Pub Date : 2023-05-01DOI: 10.1109/SP46215.2023.10179298
Yu Hao, Guoren Li, Xiaochen Zou, Weiteng Chen, Shitong Zhu, Zhiyun Qian, A. A. Sani
Fuzz testing operating system kernels has been effective overall in recent years. For example, syzkaller manages to find thousands of bugs in the Linux kernel since 2017. One necessary component of syzkaller is a collection of syscall descriptions that are often provided by human experts. However, to our knowledge, current syscall descriptions are largely written manually, which is both time-consuming and error-prone. It is especially challenging considering that there are many kernel drivers (for new hardware devices and beyond) that are continuously being developed and evolving over time. In this paper, we present a principled solution for generating syscall descriptions for Linux kernel drivers. At its core, we summarize and model the key invariants or programming conventions, extracted from the "contract" between the core kernel and drivers. This allows us to understand programmatically how a kernel driver is initialized and how its associated interfaces are constructed. With this insight, we have developed a solution in a tool called SyzDescribe that has been tested for over hundreds of kernel drivers. We show that the syscall descriptions produced by SyzDescribe are competitive to manually-curated ones, and much better than prior work (i.e., DIFUZE and KSG). Finally, we analyze the gap between our descriptions and the ground truth and point to future improvement opportunities.
{"title":"SyzDescribe: Principled, Automated, Static Generation of Syscall Descriptions for Kernel Drivers","authors":"Yu Hao, Guoren Li, Xiaochen Zou, Weiteng Chen, Shitong Zhu, Zhiyun Qian, A. A. Sani","doi":"10.1109/SP46215.2023.10179298","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179298","url":null,"abstract":"Fuzz testing operating system kernels has been effective overall in recent years. For example, syzkaller manages to find thousands of bugs in the Linux kernel since 2017. One necessary component of syzkaller is a collection of syscall descriptions that are often provided by human experts. However, to our knowledge, current syscall descriptions are largely written manually, which is both time-consuming and error-prone. It is especially challenging considering that there are many kernel drivers (for new hardware devices and beyond) that are continuously being developed and evolving over time. In this paper, we present a principled solution for generating syscall descriptions for Linux kernel drivers. At its core, we summarize and model the key invariants or programming conventions, extracted from the \"contract\" between the core kernel and drivers. This allows us to understand programmatically how a kernel driver is initialized and how its associated interfaces are constructed. With this insight, we have developed a solution in a tool called SyzDescribe that has been tested for over hundreds of kernel drivers. We show that the syscall descriptions produced by SyzDescribe are competitive to manually-curated ones, and much better than prior work (i.e., DIFUZE and KSG). Finally, we analyze the gap between our descriptions and the ground truth and point to future improvement opportunities.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116601748","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179353
Norbert Ludant, Pieter Robyns, G. Noubir
We present the first open-source tool capable of efficiently sniffing 5G control channels, 5GSniffer and demonstrate its potential to conduct attacks on users privacy. 5GSniffer builds on our analysis of the 5G RAN control channel exposing side-channel leakage. We note that decoding the 5G control channels is significantly more challenging than in LTE, since part of the information necessary for decoding is provided to the UEs over encrypted channels. We devise a set of techniques to achieve real-time control channels sniffing (over three orders of magnitude faster than brute-forcing). This enables, among other things, to retrieve the Radio Network Temporary Identifiers (RNTIs) of all users in a cell, and perform traffic analysis. To illustrate the potential of our sniffer, we analyse two privacy-focused messengers, Signal and Telegram. We identify privacy leaks that can be exploited to generate stealthy traffic to a target user. When combined with 5GSniffer, it enables stealthy exposure of the presence of a target user in a given location (solely based on their phone number), by linking the phone number to the RNTI. It also enables traffic analysis of the target user. We evaluate the attacks and our sniffer, demonstrating nearly 100% accuracy within 30 seconds of attack initiation.
