Pub Date : 2021-05-01DOI: 10.1109/SP40001.2021.00095
E. Blázquez, S. Pastrana, Álvaro Feal, Julien Gamba, Platon Kotzias, N. Vallina-Rodriguez, J. Tapiador
Android firmware updates are typically managed by the so-called FOTA (Firmware Over-the-Air) apps. Such apps are highly privileged and play a critical role in maintaining devices secured and updated. The Android operating system offers standard mechanisms—available to Original Equipment Manufacturers (OEMs)—to implement their own FOTA apps but such vendor-specific implementations could be a source of security and privacy issues due to poor software engineering practices. This paper performs the first large-scale and systematic analysis of the FOTA ecosystem through a dataset of 2,013 FOTA apps detected with a tool designed for this purpose over 422,121 pre-installed apps. We classify the different stakeholders developing and deploying FOTA apps on the Android update ecosystem, showing that 43% of FOTA apps are developed by third parties. We report that some devices can have as many as 5 apps implementing FOTA capabilities. By means of static analysis of the code of FOTA apps, we show that some apps present behaviors that can be considered privacy intrusive, such as the collection of sensitive user data (e.g., geolocation linked to unique hardware identifiers), and a significant presence of third-party trackers. We also discover implementation issues leading to critical vulnerabilities, such as the use of public AOSP test keys both for signing FOTA apps and for update verification, thus allowing any update signed with the same key to be installed. Finally, we study telemetry data collected from real devices by a commercial security tool. We demonstrate that FOTA apps are responsible for the installation of non-system apps (e.g., entertainment apps and games), including malware and Potentially Unwanted Programs (PUP). Our findings suggest that FOTA development practices are misaligned with Google’s recommendations.
{"title":"Trouble Over-The-Air: An Analysis of FOTA Apps in the Android Ecosystem","authors":"E. Blázquez, S. Pastrana, Álvaro Feal, Julien Gamba, Platon Kotzias, N. Vallina-Rodriguez, J. Tapiador","doi":"10.1109/SP40001.2021.00095","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00095","url":null,"abstract":"Android firmware updates are typically managed by the so-called FOTA (Firmware Over-the-Air) apps. Such apps are highly privileged and play a critical role in maintaining devices secured and updated. The Android operating system offers standard mechanisms—available to Original Equipment Manufacturers (OEMs)—to implement their own FOTA apps but such vendor-specific implementations could be a source of security and privacy issues due to poor software engineering practices. This paper performs the first large-scale and systematic analysis of the FOTA ecosystem through a dataset of 2,013 FOTA apps detected with a tool designed for this purpose over 422,121 pre-installed apps. We classify the different stakeholders developing and deploying FOTA apps on the Android update ecosystem, showing that 43% of FOTA apps are developed by third parties. We report that some devices can have as many as 5 apps implementing FOTA capabilities. By means of static analysis of the code of FOTA apps, we show that some apps present behaviors that can be considered privacy intrusive, such as the collection of sensitive user data (e.g., geolocation linked to unique hardware identifiers), and a significant presence of third-party trackers. We also discover implementation issues leading to critical vulnerabilities, such as the use of public AOSP test keys both for signing FOTA apps and for update verification, thus allowing any update signed with the same key to be installed. Finally, we study telemetry data collected from real devices by a commercial security tool. We demonstrate that FOTA apps are responsible for the installation of non-system apps (e.g., entertainment apps and games), including malware and Potentially Unwanted Programs (PUP). Our findings suggest that FOTA development practices are misaligned with Google’s recommendations.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"82 1","pages":"1606-1622"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88609679","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 : 2021-05-01DOI: 10.1109/SP40001.2021.00049
Shih-wei Li, Xupeng Li, Ronghui Gu, Jason Nieh, J. Hui
Commodity hypervisors are widely deployed to support virtual machines (VMs) on multiprocessor hardware. Their growing complexity poses a security risk. To enable formal verification over such a large codebase, we introduce microverification, a new approach that decomposes a commodity hypervisor into a small core and a set of untrusted services so that we can prove security properties of the entire hypervisor by verifying the core alone. To verify the multiprocessor hypervisor core, we introduce security-preserving layers to modularize the proof without hiding information leakage so we can prove each layer of the implementation refines its specification, and the top layer specification is refined by all layers of the core implementation. To verify commodity hypervisor features that require dynamically changing information flow, we introduce data oracles to mask intentional information flow. We can then prove noninterference at the top layer specification and guarantee the resulting security properties hold for the entire hypervisor implementation. Using microverification, we retrofitted the Linux KVM hypervisor with only modest modifications to its codebase. Using Coq, we proved that the hypervisor protects the confidentiality and integrity of VM data, while retaining KVM’s functionality and performance. Our work is the first machine-checked security proof for a commodity multiprocessor hypervisor.
