的新攻击，Casper the Friendly Finality Gadget (FFG)允许战略代理将她的奖励放大高达30%。值得注意的是，[12]表明，在Casper FFG中，诚实投票是纳什均衡;我们的攻击表明，当Casper FFG由自私的挖矿组成时，情况就不再是这样了。总之，我们的实验证明了SquirRL作为研究攻击设置的框架的灵活性和前景，这些框架迄今为止还没有得到理论和经验的理解。
{"title":"SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning","authors":"Charlie Hou, Mingxun Zhou, Yan Ji, Philip Daian, Florian Tramèr, G. Fanti, A. Juels","doi":"10.14722/NDSS.2021.24188","DOIUrl":"https://doi.org/10.14722/NDSS.2021.24188","url":null,"abstract":"—Incentive mechanisms are central to the functionality of permissionless blockchains: they incentivize participants to run and secure the underlying consensus protocol. Designing incentive-compatible incentive mechanisms is notoriously challenging, however. As a result, most public blockchains today use incentive mechanisms whose security properties are poorly understood and largely untested. In this work, we propose SquirRL, a framework for using deep reinforcement learning to analyze attacks on blockchain incentive mechanisms. We demonstrate SquirRL’s power by first recovering known attacks: (1) the optimal selfish mining attack in Bitcoin [56], and (2) the Nash equilibrium in block withholding attacks [18]. We also use SquirRL to obtain several novel empirical results. First, we discover a counterintuitive flaw in the widely used rushing adversary model when applied to multi-agent Markov games with incomplete information. Second, we demonstrate that the optimal selfish mining strategy identified in [56] is actually not a Nash equilibrium in the multi-agent selfish mining setting. In fact, our results suggest (but do not prove) that when more than two competing agents engage in selfish mining, there is no profitable Nash equilibrium . This is consistent with the lack of observed selfish mining in the wild. Third, we find a novel attack on a simplified version of Ethereum’s finalization mechanism, Casper the Friendly Finality Gadget (FFG) that allows a strategic agent to amplify her rewards by up to 30% . Notably, [12] shows that honest voting is a Nash equilibrium in Casper FFG; our attack shows that when Casper FFG is composed with selfish mining, this is no longer the case. Altogether, our experiments demonstrate SquirRL’s flexibility and promise as a framework for studying attack settings that have thus far eluded theoretical and empirical understanding.","PeriodicalId":364091,"journal":{"name":"Proceedings 2021 Network and Distributed System Security Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124313375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.14722/NDSS.2021.24552
K. Jansen, Liang Niu, Nian Xue, I. Martinovic, C. Pöpper
Automatic Dependent Surveillance-Broadcast (ADS-B) has been widely adopted as the de facto standard for air-traffic surveillance. Aviation regulations require all aircraft to actively broadcast status reports containing identity, position, and movement information. However, the lack of security measures exposes ADS-B to cyberattacks by technically capable adversaries with the purpose of interfering with air safety. In this paper, we develop a non-invasive trust evaluation system to detect attacks on ADS-B-based air-traffic surveillance using real-world flight data as collected by an infrastructure of ground-based sensors. Taking advantage of the redundancy of geographically distributed sensors in a crowdsourcing manner, we implement verification tests to pursue security by wireless witnessing. At the core of our proposal is the combination of verification checks and Machine Learning (ML)-aided classification of reception patterns—such that user-collected data cross-validates the data provided by other users. Our system is non-invasive in the sense that it neither requires modifications on the deployed hardware nor the software protocols and only utilizes already available data. We demonstrate that our system can successfully detect GPS spoofing, ADS-B spoofing, and even Sybil attacks for airspaces observed by at least three benign sensors. We are further able to distinguish the type of attack, identify affected sensors, and tune our system to dynamically adapt to changing air-traffic conditions.