{"title":"From 5G Sniffing to Harvesting Leakages of Privacy-Preserving Messengers","authors":"Norbert Ludant, Pieter Robyns, G. Noubir","doi":"10.1109/SP46215.2023.10179353","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179353","url":null,"abstract":"We present the first open-source tool capable of efficiently sniffing 5G control channels, 5GSniffer and demonstrate its potential to conduct attacks on users privacy. 5GSniffer builds on our analysis of the 5G RAN control channel exposing side-channel leakage. We note that decoding the 5G control channels is significantly more challenging than in LTE, since part of the information necessary for decoding is provided to the UEs over encrypted channels. We devise a set of techniques to achieve real-time control channels sniffing (over three orders of magnitude faster than brute-forcing). This enables, among other things, to retrieve the Radio Network Temporary Identifiers (RNTIs) of all users in a cell, and perform traffic analysis. To illustrate the potential of our sniffer, we analyse two privacy-focused messengers, Signal and Telegram. We identify privacy leaks that can be exploited to generate stealthy traffic to a target user. When combined with 5GSniffer, it enables stealthy exposure of the presence of a target user in a given location (solely based on their phone number), by linking the phone number to the RNTI. It also enables traffic analysis of the target user. We evaluate the attacks and our sniffer, demonstrating nearly 100% accuracy within 30 seconds of attack initiation.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116677676","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179331
W. Zong, Yang-Wai Chow, Willy Susilo, Kien Do, S. Venkatesh
While deep learning techniques have achieved great success in modern digital products, researchers have shown that deep learning models are susceptible to Trojan attacks. In a Trojan attack, an adversary stealthily modifies a deep learning model such that the model will output a predefined label whenever a trigger is present in the input. In this paper, we present TrojanModel, a practical Trojan attack against Automatic Speech Recognition (ASR) systems. ASR systems aim to transcribe voice input into text, which is easier for subsequent downstream applications to process. We consider a practical attack scenario in which an adversary inserts a Trojan into the acoustic model of a target ASR system. Unlike existing work that uses noise-like triggers that will easily arouse user suspicion, the work in this paper focuses on the use of unsuspicious sounds as a trigger, e.g., a piece of music playing in the background. In addition, TrojanModel does not require the retraining of a target model. Experimental results show that TrojanModel can achieve high attack success rates with negligible effect on the target model’s performance. We also demonstrate that the attack is effective in an over-the-air attack scenario, where audio is played over a physical speaker and received by a microphone.
{"title":"TrojanModel: A Practical Trojan Attack against Automatic Speech Recognition Systems","authors":"W. Zong, Yang-Wai Chow, Willy Susilo, Kien Do, S. Venkatesh","doi":"10.1109/SP46215.2023.10179331","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179331","url":null,"abstract":"While deep learning techniques have achieved great success in modern digital products, researchers have shown that deep learning models are susceptible to Trojan attacks. In a Trojan attack, an adversary stealthily modifies a deep learning model such that the model will output a predefined label whenever a trigger is present in the input. In this paper, we present TrojanModel, a practical Trojan attack against Automatic Speech Recognition (ASR) systems. ASR systems aim to transcribe voice input into text, which is easier for subsequent downstream applications to process. We consider a practical attack scenario in which an adversary inserts a Trojan into the acoustic model of a target ASR system. Unlike existing work that uses noise-like triggers that will easily arouse user suspicion, the work in this paper focuses on the use of unsuspicious sounds as a trigger, e.g., a piece of music playing in the background. In addition, TrojanModel does not require the retraining of a target model. Experimental results show that TrojanModel can achieve high attack success rates with negligible effect on the target model’s performance. We also demonstrate that the attack is effective in an over-the-air attack scenario, where audio is played over a physical speaker and received by a microphone.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122493340","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179308
Maximilian Noppel, Lukas Peter, Christian Wressnegger
Explainable machine learning holds great potential for analyzing and understanding learning-based systems. These methods can, however, be manipulated to present unfaithful explanations, giving rise to powerful and stealthy adversaries. In this paper, we demonstrate how to fully disguise the adversarial operation of a machine learning model. Similar to neural backdoors, we change the model’s prediction upon trigger presence but simultaneously fool an explanation method that is applied post-hoc for analysis. This enables an adversary to hide the presence of the trigger or point the explanation to entirely different portions of the input, throwing a red herring. We analyze different manifestations of these explanation-aware backdoors for gradient- and propagation-based explanation methods in the image domain, before we resume to conduct a red-herring attack against malware classification.
