Pub Date : 2020-12-10DOI: 10.1109/SP40001.2021.00108
Yunang Chen, Amrita Roy Chowdhury, Ruizhe Wang, A. Sabelfeld, Rahul Chatterjee, Earlence Fernandes
Trigger-action platforms (TAPs) allow users to connect independent web-based or IoT services to achieve useful automation. They provide a simple interface that helps end-users create trigger-compute-action rules that pass data between disparate Internet services. Unfortunately, TAPs introduce a large-scale security risk: if they are compromised, attackers will gain access to sensitive data for millions of users. To avoid this risk, we propose eTAP, a privacy-enhancing trigger-action platform that executes trigger-compute-action rules without accessing users’ private data in plaintext or learning anything about the results of the computation. We use garbled circuits as a primitive, and leverage the unique structure of trigger-compute-action rules to make them practical. We formally state and prove the security guarantees of our protocols. We prototyped eTAP, which supports the most commonly used operations on popular commercial TAPs like IFTTT and Zapier. Specifically, it supports Boolean, arithmetic, and string operations on private trigger data and can run 100% of the top-500 rules of IFTTT users and 93.4% of all publicly-available rules on Zapier. Based on ten existing rules that exercise a wide variety of operations, we show that eTAP has a modest performance impact: on average rule execution latency increases by 70 ms (55%) and throughput reduces by 59%.
{"title":"Data Privacy in Trigger-Action Systems","authors":"Yunang Chen, Amrita Roy Chowdhury, Ruizhe Wang, A. Sabelfeld, Rahul Chatterjee, Earlence Fernandes","doi":"10.1109/SP40001.2021.00108","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00108","url":null,"abstract":"Trigger-action platforms (TAPs) allow users to connect independent web-based or IoT services to achieve useful automation. They provide a simple interface that helps end-users create trigger-compute-action rules that pass data between disparate Internet services. Unfortunately, TAPs introduce a large-scale security risk: if they are compromised, attackers will gain access to sensitive data for millions of users. To avoid this risk, we propose eTAP, a privacy-enhancing trigger-action platform that executes trigger-compute-action rules without accessing users’ private data in plaintext or learning anything about the results of the computation. We use garbled circuits as a primitive, and leverage the unique structure of trigger-compute-action rules to make them practical. We formally state and prove the security guarantees of our protocols. We prototyped eTAP, which supports the most commonly used operations on popular commercial TAPs like IFTTT and Zapier. Specifically, it supports Boolean, arithmetic, and string operations on private trigger data and can run 100% of the top-500 rules of IFTTT users and 93.4% of all publicly-available rules on Zapier. Based on ten existing rules that exercise a wide variety of operations, we show that eTAP has a modest performance impact: on average rule execution latency increases by 70 ms (55%) and throughput reduces by 59%.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"7 1","pages":"501-518"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80887343","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 : 2020-10-20DOI: 10.1109/SP40001.2021.00041
Platon Kotzias, Juan Caballero, Leyla Bilge
Android is the most popular operating system with billions of active devices. Unfortunately, its popularity and openness makes it attractive for unwanted apps, i.e., malware and potentially unwanted programs (PUP). In Android, app installations typically happen via the official and alternative markets, but also via other smaller and less understood alternative distribution vectors such as Web downloads, pay-per-install (PPI) services, backup restoration, bloatware, and IM tools. This work performs a thorough investigation on unwanted app distribution by quantifying and comparing distribution through different vectors. At the core of our measurements are reputation logs of a large security vendor, which include 7.9M apps observed in 12M devices between June and September 2019. As a first step, we measure that between 10% and 24% of users devices encounter at least one unwanted app, and compare the prevalence of malware and PUP. An analysis of the who-installs-who relationships between installers and child apps reveals that the Play market is the main app distribution vector, responsible for 87% of all installs and 67% of unwanted app installs, but it also has the best defenses against unwanted apps. Alternative markets distribute instead 5.7% of all apps, but over 10% of unwanted apps. Bloatware is also a significant unwanted app distribution vector with 6% of those installs. And, backup restoration is an unintentional distribution vector that may even allow unwanted apps to survive users’ phone replacement. We estimate unwanted app distribution via PPI to be smaller than on Windows. Finally, we observe that Web downloads are rare, but provide a riskier proposition even compared to alternative markets.