{"title":"A Secure and Formally Verified Linux KVM Hypervisor","authors":"Shih-wei Li, Xupeng Li, Ronghui Gu, Jason Nieh, J. Hui","doi":"10.1109/SP40001.2021.00049","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00049","url":null,"abstract":"Commodity hypervisors are widely deployed to support virtual machines (VMs) on multiprocessor hardware. Their growing complexity poses a security risk. To enable formal verification over such a large codebase, we introduce microverification, a new approach that decomposes a commodity hypervisor into a small core and a set of untrusted services so that we can prove security properties of the entire hypervisor by verifying the core alone. To verify the multiprocessor hypervisor core, we introduce security-preserving layers to modularize the proof without hiding information leakage so we can prove each layer of the implementation refines its specification, and the top layer specification is refined by all layers of the core implementation. To verify commodity hypervisor features that require dynamically changing information flow, we introduce data oracles to mask intentional information flow. We can then prove noninterference at the top layer specification and guarantee the resulting security properties hold for the entire hypervisor implementation. Using microverification, we retrofitted the Linux KVM hypervisor with only modest modifications to its codebase. Using Coq, we proved that the hypervisor protects the confidentiality and integrity of VM data, while retaining KVM’s functionality and performance. Our work is the first machine-checked security proof for a commodity multiprocessor hypervisor.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"6 1","pages":"1782-1799"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75236651","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 : 2021-05-01DOI: 10.1109/SP40001.2021.00100
Z. Din, Harish Venugopalan, Henry Lin, Adam Wushensky, Steven Liu, Samuel T. King
App builders commonly use security challenges, a form of step-up authentication, to add security to their apps. However, the ethical implications of this type of architecture has not been studied previously.In this paper, we present a large-scale measurement study of running an existing anti-fraud security challenge, Boxer, in real apps running on mobile devices. We find that although Boxer does work well overall, it is unable to scan effectively on devices that run its machine learning models at less than one frame per second (FPS), blocking users who use inexpensive devices.With the insights from our study, we design Daredevil, a new anti-fraud system for scanning payment cards that works well across the broad range of performance characteristics and hardware configurations found on modern mobile devices. Daredevil reduces the number of devices that run at less than one FPS by an order of magnitude compared to Boxer, providing a more equitable system for fighting fraud.In total, we collect data from 5,085,444 real devices spread across 496 real apps running production software and interacting with real users.
{"title":"Doing good by fighting fraud: Ethical anti-fraud systems for mobile payments","authors":"Z. Din, Harish Venugopalan, Henry Lin, Adam Wushensky, Steven Liu, Samuel T. King","doi":"10.1109/SP40001.2021.00100","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00100","url":null,"abstract":"App builders commonly use security challenges, a form of step-up authentication, to add security to their apps. However, the ethical implications of this type of architecture has not been studied previously.In this paper, we present a large-scale measurement study of running an existing anti-fraud security challenge, Boxer, in real apps running on mobile devices. We find that although Boxer does work well overall, it is unable to scan effectively on devices that run its machine learning models at less than one frame per second (FPS), blocking users who use inexpensive devices.With the insights from our study, we design Daredevil, a new anti-fraud system for scanning payment cards that works well across the broad range of performance characteristics and hardware configurations found on modern mobile devices. Daredevil reduces the number of devices that run at less than one FPS by an order of magnitude compared to Boxer, providing a more equitable system for fighting fraud.In total, we collect data from 5,085,444 real devices spread across 496 real apps running production software and interacting with real users.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"6 1","pages":"1623-1640"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77024388","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}
Homomorphic encryption (HE) is considered as one of the most important primitives for privacy-preserving applications. However, an efficient approach to evaluate both polynomial and non-polynomial functions on encrypted data is still absent, which hinders the deployment of HE to real-life applications. To address this issue, we propose a practical framework PEGASUS. PEGASUS can efficiently switch back and forth between a packed CKKS ciphertext and FHEW ciphertexts without decryption, allowing us to evaluate arithmetic functions efficiently on the CKKS side, and to evaluate look-up tables on FHEW ciphertexts. Our FHEW → CKKS conversion algorithm is more practical than the existing methods. We improve the computational complexity from linear to sublinear. Moreover, the size of our conversion key is significantly smaller, e.g., reduced from 80 gigabytes to 12 megabytes. We present extensive benchmarks of PEGASUS, including sigmoid/ReLU/min/max/division, sorting and max-pooling. To further demonstrate the capability of PEGASUS, we developed two more applications. The first one is a private decision tree evaluation whose communication cost is about two orders of magnitude smaller than the previous HE-based approaches. The second one is a secure K-means clustering that is able to run on thousands of encrypted samples in minutes that outperforms the best existing system by 14 × – 20×. To the best of our knowledge, this is the first work that supports practical K-means clustering using HE in a single server setting.