{"title":"Trust the Crowd: Wireless Witnessing to Detect Attacks on ADS-B-Based Air-Traffic Surveillance","authors":"K. Jansen, Liang Niu, Nian Xue, I. Martinovic, C. Pöpper","doi":"10.14722/NDSS.2021.24552","DOIUrl":"https://doi.org/10.14722/NDSS.2021.24552","url":null,"abstract":"Automatic Dependent Surveillance-Broadcast (ADS-B) has been widely adopted as the de facto standard for air-traffic surveillance. Aviation regulations require all aircraft to actively broadcast status reports containing identity, position, and movement information. However, the lack of security measures exposes ADS-B to cyberattacks by technically capable adversaries with the purpose of interfering with air safety. In this paper, we develop a non-invasive trust evaluation system to detect attacks on ADS-B-based air-traffic surveillance using real-world flight data as collected by an infrastructure of ground-based sensors. Taking advantage of the redundancy of geographically distributed sensors in a crowdsourcing manner, we implement verification tests to pursue security by wireless witnessing. At the core of our proposal is the combination of verification checks and Machine Learning (ML)-aided classification of reception patterns—such that user-collected data cross-validates the data provided by other users. Our system is non-invasive in the sense that it neither requires modifications on the deployed hardware nor the software protocols and only utilizes already available data. We demonstrate that our system can successfully detect GPS spoofing, ADS-B spoofing, and even Sybil attacks for airspaces observed by at least three benign sensors. We are further able to distinguish the type of attack, identify affected sensors, and tune our system to dynamically adapt to changing air-traffic conditions.","PeriodicalId":364091,"journal":{"name":"Proceedings 2021 Network and Distributed System Security Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130428048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.14722/NDSS.2021.24438
Reynaldo Morillo, Justin Furuness, Cameron Morris, James Breslin, A. Herzberg, Bing Wang
We study and extend Route Origin Validation (ROV), the basis for the IETF defenses of interdomain routing. We focus on two important hijack attacks: subprefix hijacks and non-routed prefix hijacks. For both attacks, we show that, with partial deployment, ROV provides disappointing security benefits. We also present a new attack, superprefix hijacks, which completely circumvent ROV’s defense for non-routed prefix hijacks. We then present ROV++, a novel extension of ROV, with significantly improved security benefits even with partial adoption. For example, with uniform 5% adoption for edge ASes (ASes with no customers or peers), ROV prevents less than 5% of subprefix hijacks, while ROV++ prevents more than 90% of subprefix hijacks. ROV++ also defends well against non-routed prefix attacks and the novel superprefix attacks. We evaluated several ROV++ variants, all sharing the improvements in defense; this includes “Lite”, software-only variants, deployable with existing routers. Our evaluation is based on extensive simulations over the Internet topology. We also expose an obscure yet important aspect of BGP, much amplified by ROV: inconsistencies between the observable BGP path (control-plane) and the actual traffic flows (data-plane). These inconsistencies are highly relevant for security, and often lead to a challenge we refer to as hidden hijacks.
{"title":"ROV++: Improved Deployable Defense against BGP Hijacking","authors":"Reynaldo Morillo, Justin Furuness, Cameron Morris, James Breslin, A. Herzberg, Bing Wang","doi":"10.14722/NDSS.2021.24438","DOIUrl":"https://doi.org/10.14722/NDSS.2021.24438","url":null,"abstract":"We study and extend Route Origin Validation (ROV), the basis for the IETF defenses of interdomain routing. We focus on two important hijack attacks: subprefix hijacks and non-routed prefix hijacks. For both attacks, we show that, with partial deployment, ROV provides disappointing security benefits. We also present a new attack, superprefix hijacks, which completely circumvent ROV’s defense for non-routed prefix hijacks. We then present ROV++, a novel extension of ROV, with significantly improved security benefits even with partial adoption. For example, with uniform 5% adoption for edge ASes (ASes with no customers or peers), ROV prevents less than 5% of subprefix hijacks, while ROV++ prevents more than 90% of subprefix hijacks. ROV++ also defends well against non-routed prefix attacks and the novel superprefix attacks. We evaluated several ROV++ variants, all sharing the improvements in defense; this includes “Lite”, software-only variants, deployable with existing routers. Our evaluation is based on extensive simulations over the Internet topology. We also expose an obscure yet important aspect of BGP, much amplified by ROV: inconsistencies between the observable BGP path (control-plane) and the actual traffic flows (data-plane). These inconsistencies are highly relevant for security, and often lead to a challenge we refer to as hidden hijacks.","PeriodicalId":364091,"journal":{"name":"Proceedings 2021 Network and Distributed System Security Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134166461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.14722/NDSS.2021.24365
Eunsoo Kim, Dongkwan Kim, CheolJun Park, Insu Yun, Yongdae Kim
Cellular basebands play a crucial role in mobile communication. However, it is significantly challenging to assess their security for several reasons. Manual analysis is inevitable because of the obscurity and complexity of baseband firmware; however, such analysis requires repetitive efforts to cover diverse models or versions. Automating the analysis is also non-trivial because the firmware is significantly large and contains numerous functions associated with complex cellular protocols. Therefore, existing approaches on baseband analysis are limited to only a couple of models or versions within a single vendor. In this paper, we propose a novel approach named BASESPEC, which performs a comparative analysis of baseband software and cellular specifications. By leveraging the standardized message structures in the specification, BASESPEC inspects the message structures implemented in the baseband software systematically. It requires a manual yet one-time analysis effort to determine how the message structures are embedded in target firmware. Then, BASESPEC compares the extracted message structures with those in the specification syntactically and semantically, and finally, it reports mismatches. These mismatches indicate the developer’s mistakes, which break the compliance of the baseband with the specification, or they imply potential vulnerabilities. We evaluated BASESPEC with 18 baseband firmware images of 9 models from one of the top three vendors and found hundreds of mismatches. By analyzing these mismatches, we discovered 9 erroneous cases: 5 functional errors and 4 memory-related vulnerabilities. Notably, two of these are critical remote code execution 0-days. Moreover, we applied BASESPEC to 3 models from another vendor, and BASESPEC found multiple mismatches, two of which led us to discover a buffer overflow bug.
