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Empowering the next generation of Android Application Security Researchers

Posted by Jon Bottarini, Security Program Manager & Lena Katib, Strategic Partnerships Manager

The external security researcher community plays an integral role in making the Google Play ecosystem safe and secure. Through this partnership with the community, Google has been able to collaborate with third-party developers to fix thousands of security issues in Android applications before they are exploited and reward security researchers for their hard work and dedication.

In order to empower the next generation of Android security researchers, Google has collaborated with industry partners including HackerOne and PayPal to host a number of Android App Hacking Workshops. These workshops are an effort designed to educate security researchers and cybersecurity students of all skill levels on how to find Android application vulnerabilities through a series of hands-on working sessions, both in-person and virtual.

Through these workshops, we’ve seen attendees from groups such as Merritt College’s cybersecurity program and alumni of Hack the Hood go on to report real-world security vulnerabilities to the Google Play Security Rewards program. This reward program is designed to identify and mitigate vulnerabilities in apps on Google Play, and keep Android users, developers and the Google Play ecosystem safe.

Today, we are releasing our slide deck and workshop materials, including source code for a custom-built Android application that allows you to test your Android application security skills in a variety of capture the flag style challenges.

These materials cover a wide range of techniques for finding vulnerabilities in Android applications. Whether you’re just getting started or have already found many bugs – chances are you’ll learn something new from these challenges! If you get stuck and need a hint on solving a challenge, the solutions for each are available in the Android App Hacking Workshop here.

As you work through the challenges and learn more about the techniques and tips described in our workshop materials, we’d love to hear your feedback.

Additional Resources:

  • If you want to learn more about how to prepare, launch, and run a Vulnerability Disclosure Program (VDP) or discover how to work with external security researchers, check out our VDP course here.
  • If you’re a developer looking to build more secure applications, check out Android app security best practices here.
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Google squashes Android zero‑day bug exploited in targeted attacks

Beyond the vulnerability in the Android kernel, the monthly round of security patches plugs another 38 security loopholes

The post Google squashes Android zero‑day bug exploited in targeted attacks appeared first on WeLiveSecurity

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Pixel 6: Setting a new standard for mobile security

Posted by Dave Kleidermacher, Jesse Seed, Brandon Barbello, and Stephan Somogyi, Android, Pixel & Tensor security teams

With Pixel 6 and Pixel 6 Pro, we’re launching our most secure Pixel phone yet, with 5 years of security updates and the most layers of hardware security. These new Pixel smartphones take a layered security approach, with innovations spanning across the Google Tensor system on a chip (SoC) hardware to new Pixel-first features in the Android operating system, making it the first Pixel phone with Google security from the silicon all the way to the data center. Multiple dedicated security teams have also worked to ensure that Pixel’s security is provable through transparency and external validation.

Secure to the Core

Google has put user data protection and transparency at the forefront of hardware security with Google Tensor. Google Tensor’s main processors are Arm-based and utilize TrustZone™ technology. TrustZone is a key part of our security architecture for general secure processing, but the security improvements included in Google Tensor go beyond TrustZone.

Figure 1. Pixel Secure Environments

The Google Tensor security core is a custom designed security subsystem dedicated to the preservation of user privacy. It’s distinct from the application processor, not only logically, but physically, and consists of a dedicated CPU, ROM, one-time-programmable (OTP) memory, crypto engine, internal SRAM, and protected DRAM. For Pixel 6 and 6 Pro, the security core’s primary use cases include protecting user data keys at runtime, hardening secure boot, and interfacing with Titan M2TM.

Your secure hardware is only as good as your secure OS, and we are using Trusty, our open source trusted execution environment. Trusty OS is the secure OS used both in TrustZone and the Google Tensor security core.

With Pixel 6 and Pixel 6 Pro your security is enhanced by the new Titan M2TM, our discrete security chip, fully designed and developed by Google. In this next generation chip, we moved to an in-house designed RISC-V processor, with extra speed and memory, and made it even more resilient to advanced attacks. Titan M2TM has been tested against the most rigorous standard for vulnerability assessment, AVA_VAN.5, by an independent, accredited evaluation lab. Titan M2™ supports Android Strongbox, which securely generates and stores keys used to protect your PINs and password, and works hand-in-hand with Google Tensor security core to protect user data keys while in use in the SoC.

Moving a step higher in the system, Pixel 6 and Pixel 6 Pro ship with Android 12 and a slew of Pixel-first and Pixel-exclusive features.

Enhanced Controls

We aim to give users better ways to control their data and manage their devices with every release of Android. Starting with Android 12 on Pixel, you can use the new Security hub to manage all your security settings in one place. It helps protect your phone, apps, Google Account, and passwords by giving you a central view of your device’s current configuration. Security hub also provides recommendations to improve your security, helping you decide what settings best meet your needs.

For privacy, we are launching Privacy Dashboard, which will give you a simple and clear timeline view of the apps that have accessed your location, microphone and camera in the last 24 hours. If you notice apps that are accessing more data than you expected, the dashboard provides a path to controls to change those permissions on the fly.

To provide additional transparency, new indicators in Pixel’s status bar will show you when your camera and mic are being accessed by apps. If you want to disable that access, new privacy toggles give you the ability to turn off camera or microphone access across apps on your phone with a single tap, at any time.

The Pixel 6 and Pixel 6 Pro also include a toggle that lets you remove your device’s ability to connect to less-secure 2G networks. While necessary in certain situations, accessing 2G networks can open up additional attack vectors; this toggle helps users mitigate those risks when 2G connectivity isn’t needed.

Built-in security

By making all of our products secure by default, Google keeps more people safe online than anyone else in the world. With the Pixel 6 and Pixel 6 Pro, we’re also ratcheting up the dial on default, built-in protections.

