The App Defense Alliance launched in 2019 with a mission to protect Android users from bad apps through shared intelligence and coordinated detection between alliance partners. Earlier this year, the App Defense Alliance expanded to include new initiatives outside of malware detection and is now the home for several industry-led collaborations including Malware Mitigation, MASA (Mobile App Security Assessment) & CASA (Cloud App Security Assessment). With a new dedicated landing page at appdefensealliance.dev, the ADA has an expanded mission to protect Android users by removing threats while improving app quality across the ecosystem. Let’s walk through some of the latest program updates from the past year, including the addition of new ADA members.
Malware Mitigation
Together, with the founding ADA members - Google, ESET, Lookout, and Zimperium, the alliance has been able to reduce the risk of app-based malware and better protect Android users. These partners have access to mobile apps as they are being submitted to the Google Play Store and scan thousands of apps daily, acting as another, vital set of eyes prior to an app going live on Play. Knowledge sharing and industry collaboration are important aspects in securing the world from attacks and that’s why we’re continuing to invest in the program.
New ADA Members
We’re excited to see the ADA expand with the additions of McAfee and Trend Micro. Both McAfee and Trend Micro are leaders in the antivirus space and we look forward to their contributions to the program.
Mobile App Security Assessment (MASA)
With consumers spending four to five hours per day in mobile apps, ensuring the safety of these services is more important than ever. According to Data.ai, the pandemic accelerated existing mobile habits - with app categories like finance growing 25% YoY and users spending over 100 billion hours in shopping apps.
That’s why the ADA introduced MASA (Mobile App Security Assessment), which allows developers to have their apps independently validated against the Mobile Application Security Verification Standard (MASVS standard) under the OWASP Mobile Application Security project. The project’s mission is to “Define the industry standard for mobile application security,” and has been used by both public and private sector organizations as a form of industry best practices when it comes to mobile application security. Developers can work directly with an ADA Authorized Lab to have their apps evaluated against a set of MASVS L1 requirements. Once successful, the app’s validation is listed in the recently launched App Validation Directory, which provides users a single place to view all app validations. The Directory also allows users to access more assessment details including validation date, test lab, and a report showing all test steps and requirements. The Directory will be updated over time with new features and search functionality to make it more user friendly.
The Google Play Store is the first commercial app store to recognize and display a badge for any app that has completed an independent security review through ADA MASA. The badge is displayed within an app’s respective Data Safety section.
To learn more about the program and to help developers get started, there’s a Play Academy course dedicated to independent security review. Check out the interactive guidance on the Academy for App Success and get started today!
Cloud App Security Assessment (CASA)
As the industry continues to evolve and software connects more systems through complex cloud-to-cloud integrations, focusing on the security of cloud applications and their supporting infrastructure becomes increasingly critical. CASA (Cloud App Security Assessment) leverages the work set forth in OWASP’s Application Security Verification Standard ASVS to provide a consistent set of requirements to harden security for any application. The CASA framework provides multiple assurance levels in which low-risk cloud applications can be evaluated using either a self assessment or automated scan. For applications which present higher risk (such as a large user base, recent security breach, or processes highly sensitive data), an Authorized Lab may perform an assessment.
Further, the CASA accelerator provides developers with a workflow that minimizes the required checks depending on the developer's current valid certifications. The CASA checks have been mapped to 10 certifications and frameworks which eliminate redundant testing while lowering the cost of the assessment. Google is continuing to invest in this space with plans to use ASVS more proactively with the developer community next year.
It's been amazing to see the ADA grow this year and we are excited for the continued progress and expansion around the alliance’s mission.
Today, we’re launching the OSV-Scanner, a free tool that gives open source developers easy access to vulnerability information relevant to their project.
Last year, we undertook an effort to improve vulnerability triage for developers and consumers of open source software. This involved publishing the Open Source Vulnerability (OSV) schema and launching the OSV.dev service, the first distributed open source vulnerability database. OSV allows all the different open source ecosystems and vulnerability databases to publish and consume information in one simple, precise, and machine readable format.
