It is widely known that return-oriented programming (ROP) attack can be mounted on x86, ARM and SPARC architectures. However, it remained an open question if ROP was possible on RISC-V, a new and promising free and open instruction set architecture (ISA). In this paper we present a novel ROP technique specific to RISC-V architecture. Our method relies on the processor's saved registers and its function calling convention. We use functional gadgets (that perform primitive operations) ended in a jump instruction to an address held in a saved register. The order of gadgets chaining is given by a novel gadget, which we call the charger gadget, which loads the saved registers with the gadgets? addresses from the stack. We constructed a library of gadgets extracted from the standard Linux libraries. Finally, we evaluated our method by exploiting a buffer-overflow vulnerable application.
{"title":"Following the Pebble Trail: Extending Return-Oriented Programming to RISC-V","authors":"Bogdan Pavel Deac, Adrian Colesa","doi":"10.1145/3411495.3421366","DOIUrl":"https://doi.org/10.1145/3411495.3421366","url":null,"abstract":"It is widely known that return-oriented programming (ROP) attack can be mounted on x86, ARM and SPARC architectures. However, it remained an open question if ROP was possible on RISC-V, a new and promising free and open instruction set architecture (ISA). In this paper we present a novel ROP technique specific to RISC-V architecture. Our method relies on the processor's saved registers and its function calling convention. We use functional gadgets (that perform primitive operations) ended in a jump instruction to an address held in a saved register. The order of gadgets chaining is given by a novel gadget, which we call the charger gadget, which loads the saved registers with the gadgets? addresses from the stack. We constructed a library of gadgets extracted from the standard Linux libraries. Finally, we evaluated our method by exploiting a buffer-overflow vulnerable application.","PeriodicalId":125943,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128850874","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}
Yujun Qin, Samuel Gonzalez, K. Angstadt, Xiaowei Wang, S. Forrest, R. Das, Kevin Leach, Westley Weimer
Hardware architectural vulnerabilities, such as Spectre and Meltdown, are difficult or inefficient to mitigate in software. Although revised hardware designs may address some architectural vulnerabilities going forward, most current remedies increase execution time significantly. Techniques are needed to rapidly and efficiently detect these and other emerging threats. We present an anomaly detector, MARTINI, that analyzes traces of memory accesses in real time to detect attacks. Our experimental evaluation shows that anomalies in these traces are strongly correlated with unauthorized program execution, including architectural side-channel attacks of multiple types. MARTINI consists of a finite automaton that models normal program behavior in terms of memory addresses that are read from, and written to, at runtime. The model uses a compact representation of n-grams, i.e., short sequences of memory accesses, which can be stored and processed efficiently. Once the system is trained on authorized behavior, it rapidly detects a variety of low-level anomalous behaviors and attacks not otherwise easily discernible at the software level. MARTINI's implementation leverages recent advances in in-cache and in-memory automata for computation, and we present a hardware unit that repurposes a small portion of a last-level cache slice to monitor memory addresses. Our detector directly inspects the addresses of memory accesses, using the pre-constructed automaton to identify anomalies with high accuracy, negligible runtime overhead, and trivial increase in CPU chip area. We present analyses of expected hardware properties based on indicative cache and memory hierarchy simulations and empirical evaluations.
