arXiv subjects

Xiapu Luo

Publications and source records attributed to Xiapu Luo.

At least 91 records · Page 5Linked to original sources

arXiv2022

iLibScope: Reliable Third-Party Library Detection for iOS Mobile Apps

Vetting security impacts introduced by third-party libraries in iOS apps requires a reliable library detection technique. Especially when a new vulnerability (or a privacy-invasive behavior) was discovered in a third-party library, there is a practical need to precisely identify the existence of libraries and their versions for iOS apps. However, few studies have been proposed to tackle this problem, and they all suffer from the code duplication problem in different libraries. In this paper, we focus on third-party library detection in iOS apps. Given an app, we aim to identify the integrated libraries and pinpoint their versions (or the version range).To this end, we first conduct an in-depth study on iOS third-party libraries to demystify the code duplication challenge. By doing so, we have two key observations: 1) even though two libraries can share classes, the shared classes cannot be integrated into an app simultaneously without causing a class name conflict; and 2) code duplication between multiple versions of two libraries can vary. Based on these findings, we propose a novel profile-based similarity comparison approach to perform the detection. Specifically, we build a library database consists of original library binaries with distinct versions. After extracting profiles for each library version and the target app, we conduct a similarity comparison to find the best matches. We implemented this approach in iLibScope. We built a benchmark consists of 5,807 apps with 10,495 library integrations and applied our tool to it. Our evaluation shows that iLibScope achieves a recall exceeds 99% and a precision exceeds 97% for library detection. We also applied iLibScope to detect the presence of well-known vulnerable third-party libraries in real-world iOS mobile apps to show the promising usage of our tool. It successfully identified 405 vulnerable library usage from 4,249 apps.

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arXiv2022

Aper: Evolution-Aware Runtime Permission Misuse Detection for Android Apps

The Android platform introduces the runtime permission model in version 6.0. The new model greatly improves data privacy and user experience, but brings new challenges for app developers. First, it allows users to freely revoke granted permissions. Hence, developers cannot assume that the permissions granted to an app would keep being granted. Instead, they should make their apps carefully check the permission status before invoking dangerous APIs. Second, the permission specification keeps evolving, bringing new types of compatibility issues into the ecosystem. To understand the impact of the challenges, we conducted an empirical study on 13,352 popular Google Play apps. We found that 86.0% apps used dangerous APIs asynchronously after permission management and 61.2% apps used evolving dangerous APIs. If an app does not properly handle permission revocations or platform differences, unexpected runtime issues may happen and even cause app crashes. We call such Android Runtime Permission issues as ARP bugs. Unfortunately, existing runtime permission issue detection tools cannot effectively deal with the ARP bugs induced by asynchronous permission management and permission specification evolution. To fill the gap, we designed a static analyzer, Aper, that performs reaching definition and dominator analysis on Android apps to detect the two types of ARP bugs. To compare Aper with existing tools, we built a benchmark, ARPfix, from 60 real ARP bugs. Our experiment results show that Aper significantly outperforms two academic tools, ARPDroid and RevDroid, and an industrial tool, Lint, on ARPfix, with an average improvement of 46.3% on F1-score. In addition, Aper successfully found 34 ARP bugs in 214 opensource Android apps, most of which can result in abnormal app behaviors (such as app crashes) according to our manual validation.

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arXiv2022

BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection

Graph-based Anomaly Detection (GAD) is becoming prevalent due to the powerful representation abilities of graphs as well as recent advances in graph mining techniques. These GAD tools, however, expose a new attacking surface, ironically due to their unique advantage of being able to exploit the relations among data. That is, attackers now can manipulate those relations (i.e., the structure of the graph) to allow some target nodes to evade detection. In this paper, we exploit this vulnerability by designing a new type of targeted structural poisoning attacks to a representative regression-based GAD system termed OddBall. Specially, we formulate the attack against OddBall as a bi-level optimization problem, where the key technical challenge is to efficiently solve the problem in a discrete domain. We propose a novel attack method termed BinarizedAttack based on gradient descent. Comparing to prior arts, BinarizedAttack can better use the gradient information, making it particularly suitable for solving combinatorial optimization problems. Furthermore, we investigate the attack transferability of BinarizedAttack by employing it to attack other representation-learning-based GAD systems. Our comprehensive experiments demonstrate that BinarizedAttack is very effective in enabling target nodes to evade graph-based anomaly detection tools with limited attackers' budget, and in the black-box transfer attack setting, BinarizedAttack is also tested effective and in particular, can significantly change the node embeddings learned by the GAD systems. Our research thus opens the door to studying a new type of attack against security analytic tools that rely on graph data.