The OSV-Scanner is the next step in this effort, providing an officially supported frontend to the OSV database that connects a project’s list of dependencies with the vulnerabilities that affect them.
Software projects are commonly built on top of a mountain of dependencies—external software libraries you incorporate into a project to add functionalities without developing them from scratch. Each dependency potentially contains existing known vulnerabilities or new vulnerabilities that could be discovered at any time. There are simply too many dependencies and versions to keep track of manually, so automation is required.
Scanners provide this automated capability by matching your code and dependencies against lists of known vulnerabilities and notifying you if patches or updates are needed. Scanners bring incredible benefits to project security, which is why the 2021 U.S. Executive Order for Cybersecurity included this type of automation as a requirement for national standards on secure software development.
The OSV-Scanner generates reliable, high-quality vulnerability information that closes the gap between a developer’s list of packages and the information in vulnerability databases. Since the OSV.dev database is open source and distributed, it has several benefits in comparison with closed source advisory databases and scanners:
Running OSV-Scanner on your project will first find all the transitive dependencies that are being used by analyzing manifests, SBOMs, and commit hashes. The scanner then connects this information with the OSV database and displays the vulnerabilities relevant to your project.
OSV-Scanner is also integrated into the OpenSSF Scorecard’s Vulnerabilities check, which will extend the analysis from a project’s direct vulnerabilities to also include vulnerabilities in all its dependencies. This means that the 1.2M projects regularly evaluated by Scorecard will have a more comprehensive measure of their project security.
The OSV project has made lots of progress since our last post in June last year. The OSV schema has seen significant adoption from vulnerability databases such as GitHub Security Advisories and Android Security Bulletins. Altogether OSV.dev now supports 16 ecosystems, including all major language ecosystems, linux distributions (Debian and Alpine), as well as Android, linux Kernel, and OSS-Fuzz. This means the OSV.dev database is now the biggest open source vulnerability database of its kind, with a total of over 38,000 advisories from 15,000 advisories a year ago.
The OSV.dev website also had a complete overhaul, and now has a better UI and provides more information on each vulnerability. Prominent open source projects have also started to rely on OSV.dev, such as DependencyTrack and Flutter.
There’s still a lot to do! Our plan for OSV-Scanner is not just to build a simple vulnerability scanner; we want to build the best vulnerability management tool—something that will also minimize the burden of remediating known vulnerabilities. Here are some of our ideas for achieving this:
You can download and try out OSV-Scanner on your projects by following instructions on our new website osv.dev. Or alternatively, to automatically run OSV-Scanner on your GitHub project, try Scorecard. Please feel free to let us know what you think! You can give us feedback either by opening an issue on our Github, or through the OSV mailing list.
We care deeply about privacy. We also know that trust is built by transparency. This blog, and the technical paper reference within, is an example of that commitment: we describe an important new Android privacy infrastructure called Private Compute Core (PCC).
Some of our most exciting machine learning features use continuous sensing data — information from the microphone, camera, and screen. These features keep you safe, help you communicate, and facilitate stronger connections with people you care about. To unlock this new generation of innovative concepts, we built a specialized sandbox to privately process and protect this data.
PCC is a secure, isolated data processing environment inside of the Android operating system that gives you control of the data inside, such as deciding if, how, and when it is shared with others. This way, PCC can enable features like Live Translate without sharing continuous sensing data with service providers, including Google.
PCC is part of Protected Computing, a toolkit of technologies that transform how, when, and where data is processed to technically ensure its privacy and safety. For example, by employing cloud enclaves, edge processing, or end-to-end encryption we ensure sensitive data remains in exclusive control of the user.
PCC is designed to enable innovative features while keeping the data needed for them confidential from other subsystems. We do this by using techniques such as limiting Interprocess Communications (IPC) binds and using isolated processes. These are included as part of the Android Open Source Project and controlled by publicly available surfaces, such as Android framework APIs. For features that run inside PCC, continuous sensing data is processed safely and seamlessly while keeping it confidential.