{"title":"Disguising Attacks with Explanation-Aware Backdoors","authors":"Maximilian Noppel, Lukas Peter, Christian Wressnegger","doi":"10.1109/SP46215.2023.10179308","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179308","url":null,"abstract":"Explainable machine learning holds great potential for analyzing and understanding learning-based systems. These methods can, however, be manipulated to present unfaithful explanations, giving rise to powerful and stealthy adversaries. In this paper, we demonstrate how to fully disguise the adversarial operation of a machine learning model. Similar to neural backdoors, we change the model’s prediction upon trigger presence but simultaneously fool an explanation method that is applied post-hoc for analysis. This enables an adversary to hide the presence of the trigger or point the explanation to entirely different portions of the input, throwing a red herring. We analyze different manifestations of these explanation-aware backdoors for gradient- and propagation-based explanation methods in the image domain, before we resume to conduct a red-herring attack against malware classification.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115168587","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179474
Xiaohan Zhang, Haoqi Ye, Ziqi Huang, Xiao Ye, Yinzhi Cao, Yuan Zhang, Min Yang
Face Verification Systems (FVSes) are more and more deployed by real-world mobile applications (apps) to verify a human’s claimed identity. One popular type of FVSes is called cross-side FVS (XFVS), which splits the FVS functionality into two sides: one at a mobile phone to take pictures or videos and the other at a trusted server for verification. Prior works have studied the security of XFVSes from the machine learning perspective, i.e., whether the learning models used by XFVSes are robust to adversarial attacks. However, the security of other parts of XFVSes, especially the design and implementation of the verification procedure used by XFVSes, is not well understood.In this paper, we conduct the first measurement study on the security of real-world XFVSes used by popular mobile apps from a system perspective. More specifically, we design and implement a semi-automated system, called XFVSChecker, to detect XFVSes in mobile apps and then inspect their compliance with four security properties. Our evaluation reveals that most of existing XFVS apps, including those with billions of downloads, are vulnerable to at least one of four types of attacks. These attacks require only easily available attack prerequisites, such as one photo of the victim, to pose significant security risks, including complete account takeover, identity fraud and financial loss. Our findings result in 14 Chinese National Vulnerability Database (CNVD) IDs and one of them, particularly CNVD-2021-86899, is awarded the most valuable vulnerability in 2021 among all the reported vulnerabilities to CNVD.