{"title":"How Did That Get In My Phone? Unwanted App Distribution on Android Devices","authors":"Platon Kotzias, Juan Caballero, Leyla Bilge","doi":"10.1109/SP40001.2021.00041","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00041","url":null,"abstract":"Android is the most popular operating system with billions of active devices. Unfortunately, its popularity and openness makes it attractive for unwanted apps, i.e., malware and potentially unwanted programs (PUP). In Android, app installations typically happen via the official and alternative markets, but also via other smaller and less understood alternative distribution vectors such as Web downloads, pay-per-install (PPI) services, backup restoration, bloatware, and IM tools. This work performs a thorough investigation on unwanted app distribution by quantifying and comparing distribution through different vectors. At the core of our measurements are reputation logs of a large security vendor, which include 7.9M apps observed in 12M devices between June and September 2019. As a first step, we measure that between 10% and 24% of users devices encounter at least one unwanted app, and compare the prevalence of malware and PUP. An analysis of the who-installs-who relationships between installers and child apps reveals that the Play market is the main app distribution vector, responsible for 87% of all installs and 67% of unwanted app installs, but it also has the best defenses against unwanted apps. Alternative markets distribute instead 5.7% of all apps, but over 10% of unwanted apps. Bloatware is also a significant unwanted app distribution vector with 6% of those installs. And, backup restoration is an unintentional distribution vector that may even allow unwanted apps to survive users’ phone replacement. We estimate unwanted app distribution via PPI to be smaller than on Windows. Finally, we observe that Web downloads are rare, but provide a riskier proposition even compared to alternative markets.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"56 1","pages":"53-69"},"PeriodicalIF":0.0,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82370526","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 : 2020-10-01DOI: 10.1109/SP40001.2021.00039
Antoine Delignat-Lavaud, C. Fournet, Bryan Parno, Jonathan Protzenko, T. Ramananandro, Jay Bosamiya, Joseph Lallemand, Itsaka Rakotonirina, Yi Zhou
Drawing on earlier protocol-verification work, we investigate the security of the QUIC record layer, as standardized by the IETF in draft version 30. This version features major differences compared to Google’s original protocol and early IETF drafts. It serves as a useful test case for our verification methodology and toolchain, while also, hopefully, drawing attention to a little studied yet crucially important emerging standard.We model QUIC packet and header encryption, which uses a custom construction for privacy. To capture its goals, we propose a security definition for authenticated encryption with semi-implicit nonces. We show that QUIC uses an instance of a generic construction parameterized by a standard AEAD-secure scheme and a PRF-secure cipher. We formalize and verify the security of this construction in F. The proof uncovers interesting limitations of nonce confidentiality, due to the malleability of short headers and the ability to choose the number of least significant bits included in the packet counter. We propose improvements that simplify the proof and increase robustness against strong attacker models. In addition to the verified security model, we also give a concrete functional specification for the record layer, and prove that it satisfies important functionality properties (such as the correct successful decryption of encrypted packets) after fixing more errors in the draft. We then provide a high-performance implementation of the record layer that we prove to be memory safe, correct with respect to our concrete specification (inheriting its functional correctness properties), and secure with respect to our verified model. To evaluate this component, we develop a provably-safe implementation of the rest of the QUIC protocol. Our record layer achieves nearly 2 GB/s throughput, and our QUIC implementation’s performance is within 21% of an unverified baseline.
