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Data Security on Mobile Devices: Current State of the Art, Open Problems, and Proposed Solutions

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Data Security on Mobile Devices: Current State of the Art, Open Problems, and Proposed Solutions Data Security on Mobile Devices: Current State of the Art, Open Problems, and Proposed Solutions Maximilian Zinkus Tushar M. Jois Johns Hopkins University Johns Hopkins University [email protected] [email protected] Matthew Green Johns Hopkins University [email protected] May 27, 2021 arXiv:2105.12613v1 [cs.CR] 26 May 2021 Executive Summary In this work we present definitive evidence, analysis, and (where needed) speculation to answer the questions, (1) “Which concrete security measures in mobile devices meaningfully prevent unauthorized access to user data?” (2) “In what ways are modern mobile devices accessed by unauthorized parties?” and finally, (3) “How can we improve modern mobile devices to prevent unauthorized access?” We examine the two major platforms in the mobile space, iOS and Android, and for each we provide a thorough investigation of existing and historical security features, evidence- based discussion of known security bypass techniques, and concrete recommendations for remediation. In iOS we find a compelling set of security and privacy controls, empowered by strong encryption, and yet a critical lack in coverage due to under-utilization of these tools leading to serious privacy and security concerns. In Android we find strong protections emerging in the very latest flagship devices, but simultaneously fragmented and inconsistent security and privacy controls, not least due to disconnects between Google and Android phone manufacturers, the deeply lagging rate of Android updates reaching devices, and various software architectural considerations. We also find in both platforms exacerbating factors due to increased synchronization of data with cloud services. The markets for exploits and forensic software tools which target these platforms are alive and well. We aggregate and analyze public records, documentation, articles, and blog postings to categorize and discuss unauthorized bypass of security features by hackers and law enforcement alike. Motivated by an accelerating number of cases since Apple v. FBI in 2016, we analyze the impact of forensic tools, and the privacy risks involved in unchecked seizure and search. Then, we provide in-depth analysis of the data potentially accessed via law enforcement methodologies from both mobile devices and associated cloud services. Our fact-gathering and analysis allow us to make a number of recommendations for improving data security on these devices. In both iOS and Android we propose concrete improvements which mitigate or entirely address many concerns we raise, and provide analysis towards resolving the remainder. The mitigations we propose can be largely summarized as increasing coverage of sensitive data via strong encryption, but we detail various challenges and approaches towards this goal and others. It is our hope that this work stimulates mobile device development and research towards security and privacy, provides a unique reference of information, and acts as an evidence-based argument for the importance of reliable encryption to privacy, which we believe is both a human right and integral to a functioning democracy. Contents 1 Introduction 1 1.1 Summary of Key Findings . .3 1.1.1 Apple iOS . .4 1.1.2 Google Android & Android Phones . .6 2 Technical Background 8 2.1 Data Security Technologies for Mobile Devices . .8 2.2 Threat Model . .9 2.3 Sensitive Data on Phones . 10 3 Apple iOS 12 3.1 Protection of User Data in iOS . 12 3.2 History of Apple Security Features . 23 3.3 Known Data Security Bypass Techniques . 26 3.3.1 Jailbreaking and Software Exploits . 27 3.3.2 Local Device Data Extraction . 35 3.3.3 Cloud Data Extraction . 40 3.3.4 Conclusions from Bypasses . 41 3.4 Forensic Software for iOS . 42 3.5 Proposed Improvements to iOS . 43 4 Android 48 4.1 Protection of User Data in Android . 48 4.2 History of Android Security Features . 60 4.3 Known Data Security Bypass Techniques . 62 4.3.1 Rooting . 64 4.3.2 Alternative Techniques . 67 4.3.3 Local Device Data Extraction . 68 4.3.4 Cloud Data Extraction . 70 4.3.5 Conclusions from Bypasses . 72 4.4 Forensic Software for Android . 72 4.5 Proposed Improvements to Android . 73 2 5 Conclusion 76 A History of iOS Security Features 95 A.1 iOS Security Features Over Time . 95 A.1.1 Encryption and Data Protection over time . 95 A.1.2 Progression of passcodes to biometrics . 95 A.1.3 Introduction of the Secure Enclave . 96 A.1.4 Hardware changes for security . 96 A.1.5 Moving secrets to the cloud . 97 B History of Android Security Features 98 B.1 Android Security Features Over Time . 98 B.1.1 Application sandboxing . 98 B.1.2 Data storage & encryption . 99 B.1.3 Integrity checking . 