To stay useful, any machine learning feature has to get better over time. To keep the models that power PCC features up to date, while still keeping the data private, we leverage federated learning and analytics. Network calls to improve the performance of these models can be monitored using Private Compute Services.
The publicly-verifiable architectures in PCC demonstrate how we strive to deliver confidentiality and control, and do it in a way that is verifiable and visible to users. In addition to this blog, we provide this transparency through public documentation and open-source code — we hope you'll have a look below.
To explain in even more detail, we’ve published a technical whitepaper for researchers and interested members of the community. In it, we describe data protections in-depth, the processes and mechanisms we’ve built, and include diagrams of the privacy structures for continuous sensing features.
Private Compute Services was recently open-sourced as well, and we invite our Android community to inspect the code that controls the data management and egress policies. We hope you'll examine and report back on PCC's implementation, so that our own documentation is not the only source of analysis.
Being transparent and engaged with users, developers, researchers, and technologists around the world is part of what makes Android special and, we think, more trustworthy. The paradigm of distributed trust, where credibility is built up from verification by multiple trusted sources, continues to extend this core value. Open sourcing the mechanisms for data protection and processes is one step towards making privacy verifiable. The next step is verification by the community — and we hope you'll join in.
We'll continue sharing our progress and look forward to hearing feedback from our users and community on the evolution of Private Compute Core and data privacy at Google.
As a follow-up to a previous blog post about How Hash-Based Safe Browsing Works in Google Chrome, we wanted to provide more details about Safe Browsing’s Enhanced Protection mode in Chrome. Specifically, how it came about, the protections that are offered and what it means for your data.
Security and privacy have always been top of mind for Chrome. Our goal is to make security effortless for you while browsing the web, so that you can go about your day without having to worry about the links that you click on or the files that you download. This is why Safe Browsing’s phishing and malware protections have been a core part of Chrome since 2007. You may have seen these in action if you have ever come across one of our red warning pages.
We show these warnings whenever we believe a site that you are trying to visit or file that you are trying to download might put you at risk for an attack. To give you a better understanding of how the Enhanced Protection mode in Safe Browsing provides the strongest level of defense it’s useful to know what is offered in Standard Protection.
Enabled by default in Chrome, Standard Protection was designed to be privacy preserving at its core by using hash-based checks. This has been effective at protecting users by warning millions of users about dangerous websites. However, hash-based checks are inherently limited as they rely on lookups to a list of known bad sites. We see malicious actors moving fast and constantly evolving their tactics to avoid detection using sophisticated techniques. To counter this, we created a stronger and more customized level of protection that we could offer to users. To this end, we launched Enhanced Protection in 2020, which builds upon the Standard Protection mode in Safe Browsing to keep you safer.
This is the fastest and strongest level of protection against dangerous sites and downloads that Safe Browsing offers in Chrome. It enables more advanced detection techniques that adapt quickly as malicious activity evolves. As a result, Enhanced Protection users are phished 20-35% less than users on Standard Protection. A few of these features include:
By opting into Enhanced Protection, you are sharing additional data with Safe Browsing systems that allow us to offer better and faster security both for you, and for all users online. Ensuring user privacy is of utmost importance for us and we go through great lengths to anonymize as much of the data as possible. This data is only used for security purposes and only retained for a short period of time. As threats evolve we will continuously add and improve our existing protections for Enhanced Protection users. These features go through extensive privacy reviews to ensure that your privacy continues to be prioritized while still providing you the highest level of security possible.
Safe Browsing’s Enhanced Protection is currently available for all desktop platforms, Android devices and now iOS mobile devices. It can be enabled by navigating to the Privacy and Security option located in Chrome settings.
For enterprise admins, you have the option of enabling Enhanced Safe Browsing on your managed devices using the SafeBrowsingProtectionLevel policy and in the Admin Console.
For more details and updates about Safe Browsing and its Enhanced Protection mode, please visit our Google Safe Browsing website and follow the Google Security Blog for updates on new features.