{"title":"A Security Model and Fully Verified Implementation for the IETF QUIC Record Layer","authors":"Antoine Delignat-Lavaud, C. Fournet, Bryan Parno, Jonathan Protzenko, T. Ramananandro, Jay Bosamiya, Joseph Lallemand, Itsaka Rakotonirina, Yi Zhou","doi":"10.1109/SP40001.2021.00039","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00039","url":null,"abstract":"Drawing on earlier protocol-verification work, we investigate the security of the QUIC record layer, as standardized by the IETF in draft version 30. This version features major differences compared to Google’s original protocol and early IETF drafts. It serves as a useful test case for our verification methodology and toolchain, while also, hopefully, drawing attention to a little studied yet crucially important emerging standard.We model QUIC packet and header encryption, which uses a custom construction for privacy. To capture its goals, we propose a security definition for authenticated encryption with semi-implicit nonces. We show that QUIC uses an instance of a generic construction parameterized by a standard AEAD-secure scheme and a PRF-secure cipher. We formalize and verify the security of this construction in F. The proof uncovers interesting limitations of nonce confidentiality, due to the malleability of short headers and the ability to choose the number of least significant bits included in the packet counter. We propose improvements that simplify the proof and increase robustness against strong attacker models. In addition to the verified security model, we also give a concrete functional specification for the record layer, and prove that it satisfies important functionality properties (such as the correct successful decryption of encrypted packets) after fixing more errors in the draft. We then provide a high-performance implementation of the record layer that we prove to be memory safe, correct with respect to our concrete specification (inheriting its functional correctness properties), and secure with respect to our verified model. To evaluate this component, we develop a provably-safe implementation of the rest of the QUIC protocol. Our record layer achieves nearly 2 GB/s throughput, and our QUIC implementation’s performance is within 21% of an unverified baseline.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"1 1","pages":"1162-1178"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84977140","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 : 2020-09-29DOI: 10.1109/SP40001.2021.00027
Liyi Zhou, Kaihua Qin, C. F. Torres, D. Le, Arthur Gervais
Decentralized exchanges (DEXs) allow parties to participate in financial markets while retaining full custody of their funds. However, the transparency of blockchain-based DEX in combination with the latency for transactions to be processed, makes market-manipulation feasible. For instance, adversaries could perform front-running — the practice of exploiting (typically non-public) information that may change the price of an asset for financial gain.In this work we formalize, analytically exposit and empirically evaluate an augmented variant of front-running: sandwich attacks, which involve front- and back-running victim transactions on a blockchain-based DEX. We quantify the probability of an adversarial trader being able to undertake the attack, based on the relative positioning of a transaction within a blockchain block. We find that a single adversarial trader can earn a daily revenue of over several thousand USD when performing sandwich attacks on one particular DEX — Uniswap, an exchange with over 5M USD daily trading volume by June 2020. In addition to a single-adversary game, we simulate the outcome of sandwich attacks under multiple competing adversaries, to account for the real-world trading environment.
{"title":"High-Frequency Trading on Decentralized On-Chain Exchanges","authors":"Liyi Zhou, Kaihua Qin, C. F. Torres, D. Le, Arthur Gervais","doi":"10.1109/SP40001.2021.00027","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00027","url":null,"abstract":"Decentralized exchanges (DEXs) allow parties to participate in financial markets while retaining full custody of their funds. However, the transparency of blockchain-based DEX in combination with the latency for transactions to be processed, makes market-manipulation feasible. For instance, adversaries could perform front-running — the practice of exploiting (typically non-public) information that may change the price of an asset for financial gain.In this work we formalize, analytically exposit and empirically evaluate an augmented variant of front-running: sandwich attacks, which involve front- and back-running victim transactions on a blockchain-based DEX. We quantify the probability of an adversarial trader being able to undertake the attack, based on the relative positioning of a transaction within a blockchain block. We find that a single adversarial trader can earn a daily revenue of over several thousand USD when performing sandwich attacks on one particular DEX — Uniswap, an exchange with over 5M USD daily trading volume by June 2020. In addition to a single-adversary game, we simulate the outcome of sandwich attacks under multiple competing adversaries, to account for the real-world trading environment.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"117 1","pages":"428-445"},"PeriodicalIF":0.0,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83944843","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 : 2020-09-10DOI: 10.1109/SP40001.2021.00045
Joachim Neu, Ertem Nusret Tas, David Tse
The CAP theorem says that no blockchain can be live under dynamic participation and safe under temporary network partitions. To resolve this availability-finality dilemma, we formulate a new class of flexible consensus protocols, ebb-and-flow protocols, which support a full dynamically available ledger in conjunction with a finalized prefix ledger. The finalized ledger falls behind the full ledger when the network partitions but catches up when the network heals. Gasper, the current candidate protocol for Ethereum 2.0’s beacon chain, combines the finality gadget Casper FFG with the LMD GHOST fork choice rule and aims to achieve this property. However, we discovered an attack in the standard synchronous network model, highlighting a general difficulty with existing finality-gadget-based designs. We present a construction of provably secure ebb-and-flow protocols with optimal resilience. Nodes run an off-the-shelf dynamically available protocol, take snapshots of the growing available ledger, and input them into a separate off-the-shelf BFT protocol to finalize a prefix. We explore connections with flexible BFT and improve upon the state-of-the-art for that problem.