{"title":"Understanding the (In)Security of Cross-side Face Verification Systems in Mobile Apps: A System Perspective","authors":"Xiaohan Zhang, Haoqi Ye, Ziqi Huang, Xiao Ye, Yinzhi Cao, Yuan Zhang, Min Yang","doi":"10.1109/SP46215.2023.10179474","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179474","url":null,"abstract":"Face Verification Systems (FVSes) are more and more deployed by real-world mobile applications (apps) to verify a human’s claimed identity. One popular type of FVSes is called cross-side FVS (XFVS), which splits the FVS functionality into two sides: one at a mobile phone to take pictures or videos and the other at a trusted server for verification. Prior works have studied the security of XFVSes from the machine learning perspective, i.e., whether the learning models used by XFVSes are robust to adversarial attacks. However, the security of other parts of XFVSes, especially the design and implementation of the verification procedure used by XFVSes, is not well understood.In this paper, we conduct the first measurement study on the security of real-world XFVSes used by popular mobile apps from a system perspective. More specifically, we design and implement a semi-automated system, called XFVSChecker, to detect XFVSes in mobile apps and then inspect their compliance with four security properties. Our evaluation reveals that most of existing XFVS apps, including those with billions of downloads, are vulnerable to at least one of four types of attacks. These attacks require only easily available attack prerequisites, such as one photo of the victim, to pose significant security risks, including complete account takeover, identity fraud and financial loss. Our findings result in 14 Chinese National Vulnerability Database (CNVD) IDs and one of them, particularly CNVD-2021-86899, is awarded the most valuable vulnerability in 2021 among all the reported vulnerabilities to CNVD.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128006114","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179393
Kunming Jiang, Devora Chait-Roth, Zachary Destefano, Michael Walfish, Thomas Wies
There has been intense interest over the last decade in implementations of probabilistic proofs (IPs, SNARKs, PCPs, and so on): protocols in which an untrusted party proves to a verifier that a given computation was executed properly, possibly in zero knowledge. Nevertheless, implementations still do not scale beyond small computations. A central source of overhead is the front-end: translating from the abstract computation to a set of equivalent arithmetic constraints. This paper introduces a general-purpose framework, called Distiller, in which a user translates to constraints not the original computation but an abstracted specification of it. Distiller is the first in this area to perform such transformations in a way that is provably safe. Furthermore, by taking the idea of "encode a check in the constraints" to its literal logical extreme, Distiller exposes many new opportunities for constraint reduction, resulting in cost reductions for benchmark computations of 1.3–50×, and in some cases, better asymptotics.
{"title":"Less is more: refinement proofs for probabilistic proofs","authors":"Kunming Jiang, Devora Chait-Roth, Zachary Destefano, Michael Walfish, Thomas Wies","doi":"10.1109/SP46215.2023.10179393","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179393","url":null,"abstract":"There has been intense interest over the last decade in implementations of probabilistic proofs (IPs, SNARKs, PCPs, and so on): protocols in which an untrusted party proves to a verifier that a given computation was executed properly, possibly in zero knowledge. Nevertheless, implementations still do not scale beyond small computations. A central source of overhead is the front-end: translating from the abstract computation to a set of equivalent arithmetic constraints. This paper introduces a general-purpose framework, called Distiller, in which a user translates to constraints not the original computation but an abstracted specification of it. Distiller is the first in this area to perform such transformations in a way that is provably safe. Furthermore, by taking the idea of \"encode a check in the constraints\" to its literal logical extreme, Distiller exposes many new opportunities for constraint reduction, resulting in cost reductions for benchmark computations of 1.3–50×, and in some cases, better asymptotics.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134313650","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179289
Nate Mathews, James K. Holland, Se Eun Oh, Mohammad Saidur Rahman, Nicholas Hopper, M. Wright
Recent website fingerprinting attacks have been shown to achieve very high performance against traffic through Tor. These attacks allow an adversary to deduce the website a Tor user has visited by simply eavesdropping on the encrypted communication. This has consequently motivated the development of many defense strategies that obfuscate traffic through the addition of dummy packets and/or delays. The efficacy and practicality of many of these recent proposals have yet to be scrutinized in detail. In this study, we re-evaluate nine recent defense proposals that claim to provide adequate security with low-overheads using the latest Deep Learning-based attacks. Furthermore, we assess the feasibility of implementing these defenses within the current confines of Tor. To this end, we additionally provide the first on-network implementation of the DynaFlow defense to better assess its real-world utility.