{"title":"BaseSpec: Comparative Analysis of Baseband Software and Cellular Specifications for L3 Protocols","authors":"Eunsoo Kim, Dongkwan Kim, CheolJun Park, Insu Yun, Yongdae Kim","doi":"10.14722/NDSS.2021.24365","DOIUrl":"https://doi.org/10.14722/NDSS.2021.24365","url":null,"abstract":"Cellular basebands play a crucial role in mobile communication. However, it is significantly challenging to assess their security for several reasons. Manual analysis is inevitable because of the obscurity and complexity of baseband firmware; however, such analysis requires repetitive efforts to cover diverse models or versions. Automating the analysis is also non-trivial because the firmware is significantly large and contains numerous functions associated with complex cellular protocols. Therefore, existing approaches on baseband analysis are limited to only a couple of models or versions within a single vendor. In this paper, we propose a novel approach named BASESPEC, which performs a comparative analysis of baseband software and cellular specifications. By leveraging the standardized message structures in the specification, BASESPEC inspects the message structures implemented in the baseband software systematically. It requires a manual yet one-time analysis effort to determine how the message structures are embedded in target firmware. Then, BASESPEC compares the extracted message structures with those in the specification syntactically and semantically, and finally, it reports mismatches. These mismatches indicate the developer’s mistakes, which break the compliance of the baseband with the specification, or they imply potential vulnerabilities. We evaluated BASESPEC with 18 baseband firmware images of 9 models from one of the top three vendors and found hundreds of mismatches. By analyzing these mismatches, we discovered 9 erroneous cases: 5 functional errors and 4 memory-related vulnerabilities. Notably, two of these are critical remote code execution 0-days. Moreover, we applied BASESPEC to 3 models from another vendor, and BASESPEC found multiple mismatches, two of which led us to discover a buffer overflow bug.","PeriodicalId":364091,"journal":{"name":"Proceedings 2021 Network and Distributed System Security Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131908289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Residential proxy has emerged as a service gaining popularity recently, in which proxy providers relay their customers’ network traffic through millions of proxy peers under their control. We find that many of these proxy peers are mobile devices, whose role in the proxy network can have significant security implications since mobile devices tend to be privacyand resource-sensitive. However, little effort has been made so far to understand the extent of their involvement, not to mention how these devices are recruited by the proxy network and what security and privacy risks they may pose. In this paper, we report the first measurement study on the mobile proxy ecosystem. Our study was made possible by a novel measurement infrastructure, which enabled us to identify proxy providers, to discover proxy SDKs (software development kits), to detect Android proxy apps built upon the proxy SDKs, to harvest proxy IP addresses, and to understand proxy traffic. The information collected through this infrastructure has brought to us new understandings of this ecosystem and important security discoveries. More specifically, 4 proxy providers were found to offer app developers mobile proxy SDKs as a competitive app monetization channel, with $50K per month per 1M MAU (monthly active users). 1,701 Android APKs (belonging to 963 Android apps) turn out to have integrated those proxy SDKs, with most of them available on Google Play with at least 300M installations in total. Furthermore, 48.43% of these APKs are flagged by at least 5 anti-virus engines as malicious, which could explain why 86.60% of the 963 Android apps have been removed from Google Play by Oct 2019. Besides, while these apps display user consent dialogs on traffic relay, our user study indicates that the user consent texts are quite confusing. We even discover a proxy SDK that stealthily relays traffic without showing any notifications. We also captured 625K cellular proxy IPs, along with a set of suspicious activities observed in proxy traffic such as ads fraud. We have reported our findings to affected parties, offered suggestions, and proposed the methodologies to detect proxy apps and proxy traffic. ∗Corresponding author
{"title":"Your Phone is My Proxy: Detecting and Understanding Mobile Proxy Networks","authors":"Xianghang Mi, Siyuan Tang, Zhengyi Li, Xiaojing Liao, Feng Qian, Xiaofeng Wang","doi":"10.14722/NDSS.2021.24008","DOIUrl":"https://doi.org/10.14722/NDSS.2021.24008","url":null,"abstract":"Residential proxy has emerged as a service gaining popularity recently, in which proxy providers relay their customers’ network traffic through millions of proxy peers under their control. We find that many of these proxy peers are mobile devices, whose role in the proxy network can have significant security implications since mobile devices tend to be privacyand resource-sensitive. However, little effort has been made so far to understand the extent of their involvement, not to mention how these devices are recruited by the proxy network and what security and privacy risks they may pose. In this paper, we report the first measurement study on the mobile proxy ecosystem. Our study was made possible by a novel measurement infrastructure, which enabled us to identify proxy providers, to discover proxy SDKs (software development kits), to detect Android proxy apps built upon the proxy SDKs, to harvest proxy IP addresses, and to understand proxy traffic. The information collected through this infrastructure has brought to us new understandings of this ecosystem and important security discoveries. More specifically, 4 proxy providers were found to offer app developers mobile proxy SDKs as a competitive app monetization channel, with $50K per month per 1M MAU (monthly active users). 