Our new optical under-display fingerprint sensor ensures that your biometric information is secure and never leaves your device. As part of our ongoing security development lifecycle, Pixel 6 and 6 Pro’s fingerprint unlock has been externally validated by security experts as a strong and secure biometric unlock mechanism meeting the Class 3 strength requirements defined in the Android 12 Compatibility Definition Document (CDD).

Phishing continues to be a huge attack vector, affecting everyone across different devices.

The Pixel 6 and Pixel 6 Pro introduce new anti-phishing protections. Built-in protections automatically scan for potential threats from phone calls, text messages, emails, and links sent through apps, notifying you if there’s a potential problem.

Users are also now better protected against bad apps by enhancements to our on-device detection capabilities within Google Play Protect. Since its launch in 2017, Google Play Protect has provided the ability to detect malicious applications even when the device is offline. The Pixel 6 and Pixel 6 Pro uses new machine learning models that improve the detection of malware in Google Play Protect. The detection runs on your Pixel, and uses a privacy preserving technology called federated analytics to discover commonly-run bad apps. This will help to further protect over 3 billion users by improving Google Play Protect, which already analyzes over 100 billion apps every day to detect threats.

Many of Pixel’s privacy-preserving features run inside Private Compute Core, an open source sandbox isolated from the rest of the operating system and apps. Our open source Private Compute Services manages network communication for these features, and uses federated learning, federated analytics, and private information retrieval to improve features while preserving privacy. Some features already running on Private Compute Core include Live Caption, Now Playing, and Smart Reply suggestions.

Google Binary Transparency (GBT) is the newest addition to our open and verifiable security infrastructure, providing a new layer of software integrity for your device. Building on the principles pioneered by Certificate Transparency, GBT helps ensure your Pixel is only running verified OS software. It works by using append-only logs to store signed hashes of the system images. The logs are public and can be used to verify that what’s published is the same as what’s on the device – giving users and researchers the ability to independently verify OS integrity for the first time.

Beyond the Phone

Defense-in-depth isn’t just a matter of hardware and software layers. Security is a rigorous process. Pixel 6 and Pixel 6 Pro benefit from in-depth design and architecture reviews, memory-safe rewrites to security critical code, static analysis, formal verification of source code, fuzzing of critical components, and red-teaming, including with external security labs to pen-test our devices. Pixel is also part of the Android Vulnerability Rewards Program, which paid out $1.75 million last year, creating a valuable feedback loop between us and the security research community and, most importantly, helping us keep our users safe.

Capping off this combined hardware and software security system, is the Titan Backup Architecture, which gives your Pixel a secure foot in the cloud. Launched in 2018, the combination of Android’s Backup Service and Google Cloud’s Titan Technology means that backed-up application data can only be decrypted by a randomly generated key that isn’t known to anyone besides the client, including Google. This end-to-end service was independently audited by a third party security lab to ensure no one can access a user’s backed-up application data without specifically knowing their passcode.

To top it all off, this end-to-end security from the hardware across the software to the data center comes with no fewer than 5 years of guaranteed Android security updates on Pixel 6 and Pixel 6 Pro devices from the date they launch in the US. This is an important commitment for the industry, and we hope that other smartphone manufacturers broaden this trend.

Together, our secure chipset, software and processes make Pixel 6 and Pixel 6 Pro the most secure Pixel phone yet.

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Introducing Android’s Private Compute Services

Posted by Suzanne Frey, VP, Product, Android & Play Security and Privacy

We introduced Android’s Private Compute Core in Android 12 Beta. Today, we’re excited to announce a new suite of services that provide a privacy-preserving bridge between Private Compute Core and the cloud.

Recap: What is Private Compute Core?

Android’s Private Compute Core is an open source, secure environment that is isolated from the rest of the operating system and apps. With each new Android release we’ll add more privacy-preserving features to the Private Compute Core. Today, these include:

  • Live Caption, which adds captions to any media using Google’s on-device speech recognition
  • Now Playing, which recognizes music playing nearby and displays the song title and artist name on your device’s lock screen
  • Smart Reply, which suggests relevant responses based on the conversation you’re having in messaging apps

For these features to be private, they must:

  1. Keep the information on your device private. Android ensures that the sensitive data processed in the Private Compute Core is not shared to any apps without you taking an action. For instance, until you tap a Smart Reply, the OS keeps your reply hidden from both your keyboard and the app you’re typing into.
  2. Let your device use the cloud (to download new song catalogs or speech-recognition models) without compromising your privacy. This is where Private Compute Services comes in.

Introducing Android’s Private Compute Services

Machine learning features often improve by updating models, and Private Compute Services helps features get these updates over a private path. Android prevents any feature inside the Private Compute Core from having direct access to the network. Instead, features communicate over a small set of purposeful open-source APIs to Private Compute Services, which strips out identifying information and uses a set of privacy technologies, including Federated Learning, Federated Analytics, and Private information retrieval.

We will publicly publish the source code for Private Compute Services, so it can be audited by security researchers and other teams outside of Google. This means it can go through the same rigorous security programs that ensure the safety of the Android platform.

We’re enthusiastic about the potential for machine learning to power more helpful features inside Android, and Android’s Private Compute Core will help users benefit from these features while strengthening privacy protections via the new Private Compute Services. Android is the first open source mobile OS to include this kind of externally verifiable privacy; Private Compute Services helps the Android OS continue to innovate in machine learning, while also maintaining the highest standards of privacy and security.

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Rust/C++ interop in the Android Platform

Posted by Joel Galenson and Matthew Maurer, Android Team

One of the main challenges of evaluating Rust for use within the Android platform was ensuring we could provide sufficient interoperability with our existing codebase. If Rust is to meet its goals of improving security, stability, and quality Android-wide, we need to be able to use Rust anywhere in the codebase that native code is required. To accomplish this, we need to provide the majority of functionality platform developers use. As we discussed previously, we have too much C++ to consider ignoring it, rewriting all of it is infeasible, and rewriting older code would likely be counterproductive as the bugs in that code have largely been fixed. This means interoperability is the most practical way forward.