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arXiv2021

Trade or Trick? Detecting and Characterizing Scam Tokens on Uniswap Decentralized Exchange

The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap V2 exchange and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least \$16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem, and our approach can act as a whistleblower that identifies scam tokens at their early stages.

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arXiv2021

ATVHunter: Reliable Version Detection of Third-Party Libraries for Vulnerability Identification in Android Applications

We propose a system, named ATVHunter, which can pinpoint the precise vulnerable in-app TPL versions and provide detailed information about the vulnerabilities and TPLs. We propose a two-phase detection approach to identify specific TPL versions. Specifically, we extract the Control Flow Graphs as the coarse-grained feature to match potential TPLs in the pre-defined TPL database, and then extract opcode in each basic block of CFG as the fine-grained feature to identify the exact TPL versions. We build a comprehensive TPL database (189,545 unique TPLs with 3,006,676 versions) as the reference database. Meanwhile, to identify the vulnerable in-app TPL versions, we also construct a comprehensive and known vulnerable TPL database containing 1,180 CVEs and 224 security bugs. Experimental results show ATVHunter outperforms state-of-the-art TPL detection tools, achieving 90.55% precision and 88.79% recall with high efficiency, and is also resilient to widely-used obfuscation techniques and scalable for large-scale TPL detection. Furthermore, to investigate the ecosystem of the vulnerable TPLs used by apps, we exploit ATVHunter to conduct a large-scale analysis on 104,446 apps and find that 9,050 apps include vulnerable TPL versions with 53,337 vulnerabilities and 7,480 security bugs, most of which are with high risks and are not recognized by app developers.

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arXiv2021

Towards Robust Reasoning over Knowledge Graphs

Answering complex logical queries over large-scale knowledge graphs (KGs) represents an important artificial intelligence task, entailing a range of applications. Recently, knowledge representation learning (KRL) has emerged as the state-of-the-art approach, wherein KG entities and the query are embedded into a latent space such that entities that answer the query are embedded close to the query. Yet, despite its surging popularity, the potential security risks of KRL are largely unexplored, which is concerning, given the increasing use of such capabilities in security-critical domains (e.g., cyber-security and healthcare). This work represents a solid initial step towards bridging this gap. We systematize the potential security threats to KRL according to the underlying attack vectors (e.g., knowledge poisoning and query perturbation) and the adversary's background knowledge. More importantly, we present ROAR(Reasoning Over Adversarial Representations), a new class of attacks that instantiate a variety of such threats. We demonstrate the practicality of ROAR in two representative use cases (i.e., cyber-threat hunting and drug repurposing). For instance, ROAR attains over 99% attack success rate in misleading the threat intelligence engine to give pre-defined answers for target queries, yet without any impact on non-target ones. Further, we discuss potential countermeasures against ROAR, including filtering of poisoning facts and robust training with adversarial queries, which leads to several promising research directions.

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arXiv2021

Understanding the Evolution of Blockchain Ecosystems: A Longitudinal Measurement Study of Bitcoin, Ethereum, and EOSIO

The continuing expansion of the blockchain ecosystems has attracted much attention from the research community. However, although a large number of research studies have been proposed to understand the diverse characteristics of individual blockchain systems (e.g., Bitcoin or Ethereum), little is known at a comprehensive level on the evolution of blockchain ecosystems at scale, longitudinally, and across multiple blockchains. We argue that understanding the dynamics of blockchain ecosystems could provide unique insights that cannot be achieved through studying a single static snapshot or a single blockchain network alone. Based on billions of transaction records collected from three representative and popular blockchain systems (Bitcoin, Ethereum and EOSIO) over 10 years, we conduct the first study on the evolution of multiple blockchain ecosystems from different perspectives. Our exploration suggests that, although the overall blockchain ecosystem shows promising growth over the last decade, a number of worrying outliers exist that have disrupted its evolution.