{"title":"Ebb-and-Flow Protocols: A Resolution of the Availability-Finality Dilemma","authors":"Joachim Neu, Ertem Nusret Tas, David Tse","doi":"10.1109/SP40001.2021.00045","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00045","url":null,"abstract":"The CAP theorem says that no blockchain can be live under dynamic participation and safe under temporary network partitions. To resolve this availability-finality dilemma, we formulate a new class of flexible consensus protocols, ebb-and-flow protocols, which support a full dynamically available ledger in conjunction with a finalized prefix ledger. The finalized ledger falls behind the full ledger when the network partitions but catches up when the network heals. Gasper, the current candidate protocol for Ethereum 2.0’s beacon chain, combines the finality gadget Casper FFG with the LMD GHOST fork choice rule and aims to achieve this property. However, we discovered an attack in the standard synchronous network model, highlighting a general difficulty with existing finality-gadget-based designs. We present a construction of provably secure ebb-and-flow protocols with optimal resilience. Nodes run an off-the-shelf dynamically available protocol, take snapshots of the growing available ledger, and input them into a separate off-the-shelf BFT protocol to finalize a prefix. We explore connections with flexible BFT and improve upon the state-of-the-art for that problem.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"98 1-2 1","pages":"446-465"},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78141616","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 : 2020-09-09DOI: 10.1109/SP40001.2021.00029
Thilo Krachenfels, F. Ganji, A. Moradi, Shahin Tajik, Jean-Pierre Seifert
Due to its sound theoretical basis and practical efficiency, masking has become the most prominent countermeasure to protect cryptographic implementations against physical side-channel attacks (SCAs). The core idea of masking is to randomly split every sensitive intermediate variable during computation into at least t+1 shares, where t denotes the maximum number of shares that are allowed to be observed by an adversary without learning any sensitive information. In other words, it is assumed that the adversary is bounded either by the possessed number of probes (e.g., microprobe needles) or by the order of statistical analyses while conducting higher-order SCA attacks (e.g., differential power analysis). Such bounded models are employed to prove the SCA security of the corresponding implementations. Consequently, it is believed that given a sufficiently large number of shares, the vast majority of known SCA attacks are mitigated.In this work, we present a novel laser-assisted SCA technique, called Laser Logic State Imaging (LLSI), which offers an unlimited number of contactless probes, and therefore, violates the probing security model assumption. This technique enables us to take snapshots of hardware implementations, i.e., extract the logical state of all registers at any arbitrary clock cycle with a single measurement. To validate this, we mount our attack on masked AES hardware implementations and practically demonstrate the extraction of the full-length key in two different scenarios. First, we assume that the location of the registers (key and/or state) is known, and hence, their content can be directly read by a single snapshot. Second, we consider an implementation with unknown register locations, where we make use of multiple snapshots and a SAT solver to reveal the secrets.