Before introducing Rust into the Android Open Source Project (AOSP), we needed to demonstrate that Rust interoperability with C and C++ is sufficient for practical, convenient, and safe use within Android. Adding a new language has costs; we needed to demonstrate that Rust would be able to scale across the codebase and meet its potential in order to justify those costs. This post will cover the analysis we did more than a year ago while we evaluated Rust for use in Android. We also present a follow-up analysis with some insights into how the original analysis has held up as Android projects have adopted Rust.

Language interoperability in Android

Existing language interoperability in Android focuses on well defined foreign-function interface (FFI) boundaries, which is where code written in one programming language calls into code written in a different language. Rust support will likewise focus on the FFI boundary as this is consistent with how AOSP projects are developed, how code is shared, and how dependencies are managed. For Rust interoperability with C, the C application binary interface (ABI) is already sufficient.

Interoperability with C++ is more challenging and is the focus of this post. While both Rust and C++ support using the C ABI, it is not sufficient for idiomatic usage of either language. Simply enumerating the features of each language results in an unsurprising conclusion: many concepts are not easily translatable, nor do we necessarily want them to be. After all, we’re introducing Rust because many features and characteristics of C++ make it difficult to write safe and correct code. Therefore, our goal is not to consider all language features, but rather to analyze how Android uses C++ and ensure that interop is convenient for the vast majority of our use cases.

We analyzed code and interfaces in the Android platform specifically, not codebases in general. While this means our specific conclusions may not be accurate for other codebases, we hope the methodology can help others to make a more informed decision about introducing Rust into their large codebase. Our colleagues on the Chrome browser team have done a similar analysis, which you can find here.

This analysis was not originally intended to be published outside of Google: our goal was to make a data-driven decision on whether or not Rust was a good choice for systems development in Android. While the analysis is intended to be accurate and actionable, it was never intended to be comprehensive, and we’ve pointed out a couple of areas where it could be more complete. However, we also note that initial investigations into these areas showed that they would not significantly impact the results, which is why we decided to not invest the additional effort.

Methodology

Exported functions from Rust and C++ libraries are where we consider interop to be essential. Our goals are simple:

  • Rust must be able to call functions from C++ libraries and vice versa.
  • FFI should require a minimum of boilerplate.
  • FFI should not require deep expertise.

While making Rust functions callable from C++ is a goal, this analysis focuses on making C++ functions available to Rust so that new Rust code can be added while taking advantage of existing implementations in C++. To that end, we look at exported C++ functions and consider existing and planned compatibility with Rust via the C ABI and compatibility libraries. Types are extracted by running objdump on shared libraries to find external C++ functions they use1 and running c++filt to parse the C++ types. This gives functions and their arguments. It does not consider return values, but a preliminary analysis2 of those revealed that they would not significantly affect the results.

We then classify each of these types into one of the following buckets:

Supported by bindgen

These are generally simple types involving primitives (including pointers and references to them). For these types, Rust’s existing FFI will handle them correctly, and Android’s build system will auto-generate the bindings.

Supported by cxx compat crate

These are handled by the cxx crate. This currently includes std::string, std::vector, and C++ methods (including pointers/references to these types). Users simply have to define the types and functions they want to share across languages and cxx will generate the code to do that safely.

Native support

These types are not directly supported, but the interfaces that use them have been manually reworked to add Rust support. Specifically, this includes types used by AIDL and protobufs.

We have also implemented a native interface for StatsD as the existing C++ interface relies on method overloading, which is not well supported by bindgen and cxx3. Usage of this system does not show up in the analysis because the C++ API does not use any unique types.

Potential addition to cxx

This is currently common data structures such as std::optional and std::chrono::duration and custom string and vector implementations.

These can either be supported natively by a future contribution to cxx, or by using its ExternType facilities. We have only included types in this category that we believe are relatively straightforward to implement and have a reasonable chance of being accepted into the cxx project.

We don’t need/intend to support

Some types are exposed in today’s C++ APIs that are either an implicit part of the API, not an API we expect to want to use from Rust, or are language specific. Examples of types we do not intend to support include:

  • Mutexes – we expect that locking will take place in one language or the other, rather than needing to pass mutexes between languages, as per our coarse-grained philosophy.
  • native_handle – this is a JNI interface type, so it is inappropriate for use in Rust/C++ communication.
  • std::locale& – Android uses a separate locale system from C++ locales. This type primarily appears in output due to e.g., cout usage, which would be inappropriate to use in Rust.

Overall, this category represents types that we do not believe a Rust developer should be using.

HIDL

Android is in the process of deprecating HIDL and migrating to AIDL for HALs for new services.We’re also migrating some existing implementations to stable AIDL. Our current plan is to not support HIDL, preferring to migrate to stable AIDL instead. These types thus currently fall into the “We don’t need/intend to support” bucket above, but we break them out to be more specific. If there is sufficient demand for HIDL support, we may revisit this decision later.

Other

This contains all types that do not fit into any of the above buckets. It is currently mostly std::string being passed by value, which is not supported by cxx.

Top C++ libraries

One of the primary reasons for supporting interop is to allow reuse of existing code. With this in mind, we determined the most commonly used C++ libraries in Android: liblog, libbase, libutils, libcutils, libhidlbase, libbinder, libhardware, libz, libcrypto, and libui. We then analyzed all of the external C++ functions used by these libraries and their arguments to determine how well they would interoperate with Rust.

Overall, 81% of types are in the first three categories (which we currently fully support) and 87% are in the first four categories (which includes those we believe we can easily support). Almost all of the remaining types are those we believe we do not need to support.