{"title":"PEGASUS: Bridging Polynomial and Non-polynomial Evaluations in Homomorphic Encryption","authors":"Wen-jie Lu, Zhicong Huang, Cheng Hong, Yiping Ma, Hunter Qu","doi":"10.1109/SP40001.2021.00043","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00043","url":null,"abstract":"Homomorphic encryption (HE) is considered as one of the most important primitives for privacy-preserving applications. However, an efficient approach to evaluate both polynomial and non-polynomial functions on encrypted data is still absent, which hinders the deployment of HE to real-life applications. To address this issue, we propose a practical framework PEGASUS. PEGASUS can efficiently switch back and forth between a packed CKKS ciphertext and FHEW ciphertexts without decryption, allowing us to evaluate arithmetic functions efficiently on the CKKS side, and to evaluate look-up tables on FHEW ciphertexts. Our FHEW → CKKS conversion algorithm is more practical than the existing methods. We improve the computational complexity from linear to sublinear. Moreover, the size of our conversion key is significantly smaller, e.g., reduced from 80 gigabytes to 12 megabytes. We present extensive benchmarks of PEGASUS, including sigmoid/ReLU/min/max/division, sorting and max-pooling. To further demonstrate the capability of PEGASUS, we developed two more applications. The first one is a private decision tree evaluation whose communication cost is about two orders of magnitude smaller than the previous HE-based approaches. The second one is a secure K-means clustering that is able to run on thousands of encrypted samples in minutes that outperforms the best existing system by 14 × – 20×. To the best of our knowledge, this is the first work that supports practical K-means clustering using HE in a single server setting.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"58 1","pages":"1057-1073"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88867986","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 : 2021-05-01DOI: 10.1109/SP40001.2021.00052
Timothy Trippel, K. Shin, K. Bush, Matthew Hicks
To cope with ever-increasing design complexities, integrated circuit designers increase both the size of their design teams and their reliance on third-party intellectual property (IP). Both come at the expense of trust: it is computationally infeasible to exhaustively verify that a design is free of all possible malicious modifications (i.e., hardware Trojans). Making matters worse, unlike software, hardware modifications are permanent: there is no "patching" mechanism for hardware; and powerful: they serve as a foothold for subverting software that sits above.To counter this threat, prior work uses both static and dynamic analysis techniques to verify hardware designs are Trojan-free. Unfortunately, researchers continue to reveal weaknesses in these "one-size-fits-all", heuristic-based approaches. Instead of attempting to detect all possible hardware Trojans, we take the first step in addressing the hardware Trojan threat in a divide-and-conquer fashion: defining and eliminating Ticking Timebomb Trojans (TTTs), forcing attackers to implement larger Trojan designs detectable via existing verification and side-channel defenses. Like many system-level software defenses (e.g., Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP)), our goal is to systematically constrict the hardware attacker’s design space.First, we construct a definition of TTTs derived from their functional behavior. Next, we translate this definition into fundamental components required to realize TTT behavior in hardware. Using these components, we expand the set of all known TTTs to a total of six variants—including unseen variants. Leveraging our definition, we design and implement a TTT-specific dynamic verification toolchain extension, called Bomber-man. Using four real-world hardware designs, we demonstrate Bomberman’s ability to detect all TTT variants, where previous defenses fail, with <1.2% false positives.