{"title":"Real-World Snapshots vs. Theory: Questioning the t-Probing Security Model","authors":"Thilo Krachenfels, F. Ganji, A. Moradi, Shahin Tajik, Jean-Pierre Seifert","doi":"10.1109/SP40001.2021.00029","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00029","url":null,"abstract":"Due to its sound theoretical basis and practical efficiency, masking has become the most prominent countermeasure to protect cryptographic implementations against physical side-channel attacks (SCAs). The core idea of masking is to randomly split every sensitive intermediate variable during computation into at least t+1 shares, where t denotes the maximum number of shares that are allowed to be observed by an adversary without learning any sensitive information. In other words, it is assumed that the adversary is bounded either by the possessed number of probes (e.g., microprobe needles) or by the order of statistical analyses while conducting higher-order SCA attacks (e.g., differential power analysis). Such bounded models are employed to prove the SCA security of the corresponding implementations. Consequently, it is believed that given a sufficiently large number of shares, the vast majority of known SCA attacks are mitigated.In this work, we present a novel laser-assisted SCA technique, called Laser Logic State Imaging (LLSI), which offers an unlimited number of contactless probes, and therefore, violates the probing security model assumption. This technique enables us to take snapshots of hardware implementations, i.e., extract the logical state of all registers at any arbitrary clock cycle with a single measurement. To validate this, we mount our attack on masked AES hardware implementations and practically demonstrate the extraction of the full-length key in two different scenarios. First, we assume that the location of the registers (key and/or state) is known, and hence, their content can be directly read by a single snapshot. Second, we consider an implementation with unknown register locations, where we make use of multiple snapshots and a SAT solver to reveal the secrets.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"11 1","pages":"1955-1971"},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75226061","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 : 2020-09-08DOI: 10.1109/SP40001.2021.00061
Chen Ling, Utkucan Balci, Jeremy Blackburn, G. Stringhini
Online meeting tools like Zoom and Google Meet have become central to our professional, educational, and personal lives. This has opened up new opportunities for large scale harassment. In particular, a phenomenon known as zoombombing has emerged, in which aggressors join online meetings with the goal of disrupting them and harassing their participants. In this paper, we conduct the first data-driven analysis of calls for zoombombing attacks on social media. We identify ten popular online meeting tools and extract posts containing meeting invitations to these platforms on a mainstream social network, Twitter, and on a fringe community known for organizing coordinated attacks against online users, 4chan. We then perform manual annotation to identify posts that are calling for zoombombing attacks, and apply thematic analysis to develop a codebook to better characterize the discussion surrounding calls for zoombombing. During the first seven months of 2020, we identify over 200 calls for zoombombing between Twitter and 4chan, and analyze these calls both quantitatively and qualitatively. Our findings indicate that the vast majority of calls for zoombombing are not made by attackers stumbling upon meeting invitations or bruteforcing their meeting ID, but rather by insiders who have legitimate access to these meetings, particularly students in high school and college classes. This has important security implications because it makes common protections against zoombombing, e.g., password protection, ineffective. We also find instances of insiders instructing attackers to adopt the names of legitimate participants in the class to avoid detection, making countermeasures like setting up a waiting room and vetting participants less effective. Based on these observations, we argue that the only effective defense against zoombombing is creating unique join links for each participant.