Mainline modules

In addition to analyzing popular C++ libraries, we also examined Mainline modules. Supporting this context is critical as Android is migrating some of its core functionality to Mainline, including much of the native code we hope to augment with Rust. Additionally, their modularity presents an opportunity for interop support.

We analyzed 64 binaries and libraries in 21 modules. For each analyzed library we examined their used C++ functions and analyzed the types of their arguments to determine how well they would interoperate with Rust in the same way we did above for the top 10 libraries.

Here 88% of types are in the first three categories and 90% in the first four, with almost all of the remaining being types we do not need to handle.

Analysis of Rust/C++ Interop in AOSP

With almost a year of Rust development in AOSP behind us, and more than a hundred thousand lines of code written in Rust, we can now examine how our original analysis has held up based on how C/C++ code is currently called from Rust in AOSP.4

The results largely match what we expected from our analysis with bindgen handling the majority of interop needs. Extensive use of AIDL by the new Keystore2 service results in the primary difference between our original analysis and actual Rust usage in the “Native Support” category.

A few current examples of interop are:

  • Cxx in Bluetooth – While Rust is intended to be the primary language for Bluetooth, migrating from the existing C/C++ implementation will happen in stages. Using cxx allows the Bluetooth team to more easily serve legacy protocols like HIDL until they are phased out by using the existing C++ support to incrementally migrate their service.
  • AIDL in keystore – Keystore implements AIDL services and interacts with apps and other services over AIDL. Providing this functionality would be difficult to support with tools like cxx or bindgen, but the native AIDL support is simple and ergonomic to use.
  • Manually-written wrappers in profcollectd – While our goal is to provide seamless interop for most use cases, we also want to demonstrate that, even when auto-generated interop solutions are not an option, manually creating them can be simple and straightforward. Profcollectd is a small daemon that only exists on non-production engineering builds. Instead of using cxx it uses some small manually-written C wrappers around C++ libraries that it then passes to bindgen.

Conclusion

Bindgen and cxx provide the vast majority of Rust/C++ interoperability needed by Android. For some of the exceptions, such as AIDL, the native version provides convenient interop between Rust and other languages. Manually written wrappers can be used to handle the few remaining types and functions not supported by other options as well as to create ergonomic Rust APIs. Overall, we believe interoperability between Rust and C++ is already largely sufficient for convenient use of Rust within Android.

If you are considering how Rust could integrate into your C++ project, we recommend doing a similar analysis of your codebase. When addressing interop gaps, we recommend that you consider upstreaming support to existing compat libraries like cxx.

Acknowledgements

Our first attempt at quantifying Rust/C++ interop involved analyzing the potential mismatches between the languages. This led to a lot of interesting information, but was difficult to draw actionable conclusions from. Rather than enumerating all the potential places where interop could occur, Stephen Hines suggested that we instead consider how code is currently shared between C/C++ projects as a reasonable proxy for where we’ll also likely want interop for Rust. This provided us with actionable information that was straightforward to prioritize and implement. Looking back, the data from our real-world Rust usage has reinforced that the initial methodology was sound. Thanks Stephen!

Also, thanks to:

  • Andrei Homescu and Stephen Crane for contributing AIDL support to AOSP.
  • Ivan Lozano for contributing protobuf support to AOSP.
  • David Tolnay for publishing cxx and accepting our contributions.
  • The many authors and contributors to bindgen.
  • Jeff Vander Stoep and Adrian Taylor for contributions to this post.

  1. We used undefined symbols of function type as reported by objdump to perform this analysis. This means that any header-only functions will be absent from our analysis, and internal (non-API) functions which are called by header-only functions may appear in it. ↩

  2. We extracted return values by parsing DWARF symbols, which give the return types of functions. ↩

  3. Even without automated binding generation, manually implementing the bindings is straightforward. ↩

  4. In the case of handwritten C/C++ wrappers, we analyzed the functions they call, not the wrappers themselves. For all uses of our native AIDL library, we analyzed the types used in the C++ version of the library. ↩

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Introducing Security By Design

Posted by Jon Markoff and Sean Smith, Android Security and Privacy Team

Integrating security into your app development lifecycle can save a lot of time, money, and risk. That’s why we’ve launched Security by Design on Google Play Academy to help developers identify, mitigate, and proactively protect against security threats.

The Android ecosystem, including Google Play, has many built-in security features that help protect developers and users. The course Introduction to app security best practices takes these protections one step further by helping you take advantage of additional security features to build into your app. For example, Jetpack Security helps developers properly encrypt their data at rest and provides only safe and well known algorithms for encrypting Files and SharedPreferences. The SafetyNet Attestation API is a solution to help identify potentially dangerous patterns in usage. There are several common design vulnerabilities that are important to look out for, including using shared or improper file storage, using insecure protocols, unprotected components such as Activities, and more. The course also provides methods to test your app in order to help you keep it safe after launch. Finally, you can set up a Vulnerability Disclosure Program (VDP) to engage security researchers to help.

In the next course, you can learn how to integrate security at every stage of the development process by adopting the Security Development Lifecycle (SDL). The SDL is an industry standard process and in this course you’ll learn the fundamentals of setting up a program, getting executive sponsorship and integration into your development lifecycle.

Threat modeling is part of the Security Development Lifecycle, and in this course you will learn to think like an attacker to identify, categorize, and address threats. By doing so early in the design phase of development, you can identify potential threats and start planning for how to mitigate them at a much lower cost and create a more secure product for your users.

Improving your app’s security is a never ending process. Sign up for the Security by Design module where in a few short courses, you will learn how to integrate security into your app development lifecycle, model potential threats, and app security best practices into your app, as well as avoid potential design pitfalls.