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arXiv2021

On the Security Risks of AutoML

Neural Architecture Search (NAS) represents an emerging machine learning (ML) paradigm that automatically searches for models tailored to given tasks, which greatly simplifies the development of ML systems and propels the trend of ML democratization. Yet, little is known about the potential security risks incurred by NAS, which is concerning given the increasing use of NAS-generated models in critical domains. This work represents a solid initial step towards bridging the gap. Through an extensive empirical study of 10 popular NAS methods, we show that compared with their manually designed counterparts, NAS-generated models tend to suffer greater vulnerability to various malicious attacks (e.g., adversarial evasion, model poisoning, and functionality stealing). Further, with both empirical and analytical evidence, we provide possible explanations for such phenomena: given the prohibitive search space and training cost, most NAS methods favor models that converge fast at early training stages; this preference results in architectural properties associated with attack vulnerability (e.g., high loss smoothness and low gradient variance). Our findings not only reveal the relationships between model characteristics and attack vulnerability but also suggest the inherent connections underlying different attacks. Finally, we discuss potential remedies to mitigate such drawbacks, including increasing cell depth and suppressing skip connects, which lead to several promising research directions.

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arXiv2021

ECMO: Peripheral Transplantation to Rehost Embedded Linux Kernels

Dynamic analysis based on the full-system emulator QEMU is widely used for various purposes. However, it is challenging to run firmware images of embedded devices in QEMU, especially the process to boot the Linux kernel (we call this process rehosting the Linux kernel.) That's because embedded devices usually use different system-on-chips (SoCs) from multiple vendors and only a limited number of SoCs are currently supported in QEMU. In this work, we propose a technique called peripheral transplantation. The main idea is to transplant the device drivers of designated peripherals into the Linux kernel. By doing so, it can replace the peripherals in the kernel that are currently unsupported in QEMU with supported ones, thus making the Linux kernel rehostable. After that, various applications can be built upon. We implemented this technique inside a prototype system called ECMO and applied it to 815 firmware images, which consist of 20 kernel versions, 37 device models, and 24 vendors. The result shows that ECMO can successfully transplant peripherals for all the 815 Linux kernels. Among them,710 kernels can be successfully rehosted, i.e., launching a user-space shell (87.1% success rate). The failed cases are mainly because the root file system format (ramfs) is not supported by the kernel. We further build three applications, i.e., kernel crash analysis, rootkit forensic analysis, and kernel fuzzing, based on the rehosted kernels to demonstrate the usage scenarios of ECMO

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arXiv2021

iBatch: Saving Ethereum Fees via Secure and Cost-Effective Batching of Smart-Contract Invocations

This paper presents iBatch, a middleware system running on top of an operational Ethereum network to enable secure batching of smart-contract invocations against an untrusted relay server off-chain. iBatch does so at a low overhead by validating the server's batched invocations in smart contracts without additional states. The iBatch mechanism supports a variety of policies, ranging from conservative to aggressive batching, and can be configured adaptively to the current workloads. iBatch automatically rewrites smart contracts to integrate with legacy applications and support large-scale deployment. For cost evaluation, we develop a platform with fast and cost-accurate transaction replaying, build real transaction benchmarks on popular Ethereum applications, and build a functional prototype of iBatch on Ethereum. The evaluation results show that iBatch saves 14.6%-59.1% Gas cost per invocation with a moderate 2-minute delay and 19.06%-31.52% Ether cost per invocation with a delay of 0.26-1.66 blocks.

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arXiv2021

Automatically Locating ARM Instructions Deviation between Real Devices and CPU Emulators

Emulator is widely used to build dynamic analysis frameworks due to its fine-grained tracing capability, full system monitoring functionality, and scalability of running on different operating systemsand architectures. However, whether the emulator is consistent with real devices is unknown. To understand this problem, we aim to automatically locate inconsistent instructions, which behave differently between emulators and real devices. We target ARM architecture, which provides machine readable specification. Based on the specification, we propose a test case generator by designing and implementing the first symbolic execution engine for ARM architecture specification language (ASL). We generate 2,774,649 representative instruction streams and conduct differential testing with these instruction streams between four ARM real devices in different architecture versions (i.e., ARMv5, ARMv6, ARMv7-a, and ARMv8-a) and the state-of-the-art emulators (i.e., QEMU). We locate 155,642 inconsistent instruction streams, which cover 30% of all instruction encodings and 47.8% of the instructions. We find undefined implementation in ARM manual and implementation bugs of QEMU are the major causes of inconsistencies. Furthermore, we discover four QEMU bugs, which are confirmed and patched by thedevelopers, covering 13 instruction encodings including the most commonly used ones (e.g.,STR,BLX). With the inconsistent instructions, we build three security applications and demonstrate thecapability of these instructions on detecting emulators, anti-emulation, and anti-fuzzing.