{"title":"SoK: A Critical Evaluation of Efficient Website Fingerprinting Defenses","authors":"Nate Mathews, James K. Holland, Se Eun Oh, Mohammad Saidur Rahman, Nicholas Hopper, M. Wright","doi":"10.1109/SP46215.2023.10179289","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179289","url":null,"abstract":"Recent website fingerprinting attacks have been shown to achieve very high performance against traffic through Tor. These attacks allow an adversary to deduce the website a Tor user has visited by simply eavesdropping on the encrypted communication. This has consequently motivated the development of many defense strategies that obfuscate traffic through the addition of dummy packets and/or delays. The efficacy and practicality of many of these recent proposals have yet to be scrutinized in detail. In this study, we re-evaluate nine recent defense proposals that claim to provide adequate security with low-overheads using the latest Deep Learning-based attacks. Furthermore, we assess the feasibility of implementing these defenses within the current confines of Tor. To this end, we additionally provide the first on-network implementation of the DynaFlow defense to better assess its real-world utility.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114361378","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179310
Shubham Jain, Ana-Maria Creţu, Antoine Cully, Yves-Alexandre de Montjoye
End-to-end encryption (E2EE) provides strong technical protections to individuals from interferences. Governments and law enforcement agencies around the world have however raised concerns that E2EE also allows illegal content to be shared undetected. Client-side scanning (CSS), using perceptual hashing (PH) to detect known illegal content before it is shared, is seen as a promising solution to prevent the diffusion of illegal content while preserving encryption. While these proposals raise strong privacy concerns, proponents of the solutions have argued that the risk is limited as the technology has a limited scope: detecting known illegal content. In this paper, we show that modern perceptual hashing algorithms are actually fairly flexible pieces of technology and that this flexibility could be used by an adversary to add a secondary hidden feature to a client-side scanning system. More specifically, we show that an adversary providing the PH algorithm can "hide" a secondary purpose of face recognition of a target individual alongside its primary purpose of image copy detection. We first propose a procedure to train a dual-purpose deep perceptual hashing model by jointly optimizing for both the image copy detection and the targeted facial recognition task. Second, we extensively evaluate our dual-purpose model and show it to be able to reliably identify a target individual 67% of the time while not impacting its performance at detecting illegal content. We also show that our model is neither a general face detection nor a facial recognition model, allowing its secondary purpose to be hidden. Finally, we show that the secondary purpose can be enabled by adding a single illegal looking image to the database. Taken together, our results raise concerns that a deep perceptual hashing-based CSS system could turn billions of user devices into tools to locate targeted individuals.
{"title":"Deep perceptual hashing algorithms with hidden dual purpose: when client-side scanning does facial recognition","authors":"Shubham Jain, Ana-Maria Creţu, Antoine Cully, Yves-Alexandre de Montjoye","doi":"10.1109/SP46215.2023.10179310","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179310","url":null,"abstract":"End-to-end encryption (E2EE) provides strong technical protections to individuals from interferences. Governments and law enforcement agencies around the world have however raised concerns that E2EE also allows illegal content to be shared undetected. Client-side scanning (CSS), using perceptual hashing (PH) to detect known illegal content before it is shared, is seen as a promising solution to prevent the diffusion of illegal content while preserving encryption. While these proposals raise strong privacy concerns, proponents of the solutions have argued that the risk is limited as the technology has a limited scope: detecting known illegal content. In this paper, we show that modern perceptual hashing algorithms are actually fairly flexible pieces of technology and that this flexibility could be used by an adversary to add a secondary hidden feature to a client-side scanning system. More specifically, we show that an adversary providing the PH algorithm can \"hide\" a secondary purpose of face recognition of a target individual alongside its primary purpose of image copy detection. We first propose a procedure to train a dual-purpose deep perceptual hashing model by jointly optimizing for both the image copy detection and the targeted facial recognition task. Second, we extensively evaluate our dual-purpose model and show it to be able to reliably identify a target individual 67% of the time while not impacting its performance at detecting illegal content. We also show that our model is neither a general face detection nor a facial recognition model, allowing its secondary purpose to be hidden. Finally, we show that the secondary purpose can be enabled by adding a single illegal looking image to the database. Taken together, our results raise concerns that a deep perceptual hashing-based CSS system could turn billions of user devices into tools to locate targeted individuals.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114762602","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179440