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Introducing Security By Design

Posted by Jon Markoff and Sean Smith, Android Security and Privacy Team

Integrating security into your app development lifecycle can save a lot of time, money, and risk. That’s why we’ve launched Security by Design on Google Play Academy to help developers identify, mitigate, and proactively protect against security threats.

The Android ecosystem, including Google Play, has many built-in security features that help protect developers and users. The course Introduction to app security best practices takes these protections one step further by helping you take advantage of additional security features to build into your app. For example, Jetpack Security helps developers properly encrypt their data at rest and provides only safe and well known algorithms for encrypting Files and SharedPreferences. The SafetyNet Attestation API is a solution to help identify potentially dangerous patterns in usage. There are several common design vulnerabilities that are important to look out for, including using shared or improper file storage, using insecure protocols, unprotected components such as Activities, and more. The course also provides methods to test your app in order to help you keep it safe after launch. Finally, you can set up a Vulnerability Disclosure Program (VDP) to engage security researchers to help.

In the next course, you can learn how to integrate security at every stage of the development process by adopting the Security Development Lifecycle (SDL). The SDL is an industry standard process and in this course you’ll learn the fundamentals of setting up a program, getting executive sponsorship and integration into your development lifecycle.

Threat modeling is part of the Security Development Lifecycle, and in this course you will learn to think like an attacker to identify, categorize, and address threats. By doing so early in the design phase of development, you can identify potential threats and start planning for how to mitigate them at a much lower cost and create a more secure product for your users.

Improving your app’s security is a never ending process. Sign up for the Security by Design module where in a few short courses, you will learn how to integrate security into your app development lifecycle, model potential threats, and app security best practices into your app, as well as avoid potential design pitfalls.

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Integrating Rust Into the Android Open Source Project

Posted by Ivan Lozano, Android Team

The Android team has been working on introducing the Rust programming language into the Android Open Source Project (AOSP) since 2019 as a memory-safe alternative for platform native code development. As with any large project, introducing a new language requires careful consideration. For Android, one important area was assessing how to best fit Rust into Android’s build system. Currently this means the Soong build system (where the Rust support resides), but these design decisions and considerations are equally applicable for Bazel when AOSP migrates to that build system. This post discusses some of the key design considerations and resulting decisions we made in integrating Rust support into Android’s build system.

Rust integration into large projects

A RustConf 2019 meeting on Rust usage within large organizations highlighted several challenges, such as the risk that eschewing Cargo in favor of using the Rust Compiler, rustc, directly (see next section) may remove organizations from the wider Rust community. We share this same concern. When changes to imported third-party crates might be beneficial to the wider community, our goal is to upstream those changes. Likewise when crates developed for Android could benefit the wider Rust community, we hope to release them as independent crates. We believe that the success of Rust within Android is dependent on minimizing any divergence between Android and the Rust community at large, and hope that the Rust community will benefit from Android’s involvement.

No nested build systems

Rust provides Cargo as the default build system and package manager, collecting dependencies and invoking rustc (the Rust compiler) to build the target crate (Rust package). Soong takes this role instead in Android and calls rustc directly for several reasons:

  • In Cargo, C dependencies are handled independently in an ad-hoc manner via build.rs scripts. Soong already provides a mechanism for building C libraries and defining them as dependencies, and Android carefully controls the compiler version and global compilation flags to ensure libraries are built a particular way. Relying on Cargo would introduce a second non-Soong mechanism for defining/building C libraries that would not be constrained by the carefully selected compilation controls implemented in Soong. This could also lead to multiple different versions of the same library, negatively impacting memory/disk usage.
  • Calling compilers directly through Soong provides the stability and control Android requires for the variety of build configurations it supports (for example, specifying where target-specific dependencies are and which compilation flags to use). While it would technically be possible to achieve the necessary level of control over rustc indirectly through Cargo, Soong would have no understanding of how the Cargo.toml (the Cargo build file) would influence the commands Cargo emits to rustc. Paired with the fact that Cargo evolves independently, this would severely restrict Soong’s ability to precisely control how build artifacts are created.
  • Builds which are self-contained and insensitive to the host configuration, known as hermetic builds, are necessary for Android to produce reproducible builds. Cargo, which relies on build.rs scripts, doesn’t yet provide hermeticity guarantees.
  • Incremental builds are important to maintain engineering productivity; building Android takes a considerable amount of resources. Cargo was not designed for integration into existing build systems and does not expose its compilation units. Each Cargo invocation builds the entire crate dependency graph for a given Cargo.toml, rebuilding crates multiple times across projects1. This is too coarse for integration into Soong’s incremental build support, which expects smaller compilation units. This support is necessary to scale up Rust usage within Android.

    Using the Rust compiler directly allows us to avoid these issues and is consistent with how we compile all other code in AOSP. It provides the most control over the build process and eases integration into Android’s existing build system. Unfortunately, avoiding it introduces several challenges and influences many other build system decisions because Cargo usage is so deeply ingrained in the Rust crate ecosystem.

    No build.rs scripts

    A build.rs script compiles to a Rust binary which Cargo builds and executes during a build to handle pre-build tasks, commonly setting up the build environment, or building libraries in other languages (for example C/C++). This is analogous to configure scripts used for other languages.

    Avoiding build.rs scripts somewhat flows naturally from not relying on Cargo since supporting these would require replicating Cargo behavior and assumptions. Beyond this however, there are good reasons for AOSP to avoid build scripts as well:

    • build.rs scripts can execute arbitrary code on the build host. From a security perspective, this introduces an additional burden when adding or updating third-party code as the build.rs script needs careful scrutiny.
    • Third-party build.rs scripts may not be hermetic or reproducible in potentially subtle ways. It is also common for build.rs files to access files outside the build directory (such as /usr/lib). When they are not hermetic, we would need to either carry a local patch or work with upstream to resolve the issue.
    • The most common task for build.rs is to build C libraries which Rust code depends on. We already support this through Soong.
    • Android likewise avoids running build scripts while building for other languages, instead, simply using them to inform the structure of the Android.bp file.