{"title":"Bomberman: Defining and Defeating Hardware Ticking Timebombs at Design-time","authors":"Timothy Trippel, K. Shin, K. Bush, Matthew Hicks","doi":"10.1109/SP40001.2021.00052","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00052","url":null,"abstract":"To cope with ever-increasing design complexities, integrated circuit designers increase both the size of their design teams and their reliance on third-party intellectual property (IP). Both come at the expense of trust: it is computationally infeasible to exhaustively verify that a design is free of all possible malicious modifications (i.e., hardware Trojans). Making matters worse, unlike software, hardware modifications are permanent: there is no \"patching\" mechanism for hardware; and powerful: they serve as a foothold for subverting software that sits above.To counter this threat, prior work uses both static and dynamic analysis techniques to verify hardware designs are Trojan-free. Unfortunately, researchers continue to reveal weaknesses in these \"one-size-fits-all\", heuristic-based approaches. Instead of attempting to detect all possible hardware Trojans, we take the first step in addressing the hardware Trojan threat in a divide-and-conquer fashion: defining and eliminating Ticking Timebomb Trojans (TTTs), forcing attackers to implement larger Trojan designs detectable via existing verification and side-channel defenses. Like many system-level software defenses (e.g., Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP)), our goal is to systematically constrict the hardware attacker’s design space.First, we construct a definition of TTTs derived from their functional behavior. Next, we translate this definition into fundamental components required to realize TTT behavior in hardware. Using these components, we expand the set of all known TTTs to a total of six variants—including unseen variants. Leveraging our definition, we design and implement a TTT-specific dynamic verification toolchain extension, called Bomber-man. Using four real-world hardware designs, we demonstrate Bomberman’s ability to detect all TTT variants, where previous defenses fail, with <1.2% false positives.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"80 1","pages":"970-986"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87102931","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 : 2021-05-01DOI: 10.1109/SP40001.2021.00047
Frederick Barr-Smith, Xabier Ugarte-Pedrero, Mariano Graziano, Riccardo Spolaor, I. Martinovic
As malware detection algorithms and methods become more sophisticated, malware authors adopt equally sophisticated evasion mechanisms to defeat them. Anecdotal evidence claims Living-Off-The-Land (LotL) techniques are one of the major evasion techniques used in many malware attacks. These techniques leverage binaries already present in the system to conduct malicious actions. We present the first large-scale systematic investigation of the use of these techniques by malware on Windows systems.In this paper, we analyse how common the use of these native system binaries is across several malware datasets, containing a total of 31,805,549 samples. We identify an average 9.41% prevalence. Our results show that the use of LotL techniques is prolific, particularly in Advanced Persistent Threat (APT) malware samples where the prevalence is 26.26%, over twice that of commodity malware.To illustrate the evasive potential of LotL techniques, we test the usage of LotL techniques against several fully patched Windows systems in a local sandboxed environment and show that there is a generalised detection gap in 10 of the most popular anti-virus products.
{"title":"Survivalism: Systematic Analysis of Windows Malware Living-Off-The-Land","authors":"Frederick Barr-Smith, Xabier Ugarte-Pedrero, Mariano Graziano, Riccardo Spolaor, I. Martinovic","doi":"10.1109/SP40001.2021.00047","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00047","url":null,"abstract":"As malware detection algorithms and methods become more sophisticated, malware authors adopt equally sophisticated evasion mechanisms to defeat them. Anecdotal evidence claims Living-Off-The-Land (LotL) techniques are one of the major evasion techniques used in many malware attacks. These techniques leverage binaries already present in the system to conduct malicious actions. We present the first large-scale systematic investigation of the use of these techniques by malware on Windows systems.In this paper, we analyse how common the use of these native system binaries is across several malware datasets, containing a total of 31,805,549 samples. We identify an average 9.41% prevalence. Our results show that the use of LotL techniques is prolific, particularly in Advanced Persistent Threat (APT) malware samples where the prevalence is 26.26%, over twice that of commodity malware.To illustrate the evasive potential of LotL techniques, we test the usage of LotL techniques against several fully patched Windows systems in a local sandboxed environment and show that there is a generalised detection gap in 10 of the most popular anti-virus products.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"52 1","pages":"1557-1574"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84456726","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 : 2021-05-01DOI: 10.1109/SP40001.2021.00104
Yi Chen, Yepeng Yao, Xiaofeng Wang, Dandan Xu, Chang Yue, Xiaozhong Liu, Kai Chen, Haixu Tang, Baoxu Liu
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.