{"title":"MARTINI: Memory Access Traces to Detect Attacks","authors":"Yujun Qin, Samuel Gonzalez, K. Angstadt, Xiaowei Wang, S. Forrest, R. Das, Kevin Leach, Westley Weimer","doi":"10.1145/3411495.3421353","DOIUrl":"https://doi.org/10.1145/3411495.3421353","url":null,"abstract":"Hardware architectural vulnerabilities, such as Spectre and Meltdown, are difficult or inefficient to mitigate in software. Although revised hardware designs may address some architectural vulnerabilities going forward, most current remedies increase execution time significantly. Techniques are needed to rapidly and efficiently detect these and other emerging threats. We present an anomaly detector, MARTINI, that analyzes traces of memory accesses in real time to detect attacks. Our experimental evaluation shows that anomalies in these traces are strongly correlated with unauthorized program execution, including architectural side-channel attacks of multiple types. MARTINI consists of a finite automaton that models normal program behavior in terms of memory addresses that are read from, and written to, at runtime. The model uses a compact representation of n-grams, i.e., short sequences of memory accesses, which can be stored and processed efficiently. Once the system is trained on authorized behavior, it rapidly detects a variety of low-level anomalous behaviors and attacks not otherwise easily discernible at the software level. MARTINI's implementation leverages recent advances in in-cache and in-memory automata for computation, and we present a hardware unit that repurposes a small portion of a last-level cache slice to monitor memory addresses. Our detector directly inspects the addresses of memory accesses, using the pre-constructed automaton to identify anomalies with high accuracy, negligible runtime overhead, and trivial increase in CPU chip area. We present analyses of expected hardware properties based on indicative cache and memory hierarchy simulations and empirical evaluations.","PeriodicalId":125943,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114142874","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}
On today's Internet, combining the end-to-end security of TLS with Content Delivery Networks (CDNs) while ensuring the authenticity of connections results in a challenging delegation problem. When CDN servers provide content, they have to authenticate themselves as the origin server to establish a valid end-to-end TLS connection with the client. In standard TLS, the latter requires access to the secret key of the server. To curb this problem, multiple workarounds exist to realize a delegation of the authentication. In this paper, we present a solution that renders key sharing unnecessary and reduces the need for workarounds. By adapting identity-based signatures to this setting, our solution offers short-lived delegations. Additionally, by enabling forward-security, existing delegations remain valid even if the server's secret key leaks. We provide an implementation of the scheme and discuss integration into a TLS stack. In our evaluation, we show that an efficient implementation incurs less overhead than a typical network round trip. Thereby, we propose an alternative approach to current delegation practices on the web.
{"title":"Short-Lived Forward-Secure Delegation for TLS","authors":"Lukas Alber, Stefan More, Sebastian Ramacher","doi":"10.1145/3411495.3421362","DOIUrl":"https://doi.org/10.1145/3411495.3421362","url":null,"abstract":"On today's Internet, combining the end-to-end security of TLS with Content Delivery Networks (CDNs) while ensuring the authenticity of connections results in a challenging delegation problem. When CDN servers provide content, they have to authenticate themselves as the origin server to establish a valid end-to-end TLS connection with the client. In standard TLS, the latter requires access to the secret key of the server. To curb this problem, multiple workarounds exist to realize a delegation of the authentication. In this paper, we present a solution that renders key sharing unnecessary and reduces the need for workarounds. By adapting identity-based signatures to this setting, our solution offers short-lived delegations. Additionally, by enabling forward-security, existing delegations remain valid even if the server's secret key leaks. We provide an implementation of the scheme and discuss integration into a TLS stack. In our evaluation, we show that an efficient implementation incurs less overhead than a typical network round trip. Thereby, we propose an alternative approach to current delegation practices on the web.","PeriodicalId":125943,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130140662","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}
Ralf Ramsauer, Lukas Bulwahn, D. Lohmann, W. Mauerer
Public development processes are a key characteristic of open source projects. However, fixes for vulnerabilities are usually discussed privately among a small group of trusted maintainers, and integrated without prior public involvement. This is supposed to prevent early disclosure, and cope with embargo and non-disclosure agreement (NDA) rules. While regular development activities leave publicly available traces, fixes for vulnerabilities that bypass the standard process do not. We present a data-mining based approach to detect code fragments that arise from such infringements of the standard process. By systematically mapping public development artefacts to source code repositories, we can exclude regular process activities, and infer irregularities that stem from non-public integration channels. For the Linux kernel, the most crucial component of many systems, we apply our method to a period of seven months before the release of Linux 5.4. We find 29 commits that address 12 vulnerabilities. For these vulnerabilities, our approach provides a temporal advantage of 2 to 179 days to design exploits before public disclosure takes place, and fixes are rolled out. Established responsible disclosure approaches in open development processes are supposed to limit premature visibility of security vulnerabilities. However, our approach shows that, instead, they open additional possibilities to uncover such changes that thwart the very premise. We conclude by discussing implications and partial countermeasures.