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arXiv2021

Research on Third-Party Libraries in AndroidApps: A Taxonomy and Systematic LiteratureReview

Third-party libraries (TPLs) have been widely used in mobile apps, which play an essential part in the entire Android ecosystem. However, TPL is a double-edged sword. On the one hand, it can ease the development of mobile apps. On the other hand, it also brings security risks such as privacy leaks or increased attack surfaces (e.g., by introducing over-privileged permissions) to mobile apps. Although there are already many studies for characterizing third-party libraries, including automated detection, security and privacy analysis of TPLs, TPL attributes analysis, etc., what strikes us odd is that there is no systematic study to summarize those studies' endeavors. To this end, we conduct the first systematic literature review on Android TPL-related research. Following a well-defined systematic literature review protocol, we collected 74 primary research papers closely related to the Android third-party library from 2012 to 2020. After carefully examining these studies, we designed a taxonomy of TPL-related research studies and conducted a systematic study to summarize current solutions, limitations, challenges and possible implications of new research directions related to third-party library analysis. We hope that these contributions can give readers a clear overview of existing TPL-related studies and inspire them to go beyond the current status quo by advancing the discipline with innovative approaches.

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arXiv2021

A Systematic Assessment on Android Third-party Library Detection Tools

Third-party libraries (TPLs) have become a significant part of the Android ecosystem. Developers can employ various TPLs to facilitate their app development. Unfortunately, the popularity of TPLs also brings new security issues. For example, TPLs may carry malicious or vulnerable code, which can infect popular apps to pose threats to mobile users. Furthermore, TPL detection is essential for downstream tasks, such as vulnerabilities and malware detection. Thus, various tools have been developed to identify TPLs. However, no existing work has studied these TPL detection tools in detail, and different tools focus on different applications and techniques with performance differences. A comprehensive understanding of these tools will help us make better use of them. To this end, we conduct a comprehensive empirical study to fill the gap by evaluating and comparing all publicly available TPL detection tools based on six criteria: accuracy of TPL construction, effectiveness, efficiency, accuracy of version identification, resiliency to code obfuscation, and ease of use. Besides, we enhance these open-source tools by fixing their limitations, to improve their detection ability. Finally, we build an extensible framework that integrates all existing available TPL detection tools, providing an online service for the research community. We release the evaluation dataset and enhanced tools. According to our study, we also present the essential findings and discuss promising implications to the community. We believe our work provides a clear picture of existing TPL detection techniques and also gives a roadmap for future research.

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arXiv2021

A Measurement Study on the (In)security of End-of-Life (EoL) Embedded Devices

Embedded devices are becoming popular. Meanwhile, researchers are actively working on improving the security of embedded devices. However, previous work ignores the insecurity caused by a special category of devices, i.e., the End-of-Life (EoL in short) devices. Once a product becomes End-of-Life, vendors tend to no longer maintain its firmware or software, including providing bug fixes and security patches. This makes EoL devices susceptible to attacks. For instance, a report showed that an EoL model with thousands of active devices was exploited to redirect web traffic for malicious purposes. In this paper, we conduct the first measurement study to shed light on the (in)security of EoL devices. To this end, our study performs two types of analysis, including the aliveness analysis and the vulnerability analysis. The first one aims to detect the scale of EoL devices that are still alive. The second one is to evaluate the vulnerabilities existing in (active) EoL devices. We have applied our approach to a large number of EoL models from three vendors (i.e., D-Link, Tp-Link, and Netgear) and detect the alive devices in a time period of ten months. Our study reveals some worrisome facts that were unknown by the community. For instance, there exist more than 2 million active EoL devices. Nearly 300,000 of them are still alive even after five years since they became EoL. Although vendors may release security patches after the EoL date, however, the process is ad hoc and incomplete. As a result, more than 1 million active EoL devices are vulnerable, and nearly half of them are threatened by high-risk vulnerabilities. Attackers can achieve a minimum of 2.79 Tbps DDoS attack by compromising a large number of active EoL devices. We believe these facts pose a clear call for more attention to deal with the security issues of EoL devices.