1,701 Android APKs (belonging to 963 Android apps) turn out to have integrated those proxy SDKs, with most of them available on Google Play with at least 300M installations in total. Furthermore, 48.43% of these APKs are flagged by at least 5 anti-virus engines as malicious, which could explain why 86.60% of the 963 Android apps have been removed from Google Play by Oct 2019. Besides, while these apps display user consent dialogs on traffic relay, our user study indicates that the user consent texts are quite confusing. We even discover a proxy SDK that stealthily relays traffic without showing any notifications. We also captured 625K cellular proxy IPs, along with a set of suspicious activities observed in proxy traffic such as ads fraud. We have reported our findings to affected parties, offered suggestions, and proposed the methodologies to detect proxy apps and proxy traffic. ∗Corresponding author","PeriodicalId":364091,"journal":{"name":"Proceedings 2021 Network and Distributed System Security Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124220537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.14722/NDSS.2021.24057
Adil Ahmad, Juhee Kim, Jaebaek Seo, I. Shin, Pedro Fonseca, Byoungyoung Lee
Intel SGX aims to provide the confidentiality of user data on untrusted cloud machines. However, applications that process confidential user data may contain bugs that leak information or be programmed maliciously to collect user data. Existing research that attempts to solve this problem does not consider multi-client isolation in a single enclave. We show that by not supporting such in-enclave isolation, they incur considerable slowdown when concurrently processing multiple clients in different enclave processes, due to the limitations of SGX. This paper proposes CHANCEL, a sandbox designed for multi-client isolation within a single SGX enclave. In particular, CHANCEL allows a program’s threads to access both a per-thread memory region and a shared read-only memory region while servicing requests. Each thread handles requests from a single client at a time and is isolated from other threads, using a MultiClient Software Fault Isolation (MCSFI) scheme. Furthermore, CHANCEL supports various in-enclave services such as an inmemory file system and shielded client communication to ensure complete mediation of the program’s interactions with the outside world. We implemented CHANCEL and evaluated it on SGX hardware using both micro-benchmarks and realistic target scenarios, including private information retrieval and product recommendation services. Our results show that CHANCEL outperforms a baseline multi-process sandbox by 4.06− 53.70× on micro-benchmarks and 0.02−21.18× on realistic workloads while providing strong security guarantees.
{"title":"CHANCEL: Efficient Multi-client Isolation Under Adversarial Programs","authors":"Adil Ahmad, Juhee Kim, Jaebaek Seo, I. Shin, Pedro Fonseca, Byoungyoung Lee","doi":"10.14722/NDSS.2021.24057","DOIUrl":"https://doi.org/10.14722/NDSS.2021.24057","url":null,"abstract":"Intel SGX aims to provide the confidentiality of user data on untrusted cloud machines. However, applications that process confidential user data may contain bugs that leak information or be programmed maliciously to collect user data. Existing research that attempts to solve this problem does not consider multi-client isolation in a single enclave. We show that by not supporting such in-enclave isolation, they incur considerable slowdown when concurrently processing multiple clients in different enclave processes, due to the limitations of SGX. This paper proposes CHANCEL, a sandbox designed for multi-client isolation within a single SGX enclave. In particular, CHANCEL allows a program’s threads to access both a per-thread memory region and a shared read-only memory region while servicing requests. Each thread handles requests from a single client at a time and is isolated from other threads, using a MultiClient Software Fault Isolation (MCSFI) scheme. Furthermore, CHANCEL supports various in-enclave services such as an inmemory file system and shielded client communication to ensure complete mediation of the program’s interactions with the outside world. We implemented CHANCEL and evaluated it on SGX hardware using both micro-benchmarks and realistic target scenarios, including private information retrieval and product recommendation services. Our results show that CHANCEL outperforms a baseline multi-process sandbox by 4.06− 53.70× on micro-benchmarks and 0.02−21.18× on realistic workloads while providing strong security guarantees.","PeriodicalId":364091,"journal":{"name":"Proceedings 2021 Network and Distributed System Security Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125703597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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