Since launching in 2016, Google's free OSS-Fuzz code testing service has helped get over 8800 vulnerabilities and 28,000 bugs fixed across 850 projects. Today, we’re happy to announce an expansion of our OSS-Fuzz Rewards Program, plus new features in OSS-Fuzz and our involvement in supporting academic fuzzing research.
The OSS-Fuzz project's purpose is to support the open source community in adopting fuzz testing, or fuzzing — an automated code testing technique for uncovering bugs in software. In addition to the OSS-Fuzz service, which provides a free platform for continuous fuzzing to critical open source projects, we established an OSS-Fuzz Reward Program in 2017 as part of our wider Patch Rewards Program.
We’ve operated this successfully for the past 5 years, and to date, the OSS-Fuzz Reward Program has awarded over $600,000 to over 65 different contributors for their help integrating new projects into OSS-Fuzz.
Today, we’re excited to announce that we’ve expanded the scope of the OSS-Fuzz Reward Program considerably, introducing many new types of rewards!
These new reward types cover contributions such as:
These changes boost the total rewards possible per project integration from a maximum of $20,000 to $30,000 (depending on the criticality of the project). In addition, we’ve also established two new reward categories that reward wider improvements across all OSS-Fuzz projects, with up to $11,337 available per category.
For more details, see the fully updated rules for our dedicated OSS-Fuzz Reward Program.
We’ve continuously made improvements to OSS-Fuzz’s infrastructure over the years and expanded our language offerings to cover C/C++, Go, Rust, Java, Python, and Swift, and have introduced support for new frameworks such as FuzzTest. Additionally, as part of an ongoing collaboration with Code Intelligence, we’ll soon have support for JavaScript fuzzing through Jazzer.js.
Last year, we launched the OpenSSF FuzzIntrospector tool and integrated it into OSS-Fuzz.
We’ve continued to build on this by adding new language support and better analysis, and now C/C++, Python, and Java projects integrated into OSS-Fuzz have detailed insights on how the coverage and fuzzing effectiveness for a project can be improved.
The FuzzIntrospector tool provides these insights by identifying complex code blocks that are blocked during fuzzing at runtime, as well as suggesting new fuzz targets that can be added. We’ve seen users successfully use this tool to improve the coverage of jsonnet, file, xpdf and bzip2, among others.
Anyone can use this tool to increase the coverage of a project and in turn be rewarded as part of the refreshed OSS-Fuzz rewards. See the full list of all OSS-Fuzz FuzzIntrospector reports to get started.
The OSS-Fuzz team maintains FuzzBench, a service that enables security researchers in academia to test fuzzing improvements against real-world open source projects. Approaching its third anniversary in serving free benchmarking, FuzzBench is cited by over 100 papers and has been used as a platform for academic fuzzing workshops such as NDSS’22.
This year, FuzzBench has been invited to participate in the SBFT'23 workshop in ICSE, a premier research conference in the field, which for the first time is hosting a fuzzing competition. During this competition, the FuzzBench platform will be used to evaluate state-of-the-art fuzzers submitted by researchers from around the globe on both code coverage and bug-finding metrics.
We believe these initiatives will help scale security testing efforts across the broader open source ecosystem. We hope to accelerate the integration of critical open source projects into OSS-Fuzz by providing stronger incentives to security researchers and open source maintainers. Combined with our involvement in fuzzing research, these efforts are making OSS-Fuzz an even more powerful tool, enabling users to find more bugs, and, more critically, find them before the bad guys do!
Note: This post is a follow-up to discussions carried out on the Mozilla “Dev Security Policy” Web PKI public discussion forum Google Group in December 2022. Google Chrome communicated its distrust of TrustCor in the public forum on December 15, 2022.
The Chrome Security Team prioritizes the security and privacy of Chrome’s users, and we are unwilling to compromise on these values.
Google includes or removes CA certificates within the Chrome Root Store as it deems appropriate for user safety in accordance with our policies. The selection and ongoing inclusion of CA certificates is done to enhance the security of Chrome and promote interoperability.
Behavior that attempts to degrade or subvert security and privacy on the web is incompatible with organizations whose CA certificates are included in the Chrome Root Store. Due to a loss of confidence in its ability to uphold these fundamental principles and to protect and safeguard Chrome’s users, certificates issued by TrustCor Systems will no longer be recognized as trusted by:
This change was first communicated in the Mozilla “Dev Security Policy” Web PKI public discussion forum Google Group on December 15, 2022.
This change will be implemented via our existing mechanisms to respond to CA incidents via:
Beginning approximately March 7, 2023, navigations to websites that use a certificate that chains to one of the roots detailed below will be considered insecure and result in a full page certificate error interstitial.
Affected Certificates (SHA-256 fingerprint):
This change will be integrated into the Chromium open-source project as part of a default build. Questions about the expected behavior in specific Chromium-based browsers should be directed to their maintainers.