99 B.1.4 Trusted hardware . 100 C History of Forensic Tools 101 C.1 Forensic Tools Access . 101 C.2 Forensic Tools Listing . 107 3 List of Figures 2.1 List of Targets for NIST Mobile Device Acquisition Forensics . 11 3.1 iPhone Passcode Setup Interface . 13 3.2 Apple Code Signing Process Documentation . 14 3.3 iOS Data Protection Key Hierarchy. Each arrow . 15 3.4 List of iOS Data Protection Classes . 16 3.5 List of Data Included in iCloud Backup . 18 3.6 List of iCloud Data Accessible by Apple . 19 3.7 List of iCloud Data Encrypted “End-to-End” . 19 3.8 Secure Enclave Processor Key Derivation . 21 3.9 LocalAuthentication Interface for TouchID/FaceID . 22 3.12 Apple Documentation on Legal Requests for Data . 27 3.14 Alleged Leaked Images of the GrayKey Passcode Guessing Interface . 31 3.17 List of Data Categories Obtainable via Device Forensic Software . 36 3.18 Cellebrite UFED Touch 2 . 37 3.19 Cellebrite UFED Interface During Extraction of an iPhone . 38 3.20 Records from Arizona Law Enforcement Agencies Documenting Passcode Recovery on iOS . 39 3.21 GrayKey by Grayshift . 40 3.22 List of Data Categories Obtainable via Cloud Forensic Software . 41 4.1 PIN Unlock on Android 11 . 49 4.2 Flow Chart of an Android Keymaster Access Request . 52 4.3 Android Boot Process Using Verified Boot . 53 4.4 Relationship Between Google Play Services and an Android App . 54 4.5 Signature location in an Android APK . 55 4.6 Installing Unknown Apps on Android . 56 4.7 Android Backup Flow Diagram for App Data . 57 4.8 Android Backup Interface . 58 4.10 Google Messages RCS Chat Features . 59 4.9 List of Data Categories Included in Google Account Backup . 59 4.11 Google Messages Web Client Connection Error . 61 4.12 Google Duo Communication Flow Diagram . 61 4.14 Legitimate Bootloader Unlock on Android . 64 4 4.15 Kernel Hierarchy on Android . 65 4.16 Distribution of PHAs for Apps Installed Outside of the Google Play Store . 67 4.18 Extracting Data on a Rooted Android Device Using dd ............ 69 4.17 Autopsy Forensic Analysis for an Android Disk Image . 69 4.19 Cellebrite EDL Instructions for an Encrypted Alcatel Android Device . 70 4.20 Cellebrite UFED Interface During Extraction of an HTC Desire Android Device 71 C.1 Legend for Forensic Access Tables . 101 5 List of Tables 3.10 History of iOS Data Protection . 24 3.11 History of iPhone Hardware Security . 25 3.13 Passcode Brute-Force Time Estimates . 30 3.15 History of Jailbreaks on iPhone . 33 3.16 History of iOS Lock Screen Bypasses . 34 4.13 History of Android (AOSP) Security Features . 63 C.2 iOS and Android Forensic Tool Access (2019) . 102 C.3 iOS and Android Forensic Tool Access (2018) . 103 C.4 iOS and Android Forensic Tool Access (2017) . 104 C.5 iOS Forensic Tool Access (2010–2016) . 105 C.6 Android Forensic Tool Access (2010–2016) . 106 C.7 History of Forensic Tools (2019) . 107 C.8 History of Forensic Tools (2018) . 108 C.9 History of Forensic Tools (2017) . 109 C.10 History of Forensic Tools (2010–2016) . 110 Chapter 1 Introduction Mobile devices have become a ubiquitous component of modern life. More than 45% of the global population uses a smartphone [1], while this number exceeds 80% in the United States [2]. This widespread adoption is a double-edged sword: the smartphone vastly improves the amount of information that individuals can carry with them; at the same time, it has created new potential targets for third parties to obtain sensitive data. The portability and ease of access makes smartphones a target for malicious actors and law enforcement alike: to the former, it provides new opportunities for criminality [3, 4, 5]. To the latter it offers new avenues for investigation, monitoring, and surveillance [6, 7, 8]. Over the past decade, hardware and software manufacturers have acknowledged these concerns, in the process deploying a series of major upgrades to smartphone hardware and operating systems. These include mechanisms designed to improve software security; default use of passcodes and biometric authentication; and the incorporation of strong encryption mechanisms to protect data in motion and at rest. While these improvements have enhanced the ability of smartphones to prevent data theft, they have provoked a backlash from the law enforcement community. This reaction is best exemplified by the FBI’s “Going Dark” initiative [8], which seeks to increase law enforcement’s access to encrypted data via legislative and policy initiatives. These concerns have also motivated law enforcement agencies, in collaboration with industry partners, to invest in developing and acquiring technical means for bypassing smartphone security features. This dynamic broke into the public consciousness during the 2016 “Apple v. FBI” controversy [9, 10, 11, 12], in which Apple contested an FBI demand to bypass technical security measures. However, a vigorous debate over these issues continues to this day [6, 7, 13, 14, 15, 16].
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