{"title":"Bookworm Game: Automatic Discovery of LTE Vulnerabilities Through Documentation Analysis","authors":"Yi Chen, Yepeng Yao, Xiaofeng Wang, Dandan Xu, Chang Yue, Xiaozhong Liu, Kai Chen, Haixu Tang, Baoxu Liu","doi":"10.1109/SP40001.2021.00104","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00104","url":null,"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.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"91 1","pages":"1197-1214"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74666857","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 : 2021-05-01DOI: 10.1109/SP40001.2021.00114
Jaeseung Choi, Kangsu Kim, Daejin Lee, S. Cha
Although it is common practice for kernel fuzzers to leverage type information of system calls, current Windows kernel fuzzers do not follow the practice as most system calls are private and largely undocumented. In this paper, we present a practical static binary analyzer that automatically infers system call types on Windows at scale. We incorporate our analyzer to NtFuzz, a type-aware Windows kernel fuzzing framework. To our knowledge, this is the first practical fuzzing system that utilizes scalable binary analysis on a COTS OS. With NtFuzz, we found 11 previously unknown kernel bugs, and earned $25,000 through the bug bounty program offered by Microsoft. All these results confirm the practicality of our system as a kernel fuzzer.
{"title":"NtFuzz: Enabling Type-Aware Kernel Fuzzing on Windows with Static Binary Analysis","authors":"Jaeseung Choi, Kangsu Kim, Daejin Lee, S. Cha","doi":"10.1109/SP40001.2021.00114","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00114","url":null,"abstract":"Although it is common practice for kernel fuzzers to leverage type information of system calls, current Windows kernel fuzzers do not follow the practice as most system calls are private and largely undocumented. In this paper, we present a practical static binary analyzer that automatically infers system call types on Windows at scale. We incorporate our analyzer to NtFuzz, a type-aware Windows kernel fuzzing framework. To our knowledge, this is the first practical fuzzing system that utilizes scalable binary analysis on a COTS OS. With NtFuzz, we found 11 previously unknown kernel bugs, and earned $25,000 through the bug bounty program offered by Microsoft. All these results confirm the practicality of our system as a kernel fuzzer.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"56 1","pages":"677-693"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81525688","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 : 2021-05-01DOI: 10.1109/SP40001.2021.00090
Ziyang Li, Aravind Machiry, Binghong Chen, M. Naik, Ke Wang, Le Song
Software APIs exhibit rich diversity and complexity which not only renders them a common source of programming errors but also hinders program analysis tools for checking them. Such tools either expect a precise API specification, which requires program analysis expertise, or presume that correct API usages follow simple idioms that can be automatically mined from code, which suffers from poor accuracy. We propose a new approach that allows regular programmers to find API misuses. Our approach interacts with the user to classify valid and invalid usages of each target API method. It minimizes user burden by employing an active learning algorithm that ranks API usages by their likelihood of being invalid. We implemented our approach in a tool called ARBITRAR for C/C++ programs, and applied it to check the uses of 18 API methods in 21 large real-world programs, including OpenSSL and Linux Kernel. Within just 3 rounds of user interaction on average per API method, ARBITRAR found 40 new bugs, with patches accepted for 18 of them. Moreover, ARBITRAR finds all known bugs reported by a state-of-the-art tool APISAN in a benchmark suite comprising 92 bugs with a false positive rate of only 51.5% compared to APISAN’s 87.9%.