{"title":"A First Look at Zoombombing","authors":"Chen Ling, Utkucan Balci, Jeremy Blackburn, G. Stringhini","doi":"10.1109/SP40001.2021.00061","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00061","url":null,"abstract":"Online meeting tools like Zoom and Google Meet have become central to our professional, educational, and personal lives. This has opened up new opportunities for large scale harassment. In particular, a phenomenon known as zoombombing has emerged, in which aggressors join online meetings with the goal of disrupting them and harassing their participants. In this paper, we conduct the first data-driven analysis of calls for zoombombing attacks on social media. We identify ten popular online meeting tools and extract posts containing meeting invitations to these platforms on a mainstream social network, Twitter, and on a fringe community known for organizing coordinated attacks against online users, 4chan. We then perform manual annotation to identify posts that are calling for zoombombing attacks, and apply thematic analysis to develop a codebook to better characterize the discussion surrounding calls for zoombombing. During the first seven months of 2020, we identify over 200 calls for zoombombing between Twitter and 4chan, and analyze these calls both quantitatively and qualitatively. Our findings indicate that the vast majority of calls for zoombombing are not made by attackers stumbling upon meeting invitations or bruteforcing their meeting ID, but rather by insiders who have legitimate access to these meetings, particularly students in high school and college classes. This has important security implications because it makes common protections against zoombombing, e.g., password protection, ineffective. We also find instances of insiders instructing attackers to adopt the names of legitimate participants in the class to avoid detection, making countermeasures like setting up a waiting room and vetting participants less effective. Based on these observations, we argue that the only effective defense against zoombombing is creating unique join links for each participant.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"11 1","pages":"1452-1467"},"PeriodicalIF":0.0,"publicationDate":"2020-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84529637","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 : 2020-09-07DOI: 10.1109/SP40001.2021.00083
Sahar Abdelnabi, Mario Fritz
Recent advances in natural language generation have introduced powerful language models with high-quality output text. However, this raises concerns about the potential misuse of such models for malicious purposes. In this paper, we study natural language watermarking as a defense to help better mark and trace the provenance of text. We introduce the Adversarial Watermarking Transformer (AWT) with a jointly trained encoder-decoder and adversarial training that, given an input text and a binary message, generates an output text that is unobtrusively encoded with the given message. We further study different training and inference strategies to achieve minimal changes to the semantics and correctness of the input text.AWT is the first end-to-end model to hide data in text by automatically learning -without ground truth- word substitutions along with their locations in order to encode the message. We empirically show that our model is effective in largely preserving text utility and decoding the watermark while hiding its presence against adversaries. Additionally, we demonstrate that our method is robust against a range of attacks.
{"title":"Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding","authors":"Sahar Abdelnabi, Mario Fritz","doi":"10.1109/SP40001.2021.00083","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00083","url":null,"abstract":"Recent advances in natural language generation have introduced powerful language models with high-quality output text. However, this raises concerns about the potential misuse of such models for malicious purposes. In this paper, we study natural language watermarking as a defense to help better mark and trace the provenance of text. We introduce the Adversarial Watermarking Transformer (AWT) with a jointly trained encoder-decoder and adversarial training that, given an input text and a binary message, generates an output text that is unobtrusively encoded with the given message. We further study different training and inference strategies to achieve minimal changes to the semantics and correctness of the input text.AWT is the first end-to-end model to hide data in text by automatically learning -without ground truth- word substitutions along with their locations in order to encode the message. We empirically show that our model is effective in largely preserving text utility and decoding the watermark while hiding its presence against adversaries. Additionally, we demonstrate that our method is robust against a range of attacks.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"72 1","pages":"121-140"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75816623","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 : 2020-08-11DOI: 10.1109/SP40001.2021.00017
Umar Iqbal, Steven Englehardt, Zubair Shafiq
Browser fingerprinting is an invasive and opaque stateless tracking technique. Browser vendors, academics, and standards bodies have long struggled to provide meaningful protections against browser fingerprinting that are both accurate and do not degrade user experience. We propose FP-Inspector, a machine learning based syntactic-semantic approach to accurately detect browser fingerprinting. We show that FP-Inspector performs well, allowing us to detect 26% more fingerprinting scripts than the state-of-the-art. We show that an API-level fingerprinting countermeasure, built upon FP-Inspector, helps reduce website breakage by a factor of 2. We use FP-Inspector to perform a measurement study of browser fingerprinting on top-100K websites. We find that browser fingerprinting is now present on more than 10% of the top-100K websites and over a quarter of the top-10K websites. We also discover previously unreported uses of JavaScript APIs by fingerprinting scripts suggesting that they are looking to exploit APIs in new and unexpected ways.