This change will be incorporated as part of the regular Chrome release process to ensure sufficient time for testing and replacing affected certificates by website operators. Information about release timetables and milestones is available at https://chromiumdash.appspot.com/schedule.
Beginning approximately February 9, 2023, website operators can preview these changes in Chrome 111 Beta. Website operators will also be able to preview the change sooner, using our Dev and Canary channels. The majority of users will not encounter behavior changes until the release of Chrome 111 to the Stable channel, approximately March 7, 2023.
Summarizing security response of other Google products:
We are pleased to announce that moving forward, the Chromium project is going to support the use of third-party Rust libraries from C++ in Chromium. To do so, we are now actively pursuing adding a production Rust toolchain to our build system. This will enable us to include Rust code in the Chrome binary within the next year. We’re starting slow and setting clear expectations on what libraries we will consider once we’re ready.
In this blog post, we will discuss how we arrived at the decision to support third-party Rust libraries at this time, and not broader usage of Rust in Chromium.
Why We Chose to Bring Rust into Chromium
Our goal in bringing Rust into Chromium is to provide a simpler (no IPC) and safer (less complex C++ overall, no memory safety bugs in a sandbox either) way to satisfy the rule of two, in order to speed up development (less code to write, less design docs, less security review) and improve the security (increasing the number of lines of code without memory safety bugs, decreasing the bug density of code) of Chrome. And we believe that we can use third-party Rust libraries to work toward this goal.
Rust was developed by Mozilla specifically for use in writing a browser, so it’s very fitting that Chromium would finally begin to rely on this technology too. Thank you Mozilla for your huge contribution to the systems software industry. Rust has been an incredible proof that we should be able to expect a language to provide safety while also being performant.
We know that C++ and Rust can play together nicely, through tools like cxx, autocxx bindgen, cbindgen, diplomat, and (experimental) crubit. However there are also limitations. We can expect that the shape of these limitations will change in time through new or improved tools, but the decisions and descriptions here are based on the current state of technology.
How Chromium Will Support the Use of Rust
The Chrome Security team has been investing time into researching how we should approach using Rust alongside our C++ code. Understanding the implications of incrementally moving to writing Rust instead of C++, even in the middle of our software stack. What the limits of safe, simple, and reliable interop might be.
Based on our research, we landed on two outcomes for Chromium.
The Interop Between Rust and C++ in Chromium
We have observed that most successful C/C++ and Rust interop stories to date have been built around interop through narrow APIs (e.g. libraries for QUIC or bluetooth, Linux drivers) or through clearly isolated components (e.g. IDLs, IPCs). Chrome is built on foundational but really wide C++ APIs, such as the //content/public layer. We examined what it would mean for us to build Rust components against these types of APIs. At a high level what we found was that because C++ and Rust play by different rules, things can go sideways very easily.
For example, Rust guarantees temporal memory safety with static analysis that relies on two inputs: lifetimes (inferred or explicitly written) and exclusive mutability. The latter is incompatible with how the majority of Chromium’s C++ is written. We hold redundant mutable pointers throughout the system, and pointers that provide multiple paths to reach mutable pointers. We have cyclical mutable data structures. This is especially true in our browser process, which contains a giant interconnected system of (mutable) pointers. If these C++ pointers were also used as Rust references in a complex or long-lived way, it would require our C++ authors to understand the aliasing rules of Rust and prevent the possibility of violating them, such as by:
Without interop tools providing support via the compiler and the type system, developers would need to understand all of the assumptions being made by Rust compiler, in order to not violate them from C++. In this framing, C++ is much like unsafe Rust. And while unsafe Rust is very costly to a project, its cost is managed by keeping it encapsulated and to the minimum possible. In the same way, the full complexity of C++ would need to be encapsulated from safe Rust. Narrow APIs designed for interop can provide similar encapsulation, and we hope that interop tools can provide encapsulation in other ways that allow wider APIs between the languages.
The high-level summary is that without additional interop tooling support:
Any cross-language interop between arbitrary code introduces difficulties where concepts in one language are not found in the other. For Rust calling into C++, support for language features like templates or inheritance can be difficult for a binding generator to support. For C++ calling into Rust, proc macros, and traits are examples that provide similar challenges. At times, the impedance mismatch represents intentional design choices made for either language, however they also imply limits on FFI (interop) between the languages. We rely on interop tools to model the ideas of each language in a way that makes sense to the other, or to disallow them.
Accessing the Rust Ecosystem from Chromium
These challenges present an opportunity, both to make interop easier and more seamless, but also to get access to a wider range of libraries from either language. Google is investing in Crubit, an experiment in how to increase the fidelity of interop between C++ and Rust and express or encapsulate the requirements of each language to the other.