{"title":"ARBITRAR: User-Guided API Misuse Detection","authors":"Ziyang Li, Aravind Machiry, Binghong Chen, M. Naik, Ke Wang, Le Song","doi":"10.1109/SP40001.2021.00090","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00090","url":null,"abstract":"Software APIs exhibit rich diversity and complexity which not only renders them a common source of programming errors but also hinders program analysis tools for checking them. Such tools either expect a precise API specification, which requires program analysis expertise, or presume that correct API usages follow simple idioms that can be automatically mined from code, which suffers from poor accuracy. We propose a new approach that allows regular programmers to find API misuses. Our approach interacts with the user to classify valid and invalid usages of each target API method. It minimizes user burden by employing an active learning algorithm that ranks API usages by their likelihood of being invalid. We implemented our approach in a tool called ARBITRAR for C/C++ programs, and applied it to check the uses of 18 API methods in 21 large real-world programs, including OpenSSL and Linux Kernel. Within just 3 rounds of user interaction on average per API method, ARBITRAR found 40 new bugs, with patches accepted for 18 of them. Moreover, ARBITRAR finds all known bugs reported by a state-of-the-art tool APISAN in a benchmark suite comprising 92 bugs with a false positive rate of only 51.5% compared to APISAN’s 87.9%.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"8 1","pages":"1400-1415"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82339484","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 : 2021-05-01DOI: 10.1109/SP40001.2021.00031
Cheng Shen, T. Liu, Jun Huang, Rui Tan
Due to the low power of electromagnetic radiation (EMR), EM convert channel has been widely considered as a short-range attack that can be easily mitigated by shielding. This paper overturns this common belief by demonstrating how covert EM signals leaked from typical laptops, desktops and servers are decoded from hundreds of meters away, or penetrate aggressive shield previously considered as sufficient to ensure emission security. We achieve this by designing EMLoRa – a super resilient EM covert channel that exploits memory as a LoRa-like radio. EMLoRa represents the first attempt of designing an EM covert channel using state-of-the-art spread spectrum technology. It tackles a set of unique challenges, such as handling complex spectral characteristics of EMR, tolerating signal distortions caused by CPU contention, and preventing adversarial detectors from demodulating covert signals. Experiment results show that EMLoRa boosts communication range by 20x and improves attenuation resilience by up to 53 dB when compared with prior EM covert channels at the same bit rate. By achieving this, EMLoRa allows an attacker to circumvent security perimeter, breach Faraday cage, and localize air-gapped devices in a wide area using just a small number of inexpensive sensors. To countermeasure EMLoRa, we further explore the feasibility of uncovering EMLoRa's signal using energy- and CNN-based detectors. Experiments show that both detectors suffer limited range, allowing EMLoRa to gain a significant range advantage. Our results call for further research on the countermeasure against spread spectrum-based EM covert channels.
{"title":"When LoRa Meets EMR: Electromagnetic Covert Channels Can Be Super Resilient","authors":"Cheng Shen, T. Liu, Jun Huang, Rui Tan","doi":"10.1109/SP40001.2021.00031","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00031","url":null,"abstract":"Due to the low power of electromagnetic radiation (EMR), EM convert channel has been widely considered as a short-range attack that can be easily mitigated by shielding. This paper overturns this common belief by demonstrating how covert EM signals leaked from typical laptops, desktops and servers are decoded from hundreds of meters away, or penetrate aggressive shield previously considered as sufficient to ensure emission security. We achieve this by designing EMLoRa – a super resilient EM covert channel that exploits memory as a LoRa-like radio. EMLoRa represents the first attempt of designing an EM covert channel using state-of-the-art spread spectrum technology. It tackles a set of unique challenges, such as handling complex spectral characteristics of EMR, tolerating signal distortions caused by CPU contention, and preventing adversarial detectors from demodulating covert signals. Experiment results show that EMLoRa boosts communication range by 20x and improves attenuation resilience by up to 53 dB when compared with prior EM covert channels at the same bit rate. By achieving this, EMLoRa allows an attacker to circumvent security perimeter, breach Faraday cage, and localize air-gapped devices in a wide area using just a small number of inexpensive sensors. To countermeasure EMLoRa, we further explore the feasibility of uncovering EMLoRa's signal using energy- and CNN-based detectors. Experiments show that both detectors suffer limited range, allowing EMLoRa to gain a significant range advantage. Our results call for further research on the countermeasure against spread spectrum-based EM covert channels.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"48 1","pages":"1304-1317"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75750214","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：