For more than a decade, memory safety vulnerabilities have consistently represented more than 65% of vulnerabilities across products, and across the industry. On Android, we’re now seeing something different - a significant drop in memory safety vulnerabilities and an associated drop in the severity of our vulnerabilities.
Looking at vulnerabilities reported in the Android security bulletin, which includes critical/high severity vulnerabilities reported through our vulnerability rewards program (VRP) and vulnerabilities reported internally, we see that the number of memory safety vulnerabilities have dropped considerably over the past few years/releases. From 2019 to 2022 the annual number of memory safety vulnerabilities dropped from 223 down to 85.
This drop coincides with a shift in programming language usage away from memory unsafe languages. Android 13 is the first Android release where a majority of new code added to the release is in a memory safe language.
While correlation doesn’t necessarily mean causation, it’s interesting to note that the percent of vulnerabilities caused by memory safety issues seems to correlate rather closely with the development language that’s used for new code. This matches the expectations published in our blog post 2 years ago about the age of memory safety vulnerabilities and why our focus should be on new code, not rewriting existing components. Of course there may be other contributing factors or alternative explanations. However, the shift is a major departure from industry-wide trends that have persisted for more than a decade (and likely longer) despite substantial investments in improvements to memory unsafe languages.We continue to invest in tools to improve the safety of our C/C++. Over the past few releases we’ve introduced the Scudo hardened allocator, HWASAN, GWP-ASAN, and KFENCE on production Android devices. We’ve also increased our fuzzing coverage on our existing code base. Vulnerabilities found using these tools contributed both to prevention of vulnerabilities in new code as well as vulnerabilities found in old code that are included in the above evaluation. These are important tools, and critically important for our C/C++ code. However, these alone do not account for the large shift in vulnerabilities that we’re seeing, and other projects that have deployed these technologies have not seen a major shift in their vulnerability composition. We believe Android’s ongoing shift from memory-unsafe to memory-safe languages is a major factor.
In Android 12 we announced support for the Rust programming language in the Android platform as a memory-safe alternative to C/C++. Since then we’ve been scaling up our Rust experience and usage within the Android Open Source Project (AOSP).As we noted in the original announcement, our goal is not to convert existing C/C++ to Rust, but rather to shift development of new code to memory safe languages over time.
In Android 13, about 21% of all new native code (C/C++/Rust) is in Rust. There are approximately 1.5 million total lines of Rust code in AOSP across new functionality and components such as Keystore2, the new Ultra-wideband (UWB) stack, DNS-over-HTTP3, Android’s Virtualization framework (AVF), and various other components and their open source dependencies. These are low-level components that require a systems language which otherwise would have been implemented in C++.
To date, there have been zero memory safety vulnerabilities discovered in Android’s Rust code.
We don’t expect that number to stay zero forever, but given the volume of new Rust code across two Android releases, and the security-sensitive components where it’s being used, it’s a significant result. It demonstrates that Rust is fulfilling its intended purpose of preventing Android’s most common source of vulnerabilities. Historical vulnerability density is greater than 1/kLOC (1 vulnerability per thousand lines of code) in many of Android’s C/C++ components (e.g. media, Bluetooth, NFC, etc). Based on this historical vulnerability density, it’s likely that using Rust has already prevented hundreds of vulnerabilities from reaching production.
Operating system development requires accessing resources that the compiler cannot reason about. For memory-safe languages this means that an escape hatch is required to do systems programming. For Java, Android uses JNI to access low-level resources. When using JNI, care must be taken to avoid introducing unsafe behavior. Fortunately, it has proven significantly simpler to review small snippets of C/C++ for safety than entire programs. There are no pure Java processes in Android. It’s all built on top of JNI. Despite that, memory safety vulnerabilities are exceptionally rare in our Java code.
Rust likewise has the unsafe{} escape hatch which allows interacting with system resources and non-Rust code. Much like with Java + JNI, using this escape hatch comes with additional scrutiny. But like Java, our Rust code is proving to be significantly safer than pure C/C++ implementations. Let’s look at the new UWB stack as an example.