For instances in third-party code where a build script is used only to compile C dependencies, we either use existing cc_library Soong definitions (such as boringssl for quiche) or create new definitions for crate-specific code.

When the build.rs is used to generate source, we try to replicate the core functionality in a Soong rust_binary module for use as a custom source generator. In other cases where Soong can provide the information without source generation, we may carry a small patch that leverages this information.

Why proc_macro but not build.rs?

Why do we support proc_macros, which are compiler plug-ins that execute code on the host within the compiler context, but not build.rs scripts?

While build.rs code is written as one-off code to handle building a single crate, proc_macros define reusable functionality within the compiler which can become widely relied upon across the Rust community. As a result popular proc_macros are generally better maintained and more scrutinized upstream, which makes the code review process more manageable. They are also more readily sandboxed as part of the build process since they are less likely to have dependencies external to the compiler.

proc_macros are also a language feature rather than a method for building code. These are relied upon by source code, are unavoidable for third-party dependencies, and are useful enough to define and use within our platform code. While we can avoid build.rs by leveraging our build system, the same can’t be said of proc_macros.

There is also precedence for compiler plugin support within the Android build system. For example see Soong’s java_plugin modules.

Generated source as crates

Unlike C/C++ compilers, rustc only accepts a single source file representing an entry point to a binary or library. It expects that the source tree is structured such that all required source files can be automatically discovered. This means that generated source either needs to be placed in the source tree or provided through an include directive in source: 

include!("/path/to/hello.rs");

The Rust community depends on build.rs scripts alongside assumptions about the Cargo build environment to get around this limitation. When building, the cargo command sets an OUT_DIR environment variable which build.rs scripts are expected to place generated source code in. This source can then be included via: 

include!(concat!(env!("OUT_DIR"), "/hello.rs"));

This presents a challenge for Soong as outputs for each module are placed in their own out/ directory2; there is no single OUT_DIR where dependencies output their generated source.

For platform code, we prefer to package generated source into a crate that can be imported. There are a few reasons to favor this approach:

  • Prevent generated source file names from colliding.
  • Reduce boilerplate code checked-in throughout the tree and which needs to be maintained. Any boilerplate necessary to make the generated source compile into a crate can be centrally maintained.
  • Avoid implicit3 interactions between generated code and the surrounding crate.
  • Reduce pressure on memory and disk by dynamically liking commonly used generated sources.

    As a result, all of Android’s Rust source generation module types produce code that can be compiled and used as a crate.

    We still support third-party crates without modification by copying all the generated source dependencies for a module into a single per-module directory similar to Cargo. Soong then sets the OUT_DIR environment variable to that directory when compiling the module so the generated source can be found. However we discourage use of this mechanism in platform code unless absolutely necessary for the reasons described above.

    Dynamic linkage by default

    By default, the Rust ecosystem assumes that crates will be statically linked into binaries. The usual benefits of dynamic libraries are upgrades (whether for security or functionality) and decreased memory usage. Rust’s lack of a stable binary interface and usage of cross-crate information flow prevents upgrading libraries without upgrading all dependent code. Even when the same crate is used by two different programs on the system, it is unlikely to be provided by the same shared object4 due to the precision with which Rust identifies its crates. This makes Rust binaries more portable but also results in larger disk and memory footprints.

    This is problematic for Android devices where resources like memory and disk usage must be carefully managed because statically linking all crates into Rust binaries would result in excessive code duplication (especially in the standard library). However, our situation is also different from the standard host environment: we build Android using global decisions about dependencies. This means that nearly every crate is shareable between all users of that crate. Thus, we opt to link crates dynamically by default for device targets. This reduces the overall memory footprint of Rust in Android by allowing crates to be reused across multiple binaries which depend on them.

    Since this is unusual in the Rust community, not all third-party crates support dynamic compilation. Sometimes we must carry small patches while we work with upstream maintainers to add support.

    Current Status of Build Support

    We support building all output types supported by rustc (rlibs, dylibs, proc_macros, cdylibs, staticlibs, and executables). Rust modules can automatically request the appropriate crate linkage for a given dependency (rlib vs dylib). C and C++ modules can depend on Rust cdylib or staticlib producing modules the same way as they would for a C or C++ library.

    In addition to being able to build Rust code, Android’s build system also provides support for protobuf and gRPC and AIDL generated crates. First-class bindgen support makes interfacing with existing C code simple and we have support modules using cxx for tighter integration with C++ code.

    The Rust community produces great tooling for developers, such as the language server rust-analyzer. We have integrated support for rust-analyzer into the build system so that any IDE which supports it can provide code completion and goto definitions for Android modules.

    Source-based code coverage builds are supported to provide platform developers high level signals on how well their code is covered by tests. Benchmarks are supported as their own module type, leveraging the criterion crate to provide performance metrics. In order to maintain a consistent style and level of code quality, a default set of clippy lints and rustc lints are enabled by default. Additionally, HWASAN/ASAN fuzzers are supported, with the HWASAN rustc support added to upstream.

    In the near future, we plan to add documentation to source.android.com on how to define and use Rust modules in Soong. We expect Android’s support for Rust to continue evolving alongside the Rust ecosystem and hope to continue to participate in discussions around how Rust can be integrated into existing build systems.

    Thank you to Matthew Maurer, Jeff Vander Stoep, Joel Galenson, Manish Goregaokar, and Tyler Mandry for their contributions to this post.