Lukas Giner, Stefan Steinegger, Antoon Purnal, Maria Eichlseder, Thomas Unterluggauer, S. Mangard, D. Gruss
In this paper, we propose SassCache, a secure skewed associative cache with keyed index mapping. For this purpose, we design a new two-layered, low-latency cryptographic construction with configurable output coverage based on state-of-the-art cryptographic primitives. Based on this construction, SassCache is the first secure randomized cache with secure spacing. Victim cache lines automatically hide in locations the attacker cannot reach after less than 1 access on average. Consequently, attackers cannot evict the cache line, no matter which and how many memory accesses they perform. Our security analysis shows that all existing techniques for eviction set construction fail, and state-of-the-art attacks only apply to 1 in 3 million addresses, where SassCache is still as secure as ScatterCache. Compared to standard caches, Sass Cache has a single-threaded performance penalty of 1.75 % on the last-level cache hit rate in the SPEC2017 benchmark, and an average decrease of 11.7 p.p. in hit rate for MiBench, GAP and Scimark for our high-security settings.
{"title":"Scatter and Split Securely: Defeating Cache Contention and Occupancy Attacks","authors":"Lukas Giner, Stefan Steinegger, Antoon Purnal, Maria Eichlseder, Thomas Unterluggauer, S. Mangard, D. Gruss","doi":"10.1109/SP46215.2023.10179440","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179440","url":null,"abstract":"In this paper, we propose SassCache, a secure skewed associative cache with keyed index mapping. For this purpose, we design a new two-layered, low-latency cryptographic construction with configurable output coverage based on state-of-the-art cryptographic primitives. Based on this construction, SassCache is the first secure randomized cache with secure spacing. Victim cache lines automatically hide in locations the attacker cannot reach after less than 1 access on average. Consequently, attackers cannot evict the cache line, no matter which and how many memory accesses they perform. Our security analysis shows that all existing techniques for eviction set construction fail, and state-of-the-art attacks only apply to 1 in 3 million addresses, where SassCache is still as secure as ScatterCache. Compared to standard caches, Sass Cache has a single-threaded performance penalty of 1.75 % on the last-level cache hit rate in the SPEC2017 benchmark, and an average decrease of 11.7 p.p. in hit rate for MiBench, GAP and Scimark for our high-security settings.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124940333","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179317
Erik Trickel, Fabio Pagani, Chang Zhu, Lukas Dresel, G. Vigna, Christopher Kruegel, Ruoyu Wang, Tiffany Bao, Yan Shoshitaishvili, Adam Doupé
Black-box web application vulnerability scanners attempt to automatically identify vulnerabilities in web applications without access to the source code. However, they do so by using a manually curated list of vulnerability-inducing inputs, which significantly reduces the ability of a black-box scanner to explore the web application’s input space and which can cause false negatives. In addition, black-box scanners must attempt to infer that a vulnerability was triggered, which causes false positives.To overcome these limitations, we propose Witcher, a novel web vulnerability discovery framework that is inspired by grey-box coverage-guided fuzzing. Witcher implements the concept of fault escalation to detect both SQL and command injection vulnerabilities. Additionally, Witcher captures coverage information and creates output-derived input guidance to focus the input generation and, therefore, to increase the state-space exploration of the web application. On a dataset of 18 web applications written in PHP, Python, Node.js, Java, Ruby, and C, 13 of which had known vulnerabilities, Witcher was able to find 23 of the 36 known vulnerabilities (64%), and additionally found 67 previously unknown vulnerabilities, 4 of which received CVE numbers. In our experiments, Witcher outperformed state of the art scanners both in terms of number of vulnerabilities found, but also in terms of coverage of web applications.
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