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arXiv2021

Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware

As the COVID-19 pandemic emerged in early 2020, a number of malicious actors have started capitalizing the topic. Although a few media reports mentioned the existence of coronavirus-themed mobile malware, the research community lacks the understanding of the landscape of the coronavirus-themed mobile malware. In this paper, we present the first systematic study of coronavirus-themed Android malware. We first make efforts to create a daily growing COVID-19 themed mobile app dataset, which contains 4,322 COVID-19 themed apk samples (2,500 unique apps) and 611 potential malware samples (370 unique malicious apps) by the time of mid-November, 2020. We then present an analysis of them from multiple perspectives including trends and statistics, installation methods, malicious behaviors and malicious actors behind them. We observe that the COVID-19 themed apps as well as malicious ones began to flourish almost as soon as the pandemic broke out worldwide. Most malicious apps are camouflaged as benign apps using the same app identifiers (e.g., app name, package name and app icon). Their main purposes are either stealing users' private information or making profit by using tricks like phishing and extortion. Furthermore, only a quarter of the COVID-19 malware creators are habitual developers who have been active for a long time, while 75% of them are newcomers in this pandemic. The malicious developers are mainly located in US, mostly targeting countries including English-speaking countries, China, Arabic countries and Europe. To facilitate future research, we have publicly released all the well-labelled COVID-19 themed apps (and malware) to the research community. Till now, over 30 research institutes around the world have requested our dataset for COVID-19 themed research.

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arXiv2021

Ethereum Name Service: the Good, the Bad, and the Ugly

DNS has always been criticized for its inherent design flaws, making the system vulnerable to kinds of attacks. Besides, DNS domain names are not fully controlled by the users, which can be easily taken down by the authorities and registrars. Since blockchain has its unique properties like immutability and decentralization, it seems to be promising to build a decentralized name service on blockchain. Ethereum Name Service (ENS), as a novel name service built atop Etheruem, has received great attention from the community. Yet, no existing work has systematically studied this emerging system, especially the security issues and misbehaviors in ENS. To fill the void, we present the first large-scale study of ENS by collecting and analyzing millions of event logs related to ENS. We characterize the ENS system from a number of perspectives. Our findings suggest that ENS is showing gradually popularity during its four years' evolution, mainly due to its distributed and open nature that ENS domain names can be set to any kinds of records, even censored and malicious contents. We have identified several security issues and misbehaviors including traditional DNS security issues and new issues introduced by ENS smart contracts. Attackers are abusing the system with thousands of squatting ENS names, a number of scam blockchain addresses and malicious websites, etc. Our exploration suggests that our community should invest more effort into the detection and mitigation of issues in Blockchain-based Name Services towards building an open and trustworthy name service.

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arXiv2021

Smart Contract Security: a Practitioners' Perspective

Smart contracts have been plagued by security incidents, which resulted in substantial financial losses. Given numerous research efforts in addressing the security issues of smart contracts, we wondered how software practitioners build security into smart contracts in practice. We performed a mixture of qualitative and quantitative studies with 13 interviewees and 156 survey respondents from 35 countries across six continents to understand practitioners' perceptions and practices on smart contract security. Our study uncovers practitioners' motivations and deterrents of smart contract security, as well as how security efforts and strategies fit into the development lifecycle. We also find that blockchain platforms have a statistically significant impact on practitioners' security perceptions and practices of smart contract development. Based on our findings, we highlight future research directions and provide recommendations for practitioners.

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arXiv2021

DEFECTCHECKER: Automated Smart Contract Defect Detection by Analyzing EVM Bytecode

Smart contracts are Turing-complete programs running on the blockchain. They are immutable and cannot be modified, even when bugs are detected. Therefore, ensuring smart contracts are bug-free and well-designed before deploying them to the blockchain is extremely important. A contract defect is an error, flaw or fault in a smart contract that causes it to produce an incorrect or unexpected result, or to behave in unintended ways. Detecting and removing contract defects can avoid potential bugs and make programs more robust. Our previous work defined 20 contract defects for smart contracts and divided them into five impact levels. According to our classification, contract defects with seriousness level between 1-3 can lead to unwanted behaviors, e.g., a contract being controlled by attackers. In this paper, we propose DefectChecker, a symbolic execution-based approach and tool to detect eight contract defects that can cause unwanted behaviors of smart contracts on the Ethereum blockchain platform. DefectChecker can detect contract defects from smart contracts bytecode. We compare DefectChecker with key previous works, including Oyente, Mythril and Securify by using an open-source dataset. Our experimental results show that DefectChecker performs much better than these tools in terms of both speed and accuracy. We also applied DefectChecker to 165,621 distinct smart contracts on the Ethereum platform. We found that 25,815 of these smart contracts contain at least one of the contract defects that belongs to impact level 1-3, including some real-world attacks.

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