    Notes

    1. This can be mitigated to some extent with workspaces, but requires a very specific directory arrangement that AOSP does not conform to. ↩

    2. This presents no problem for C/C++ and similar languages as the path to the generated source is provided directly to the compiler. ↩

    3. Since include! works by textual inclusion, it may reference values from the enclosing namespace, modify the namespace, or use constructs like #![foo]. These implicit interactions can be difficult to maintain. Macros should be preferred if interaction with the rest of the crate is truly required.  ↩

    4. While libstd would usually be shareable for the same compiler revision, most other libraries would end up with several copies for Cargo-built Rust binaries, since each build would attempt to use a minimum feature set and may select different dependency versions for the library in question. Since information propagates across crate boundaries, you cannot simply produce a “most general” instance of that library. ↩

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Rust in the Android platform

Posted by Jeff Vander Stoep and Stephen Hines, Android Team

Correctness of code in the Android platform is a top priority for the security, stability, and quality of each Android release. Memory safety bugs in C and C++ continue to be the most-difficult-to-address source of incorrectness. We invest a great deal of effort and resources into detecting, fixing, and mitigating this class of bugs, and these efforts are effective in preventing a large number of bugs from making it into Android releases. Yet in spite of these efforts, memory safety bugs continue to be a top contributor of stability issues, and consistently represent ~70% of Android’s high severity security vulnerabilities.

In addition to ongoing and upcoming efforts to improve detection of memory bugs, we are ramping up efforts to prevent them in the first place. Memory-safe languages are the most cost-effective means for preventing memory bugs. In addition to memory-safe languages like Kotlin and Java, we’re excited to announce that the Android Open Source Project (AOSP) now supports the Rust programming language for developing the OS itself.

Systems programming

Managed languages like Java and Kotlin are the best option for Android app development. These languages are designed for ease of use, portability, and safety. The Android Runtime (ART) manages memory on behalf of the developer. The Android OS uses Java extensively, effectively protecting large portions of the Android platform from memory bugs. Unfortunately, for the lower layers of the OS, Java and Kotlin are not an option.

Lower levels of the OS require systems programming languages like C, C++, and Rust. These languages are designed with control and predictability as goals. They provide access to low level system resources and hardware. They are light on resources and have more predictable performance characteristics.

For C and C++, the developer is responsible for managing memory lifetime. Unfortunately, it’s easy to make mistakes when doing this, especially in complex and multithreaded codebases.

Rust provides memory safety guarantees by using a combination of compile-time checks to enforce object lifetime/ownership and runtime checks to ensure that memory accesses are valid. This safety is achieved while providing equivalent performance to C and C++.

The limits of sandboxing

C and C++ languages don’t provide these same safety guarantees and require robust isolation. All Android processes are sandboxed and we follow the Rule of 2 to decide if functionality necessitates additional isolation and deprivileging. The Rule of 2 is simple: given three options, developers may only select two of the following three options.

For Android, this means that if code is written in C/C++ and parses untrustworthy input, it should be contained within a tightly constrained and unprivileged sandbox. While adherence to the Rule of 2 has been effective in reducing the severity and reachability of security vulnerabilities, it does come with limitations. Sandboxing is expensive: the new processes it requires consume additional overhead and introduce latency due to IPC and additional memory usage. Sandboxing doesn’t eliminate vulnerabilities from the code and its efficacy is reduced by high bug density, allowing attackers to chain multiple vulnerabilities together.

Memory-safe languages like Rust help us overcome these limitations in two ways:

  1. Lowers the density of bugs within our code, which increases the effectiveness of our current sandboxing.
  2. Reduces our sandboxing needs, allowing introduction of new features that are both safer and lighter on resources.

But what about all that existing C++?

Of course, introducing a new programming language does nothing to address bugs in our existing C/C++ code. Even if we redirected the efforts of every software engineer on the Android team, rewriting tens of millions of lines of code is simply not feasible.

The above analysis of the age of memory safety bugs in Android (measured from when they were first introduced) demonstrates why our memory-safe language efforts are best focused on new development and not on rewriting mature C/C++ code. Most of our memory bugs occur in new or recently modified code, with about 50% being less than a year old.

The comparative rarity of older memory bugs may come as a surprise to some, but we’ve found that old code is not where we most urgently need improvement. Software bugs are found and fixed over time, so we would expect the number of bugs in code that is being maintained but not actively developed to go down over time. Just as reducing the number and density of bugs improves the effectiveness of sandboxing, it also improves the effectiveness of bug detection.

Limitations of detection

Bug detection via robust testing, sanitization, and fuzzing is crucial for improving the quality and correctness of all software, including software written in Rust. A key limitation for the most effective memory safety detection techniques is that the erroneous state must actually be triggered in instrumented code in order to be detected. Even in code bases with excellent test/fuzz coverage, this results in a lot of bugs going undetected.

Another limitation is that bug detection is scaling faster than bug fixing. In some projects, bugs that are being detected are not always getting fixed. Bug fixing is a long and costly process.

Each of these steps is costly, and missing any one of them can result in the bug going unpatched for some or all users. For complex C/C++ code bases, often there are only a handful of people capable of developing and reviewing the fix, and even with a high amount of effort spent on fixing bugs, sometimes the fixes are incorrect.

Bug detection is most effective when bugs are relatively rare and dangerous bugs can be given the urgency and priority that they merit. Our ability to reap the benefits of improvements in bug detection require that we prioritize preventing the introduction of new bugs.