{"title":"Fingerprinting the Fingerprinters: Learning to Detect Browser Fingerprinting Behaviors","authors":"Umar Iqbal, Steven Englehardt, Zubair Shafiq","doi":"10.1109/SP40001.2021.00017","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00017","url":null,"abstract":"Browser fingerprinting is an invasive and opaque stateless tracking technique. Browser vendors, academics, and standards bodies have long struggled to provide meaningful protections against browser fingerprinting that are both accurate and do not degrade user experience. We propose FP-Inspector, a machine learning based syntactic-semantic approach to accurately detect browser fingerprinting. We show that FP-Inspector performs well, allowing us to detect 26% more fingerprinting scripts than the state-of-the-art. We show that an API-level fingerprinting countermeasure, built upon FP-Inspector, helps reduce website breakage by a factor of 2. We use FP-Inspector to perform a measurement study of browser fingerprinting on top-100K websites. We find that browser fingerprinting is now present on more than 10% of the top-100K websites and over a quarter of the top-10K websites. We also discover previously unreported uses of JavaScript APIs by fingerprinting scripts suggesting that they are looking to exploit APIs in new and unexpected ways.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"3 1","pages":"1143-1161"},"PeriodicalIF":0.0,"publicationDate":"2020-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86380751","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}
Cache randomization has recently been revived as a promising defense against conflict-based cache side-channel attacks. As two of the latest implementations, CEASER-S and ScatterCache both claim to thwart conflict-based cache side-channel attacks using randomized skewed caches. Unfortunately, our experiments show that an attacker can easily find a usable eviction set within the chosen remap period of CEASER-S and increasing the number of partitions without dynamic remapping, such as ScatterCache, cannot eliminate the threat. By quantitatively analyzing the access patterns left by various attacks in the LLC, we have newly discovered several problems with the hypotheses and implementations of randomized caches, which are also overlooked by the research on conflict-based cache side-channel attacks.However, cache randomization is not a false hope and it is an effective defense that should be widely adopted in future processors. The newly discovered problems are corresponding to flaws associated with the existing implementation of cache randomization and are fixable. Several new defense ideas are proposed in this paper. Our experiments show that all the newly discovered problems are fixed within the current performance budget. We also argue that randomized set-associative caches can be sufficiently strengthened and possess a better chance to be actually adopted in commercial processors than their skewed counterparts because they introduce less overhaul to the existing cache structure.
{"title":"Randomized Last-Level Caches Are Still Vulnerable to Cache Side-Channel Attacks! But We Can Fix It","authors":"Wei Song, Boya Li, Zihan Xue, Zhenzhen Li, Wenhao Wang, Peng Liu","doi":"10.1109/SP40001.2021.00050","DOIUrl":"https://doi.org/10.1109/SP40001.2021.00050","url":null,"abstract":"Cache randomization has recently been revived as a promising defense against conflict-based cache side-channel attacks. As two of the latest implementations, CEASER-S and ScatterCache both claim to thwart conflict-based cache side-channel attacks using randomized skewed caches. Unfortunately, our experiments show that an attacker can easily find a usable eviction set within the chosen remap period of CEASER-S and increasing the number of partitions without dynamic remapping, such as ScatterCache, cannot eliminate the threat. By quantitatively analyzing the access patterns left by various attacks in the LLC, we have newly discovered several problems with the hypotheses and implementations of randomized caches, which are also overlooked by the research on conflict-based cache side-channel attacks.However, cache randomization is not a false hope and it is an effective defense that should be widely adopted in future processors. The newly discovered problems are corresponding to flaws associated with the existing implementation of cache randomization and are fixable. Several new defense ideas are proposed in this paper. Our experiments show that all the newly discovered problems are fixed within the current performance budget. We also argue that randomized set-associative caches can be sufficiently strengthened and possess a better chance to be actually adopted in commercial processors than their skewed counterparts because they introduce less overhaul to the existing cache structure.","PeriodicalId":6786,"journal":{"name":"2021 IEEE Symposium on Security and Privacy (SP)","volume":"15 2","pages":"955-969"},"PeriodicalIF":0.0,"publicationDate":"2020-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91436228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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