There are exactly two uses of unsafe in the UWB code: one to materialize a reference to a Rust object stored inside a Java object, and another for the teardown of the same. Unsafe was actively helpful in this situation because the extra attention on this code allowed us to discover a possible race condition and guard against it.
In general, use of unsafe in Android’s Rust appears to be working as intended. It’s used rarely, and when it is used, it’s encapsulating behavior that’s easier to reason about and review for safety.
Mobile devices have limited resources and we’re always trying to make better use of them to provide users with a better experience (for example, by optimizing performance, improving battery life, and reducing lag). Using memory unsafe code often means that we have to make tradeoffs between security and performance, such as adding additional sandboxing, sanitizers, runtime mitigations, and hardware protections. Unfortunately, these all negatively impact code size, memory, and performance.
Using Rust in Android allows us to optimize both security and system health with fewer compromises. For example, with the new UWB stack we were able to save several megabytes of memory and avoid some IPC latency by running it within an existing process. The new DNS-over-HTTP/3 implementation uses fewer threads to perform the same amount of work by using Rust’s async/await feature to process many tasks on a single thread in a safe manner.
The number of vulnerabilities reported in the bulletin has stayed somewhat steady over the past 4 years at around 20 per month, even as the number of memory safety vulnerabilities has gone down significantly. So, what gives? A few thoughts on that.
Memory safety vulnerabilities disproportionately represent our most severe vulnerabilities. In 2022, despite only representing 36% of vulnerabilities in the security bulletin, memory-safety vulnerabilities accounted for 86% of our critical severity security vulnerabilities, our highest rating, and 89% of our remotely exploitable vulnerabilities. Over the past few years, memory safety vulnerabilities have accounted for 78% of confirmed exploited “in-the-wild” vulnerabilities on Android devices.
Many vulnerabilities have a well defined scope of impact. For example, a permissions bypass vulnerability generally grants access to a specific set of information or resources and is generally only reachable if code is already running on the device. Memory safety vulnerabilities tend to be much more versatile. Getting code execution in a process grants access not just to a specific resource, but everything that that process has access to, including attack surface to other processes. Memory safety vulnerabilities are often flexible enough to allow chaining multiple vulnerabilities together. The high versatility is perhaps one reason why the vast majority of exploit chains that we have seen use one or more memory safety vulnerabilities.With the drop in memory safety vulnerabilities, we’re seeing a corresponding drop in vulnerability severity.
Despite most of the existing code in Android being in C/C++, most of Android’s API surface is implemented in Java. This means that Java is disproportionately represented in the OS’s attack surface that is reachable by apps. This provides an important security property: most of the attack surface that’s reachable by apps isn’t susceptible to memory corruption bugs. It also means that we would expect Java to be over-represented when looking at non-memory safety vulnerabilities. It’s important to note however that types of vulnerabilities that we’re seeing in Java are largely logic bugs, and as mentioned above, generally lower in severity. Going forward, we will be exploring how Rust’s richer type system can help prevent common types of logic bugs as well.
With the vulnerability types we’re seeing now, Google’s ability to detect and prevent misuse is considerably better. Apps are scanned to help detect misuse of APIs before being published on the Play store and Google Play Protect warns users if they have abusive apps installed.
Migrating away from C/C++ is challenging, but we’re making progress. Rust use is growing in the Android platform, but that’s not the end of the story. To meet the 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. We’re implementing userspace HALs in Rust. We’re adding support for Rust in Trusted Applications. We’ve migrated VM firmware in the Android Virtualization Framework to Rust. With support for Rust landing in linux 6.1 we’re excited to bring memory-safety to the kernel, starting with kernel drivers.
As Android migrates away from C/C++ to Java/Kotlin/Rust, we expect the number of memory safety vulnerabilities to continue to fall. Here’s to a future where memory corruption bugs on Android are rare!
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