Prioritizing prevention

Rust modernizes a range of other language aspects, which results in improved correctness of code:

  • Memory safety – enforces memory safety through a combination of compiler and run-time checks.
  • Data concurrency – prevents data races. The ease with which this allows users to write efficient, thread-safe code has given rise to Rust’s Fearless Concurrency slogan.
  • More expressive type system – helps prevent logical programming bugs (e.g. newtype wrappers, enum variants with contents).
  • References and variables are immutable by default – assist the developer in following the security principle of least privilege, marking a reference or variable mutable only when they actually intend it to be so. While C++ has const, it tends to be used infrequently and inconsistently. In comparison, the Rust compiler assists in avoiding stray mutability annotations by offering warnings for mutable values which are never mutated.
  • Better error handling in standard libraries – wrap potentially failing calls in Result, which causes the compiler to require that users check for failures even for functions which do not return a needed value. This protects against bugs like the Rage Against the Cage vulnerability which resulted from an unhandled error. By making it easy to propagate errors via the ? operator and optimizing Result for low overhead, Rust encourages users to write their fallible functions in the same style and receive the same protection.
  • Initialization – requires that all variables be initialized before use. Uninitialized memory vulnerabilities have historically been the root cause of 3-5% of security vulnerabilities on Android. In Android 11, we started auto initializing memory in C/C++ to reduce this problem. However, initializing to zero is not always safe, particularly for things like return values, where this could become a new source of faulty error handling. Rust requires every variable be initialized to a legal member of its type before use, avoiding the issue of unintentionally initializing to an unsafe value. Similar to Clang for C/C++, the Rust compiler is aware of the initialization requirement, and avoids any potential performance overhead of double initialization.
  • Safer integer handling – Overflow sanitization is on for Rust debug builds by default, encouraging programmers to specify a wrapping_add if they truly intend a calculation to overflow or saturating_add if they don’t. We intend to enable overflow sanitization for all builds in Android. Further, all integer type conversions are explicit casts: developers can not accidentally cast during a function call when assigning to a variable or when attempting to do arithmetic with other types.

Where we go from here

Adding a new language to the Android platform is a large undertaking. There are toolchains and dependencies that need to be maintained, test infrastructure and tooling that must be updated, and developers that need to be trained. For the past 18 months we have been adding Rust support to the Android Open Source Project, and we have a few early adopter projects that we will be sharing in the coming months. Scaling this to more of the OS is a multi-year project. Stay tuned, we will be posting more updates on this blog.

Java is a registered trademark of Oracle and/or its affiliates.

Thanks Matthew Maurer, Bram Bonne, and Lars Bergstrom for contributions to this post. Special thanks to our colleagues, Adrian Taylor for his insight into the age of memory vulnerabilities, and to Chris Palmer for his work on “The Rule of 2” and “The limits of Sandboxing”.

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Privacy-Preserving Smart Input with Gboard

Posted by Yang Lu, Software Engineer, Angana Ghosh, Group Product Manager, and Xu Liu, Director of Engineering, Gboard team

Google Keyboard (a.k.a Gboard) has a critical mission to provide frictionless input on Android to empower users to communicate accurately and express themselves effortlessly. In order to accomplish this mission, Gboard must also protect users’ private and sensitive data. Nothing users type is sent to Google servers. We recently launched privacy-preserving input by further advancing the latest federated technologies. In Android 11, Gboard also launched the contextual input suggestion experience by integrating on-device smarts into the user’s daily communication in a privacy-preserving way.

Before Android 11, input suggestions were surfaced to users in several different places. In Android 11, Gboard launched a consistent and coordinated approach to access contextual input suggestions. For the first time, we’ve brought Smart Replies to the keyboard suggestions – powered by system intelligence running entirely on device. The smart input suggestions are rendered with a transparent layer on top of Gboard’s suggestion strip. This structure maintains the trust boundaries between the Android platform and Gboard, meaning sensitive personal content cannot be not accessed by Gboard. The suggestions are only sent to the app after the user taps to accept them.

For instance, when a user receives the message “Have a virtual coffee at 5pm?” in Whatsapp, on-device system intelligence predicts smart text and emoji replies “Sounds great!” and “👍”. Android system intelligence can see the incoming message but Gboard cannot. In Android 11, these Smart Replies are rendered by the Android platform on Gboard’s suggestion strip as a transparent layer. The suggested reply is generated by the system intelligence. When the user taps the suggestion, Android platform sends it to the input field directly. If the user doesn’t tap the suggestion, gBoard and the app cannot see it. In this way, Android and Gboard surface the best of Google smarts whilst keeping users’ data private: none of their data goes to any app, including the keyboard, unless they’ve tapped a suggestion.

Additionally, federated learning has enabled Gboard to train intelligent input models across many devices while keeping everything individual users type on their device. Today, the emoji is as common as punctuation – and have become the way for our users to express themselves in messaging. Our users want a way to have fresh and diversified emojis to better express their thoughts in messaging apps. Recently, we launched new on-device transformer models that are fine-tuned with federated learning in Gboard, to produce more contextual emoji predictions for English, Spanish and Portuguese.

Furthermore, following the success of privacy-preserving machine learning techniques, Gboard continues to leverage federated analytics to understand how Gboard is used from decentralized data. What we’ve learned from privacy-preserving analysis has let us make better decisions in our product.

When a user shares an emoji in a conversation, their phone keeps an ongoing count of which emojis are used. Later, when the phone is idle, plugged in, and connected to WiFi, Google’s federated analytics server invites the device to join a “round” of federated analytics data computation with hundreds of other participating phones. Every device involved in one round will compute the emoji share frequency, encrypt the result and send it a federated analytics server. Although the server can’t decrypt the data individually, the final tally of total emoji counts can be decrypted when combining encrypted data across devices. The aggregated data shows that the most popular emoji is 😂 in Whatsapp, 😭 in Roblox(gaming), and ✔ in Google Docs. Emoji 😷 moved up from 119th to 42nd in terms of frequency during COVID-19.

Gboard always has a strong commitment to Google’s Privacy Principles. Gboard strives to build privacy-preserving effortless input products for users to freely express their thoughts in 900+ languages while safeguarding user data. We will keep pushing the state of the art in smart input technologies on Android while safeguarding user data. Stay tuned!

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