{"title":"The Sound of Silence: Mining Security Vulnerabilities from Secret Integration Channels in Open-Source Projects","authors":"Ralf Ramsauer, Lukas Bulwahn, D. Lohmann, W. Mauerer","doi":"10.1145/3411495.3421360","DOIUrl":"https://doi.org/10.1145/3411495.3421360","url":null,"abstract":"Public development processes are a key characteristic of open source projects. However, fixes for vulnerabilities are usually discussed privately among a small group of trusted maintainers, and integrated without prior public involvement. This is supposed to prevent early disclosure, and cope with embargo and non-disclosure agreement (NDA) rules. While regular development activities leave publicly available traces, fixes for vulnerabilities that bypass the standard process do not. We present a data-mining based approach to detect code fragments that arise from such infringements of the standard process. By systematically mapping public development artefacts to source code repositories, we can exclude regular process activities, and infer irregularities that stem from non-public integration channels. For the Linux kernel, the most crucial component of many systems, we apply our method to a period of seven months before the release of Linux 5.4. We find 29 commits that address 12 vulnerabilities. For these vulnerabilities, our approach provides a temporal advantage of 2 to 179 days to design exploits before public disclosure takes place, and fixes are rolled out. Established responsible disclosure approaches in open development processes are supposed to limit premature visibility of security vulnerabilities. However, our approach shows that, instead, they open additional possibilities to uncover such changes that thwart the very premise. We conclude by discussing implications and partial countermeasures.","PeriodicalId":125943,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116604847","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}
Data holders are increasingly seeking to protect their user's privacy, whilst still maximizing their ability to produce machine learning (ML) models with high quality predictions. In this work, we empirically evaluate various implementations of differential privacy (DP), and measure their ability to fend off real-world privacy attacks, in addition to measuring their core goal of providing accurate classifications. We establish an evaluation framework to ensure each of these implementations are fairly evaluated. Our selection of DP implementations add DP noise at different positions within the framework, either at the point of data collection/release, during updates while training of the model, or after training by perturbing learned model parameters. We evaluate each implementation across a range of privacy budgets and datasets, each implementation providing the same mathematical privacy guarantees. By measuring the models' resistance to real world attacks of membership and attribute inference, and their classification accuracy. we determine which implementations provide the most desirable tradeoff between privacy and utility. We found that the number of classes of a given dataset is unlikely to influence where the privacy and utility tradeoff occurs, a counter-intuitive inference in contrast to the known relationship of increased privacy vulnerability in datasets with more classes. Additionally, in the scenario that high privacy constraints are required, perturbing input training data before applying ML modeling does not trade off as much utility, as compared to noise added later in the ML process.
{"title":"Not one but many Tradeoffs: Privacy Vs. Utility in Differentially Private Machine Learning","authors":"Benjamin Zi Hao Zhao, M. Kâafar, N. Kourtellis","doi":"10.1145/3411495.3421352","DOIUrl":"https://doi.org/10.1145/3411495.3421352","url":null,"abstract":"Data holders are increasingly seeking to protect their user's privacy, whilst still maximizing their ability to produce machine learning (ML) models with high quality predictions. In this work, we empirically evaluate various implementations of differential privacy (DP), and measure their ability to fend off real-world privacy attacks, in addition to measuring their core goal of providing accurate classifications. We establish an evaluation framework to ensure each of these implementations are fairly evaluated. Our selection of DP implementations add DP noise at different positions within the framework, either at the point of data collection/release, during updates while training of the model, or after training by perturbing learned model parameters. We evaluate each implementation across a range of privacy budgets and datasets, each implementation providing the same mathematical privacy guarantees. By measuring the models' resistance to real world attacks of membership and attribute inference, and their classification accuracy. we determine which implementations provide the most desirable tradeoff between privacy and utility. We found that the number of classes of a given dataset is unlikely to influence where the privacy and utility tradeoff occurs, a counter-intuitive inference in contrast to the known relationship of increased privacy vulnerability in datasets with more classes. Additionally, in the scenario that high privacy constraints are required, perturbing input training data before applying ML modeling does not trade off as much utility, as compared to noise added later in the ML process.","PeriodicalId":125943,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130867203","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}
Voice User Interfaces (VUIs) are increasingly popular and built into smartphones, home assistants, and Internet of Things (IoT) devices. Despite offering an always-on convenient user experience, VUIs raise new security and privacy concerns for their users. In this paper, we focus on attribute inference attacks in the speech domain, demonstrating the potential for an attacker to accurately infer a target user's sensitive and private attributes (e.g. their emotion, sex, or health status) from deep acoustic models. To defend against this class of attacks, we design, implement, and evaluate a user-configurable, privacy-aware framework for optimizing speech-related data sharing mechanisms. Our objective is to enable primary tasks such as speech recognition and user identification, while removing sensitive attributes in the raw speech data before sharing it with a cloud service provider. We leverage disentangled representation learning to explicitly learn independent factors in the raw data. Based on a user's preferences, a supervision signal informs the filtering out of invariant factors while retaining the factors reflected in the selected preference. Our experimental evaluation over five datasets shows that the proposed framework can effectively defend against attribute inference attacks by reducing their success rates to approximately that of guessing at random, while maintaining accuracy in excess of 99% for the tasks of interest. We conclude that negotiable privacy settings enabled by disentangled representations can bring new opportunities for privacy-preserving applications.
{"title":"Privacy-preserving Voice Analysis via Disentangled Representations","authors":"Ranya Aloufi, H. Haddadi, David Boyle","doi":"10.1145/3411495.3421355","DOIUrl":"https://doi.org/10.1145/3411495.3421355","url":null,"abstract":"Voice User Interfaces (VUIs) are increasingly popular and built into smartphones, home assistants, and Internet of Things (IoT) devices. Despite offering an always-on convenient user experience, VUIs raise new security and privacy concerns for their users. In this paper, we focus on attribute inference attacks in the speech domain, demonstrating the potential for an attacker to accurately infer a target user's sensitive and private attributes (e.g. their emotion, sex, or health status) from deep acoustic models. To defend against this class of attacks, we design, implement, and evaluate a user-configurable, privacy-aware framework for optimizing speech-related data sharing mechanisms. Our objective is to enable primary tasks such as speech recognition and user identification, while removing sensitive attributes in the raw speech data before sharing it with a cloud service provider. We leverage disentangled representation learning to explicitly learn independent factors in the raw data. Based on a user's preferences, a supervision signal informs the filtering out of invariant factors while retaining the factors reflected in the selected preference. Our experimental evaluation over five datasets shows that the proposed framework can effectively defend against attribute inference attacks by reducing their success rates to approximately that of guessing at random, while maintaining accuracy in excess of 99% for the tasks of interest. We conclude that negotiable privacy settings enabled by disentangled representations can bring new opportunities for privacy-preserving applications.","PeriodicalId":125943,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132871265","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}
seek to influence and polarize political topics through massive coordinated efforts. In the process, these efforts leave behind artifacts, which researchers have leveraged to analyze the tactics employed by disinformation campaigns after they are taken down. Coordination network analysis has proven helpful for learning about how disinformation campaigns operate; however, the usefulness of these forensic tools as a detection mechanism is still an open question. In this paper, we explore the use of coordination network analysis to generate features for distinguishing the activity of a disinformation campaign from legitimate Twitter activity. Doing so would provide more evidence to human analysts as they consider takedowns. We create a time series of daily coordination networks for both Twitter disinformation campaigns and legitimate Twitter communities, and train a binary classifier based on statistical features extracted from these networks. Our results show that the classifier can predict future coordinated activity of known disinformation campaigns with high accuracy (F1 =0.98). On the more challenging task of out-of-distribution activity classification, the performance drops yet is still promising (F1= 0.71), mainly due to an increase in the false positive rate. By doing this analysis, we show that while coordination patterns could be useful for providing evidence of disinformation activity, further investigation is needed to improve upon this method before deployment at scale.
{"title":"On the Detection of Disinformation Campaign Activity with Network Analysis","authors":"Luis Vargas, Patrick Emami, Patrick Traynor","doi":"10.1145/3411495.3421363","DOIUrl":"https://doi.org/10.1145/3411495.3421363","url":null,"abstract":"seek to influence and polarize political topics through massive coordinated efforts. In the process, these efforts leave behind artifacts, which researchers have leveraged to analyze the tactics employed by disinformation campaigns after they are taken down. Coordination network analysis has proven helpful for learning about how disinformation campaigns operate; however, the usefulness of these forensic tools as a detection mechanism is still an open question. In this paper, we explore the use of coordination network analysis to generate features for distinguishing the activity of a disinformation campaign from legitimate Twitter activity. Doing so would provide more evidence to human analysts as they consider takedowns. We create a time series of daily coordination networks for both Twitter disinformation campaigns and legitimate Twitter communities, and train a binary classifier based on statistical features extracted from these networks. Our results show that the classifier can predict future coordinated activity of known disinformation campaigns with high accuracy (F1 =0.98). On the more challenging task of out-of-distribution activity classification, the performance drops yet is still promising (F1= 0.71), mainly due to an increase in the false positive rate. By doing this analysis, we show that while coordination patterns could be useful for providing evidence of disinformation activity, further investigation is needed to improve upon this method before deployment at scale.","PeriodicalId":125943,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128383377","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：