The Rust ecosystem is incredibly important, especially to a security-focused open source project like Chromium. The ecosystem is enormous (96k+ crates on crates.io) and growing, with investment from the systems development industry at large, including Google. Chrome relies heavily on third-party code, and we need to keep up with where that third-party investment is happening. It is critical that we build out support for including Rust into the Chromium project.
We will be following this strategy to establish norms, and to maintain a level of API review through the third-party process, while we look to the future of interop support pushing the boundaries of what is possible and reasonable to do between Rust and C++.
Some Other Related Content
Memory unsafety is an industry-wide problem, and making use of Rust is one part of a strategy to move the needle in this area. Recently, Android and Apple have each published a great blog post on the subject if you’re interested in learning more. With Chrome’s millions of lines of C++, we’re still working hard to improve the safety of our C++ too, through projects such as MiraclePtr.
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!
We believe that security and transparency are paramount pillars for electronic products connected to the Internet. Over the past year, we’ve been excited to see more focused activity across policymakers, industry partners, developers, and public interest advocates around raising the security and transparency bar for IoT products.
That said, the details of IoT product labeling - the definition of labeling, what labeling needs to convey in terms of security and privacy, where the label should reside, and how to achieve consumer acceptance, are still open for debate. Google has also been considering these core questions for a long time. As an operating system, IoT product provider, and the maintainer of multiple large ecosystems, we see firsthand how critical these details will be to the future of the IoT. In an effort to be a catalyst for collaboration and transparency, today we’re sharing our proposed list of principles around IoT security labeling.
Setting the Stage: Defining IoT Labeling
IoT labeling is a complex and nuanced topic, so as an industry, we should first align on a set of labeling definitions that could help reduce potential fragmentation and offer a harmonized approach that could drive a desired outcome:
Proposed Principles for IoT Security Labeling Schemes
We believe in five core principles for IoT labeling schemes. These principles will help increase transparency against the full baseline of security criteria for IoT. These principles will also increase competition in security and push manufacturers to offer products with effective security protections, increase transparency, and help generate higher levels of assurance of protection over time.
1. A printed label must not imply trust
Unlike food labels, digital security labels must be “live” labels, where security/privacy status is conveyed on a central maintained website, which ideally would be the same site hosting the evaluation scheme. A physical label, either printed on a box or visible in an app, can be used if and only if it encourages users to visit the website (e.g. scan a QR code or click a link) to obtain the real-time status.
At any point in time, a digital product may become unsafe for use. For example, if a critical, in-the-wild, remote exploit of a product is discovered and cannot be mitigated (e.g. via a patch), then it may be necessary to change that product’s status from safe to unsafe.
Printed labels, if they convey trust implicitly such as, “certified to NNN standard” or, “3 stars”, run the danger of influencing consumers to make harmful decisions. A consumer may purchase a webcam with a “3-star” security label only to find when they return home the product has non-mitigatable vulnerabilities that make it unsafe. Or, a product may sit on a shelf long enough to become non-compliant or unsafe. Labeling programs should help consumers make better security decisions. The dangers around a printed “trust me” label will in some circumstances, mislead consumers.
2. Labels must reference strong international evaluation schemes
The challenge of utilizing a labeling scheme is not the physical manifestation of the label but rather ensuring that the label references a security/privacy status/posture that is maintained by a trustworthy security/privacy evaluation scheme, such as the ones being developed by the Connectivity Standards Alliance (CSA) and GSMA. Both of these organizations are actively developing IoT security/privacy evaluation schemes that reference well-regarded standards, including recent IoT baseline security guidance from NIST, ETSI, ISO, and OWASP. Some important requirements for evaluation schemes leveraged by a national labeling program include:
Strong governance: The NGO must have strong governance. For example, NGOs that house both a scheme and their own in-house evaluation lab introduce potential conflicts of interest that should be avoided.
Strong track record for managing evaluation schemes at scale: Managing a high quality, global scheme is hard. National authorities have struggled at this for many years, especially in the consumer realm. An NGO that has no prior track record of managing a scheme with significant global adoption is unlikely to be sufficiently trustworthy for a national labeling scheme to reference. CSA and GSMA have long track records of managing global schemes that have stood the test of time.
Choice with a high quality bar: The world needs a small set of high quality evaluation schemes that can act as the hub within a hub and spoke model for enabling national labeling schemes across the globe. Evaluation schemes will authorize a range of labs for lab-tested results, providing price competition for lab engagements. We need more than one scheme to encourage competition among evaluation schemes, as they too will levy fees for membership, certification, and monitoring. However, balance is key, as too many schemes could be challenging for governments to monitor and trust. Setting a high bar for governance and track record, as described above, will help curate global evaluation scheme choices.
International participation: National labeling schemes must recognize that many manufacturers sell products across the world. A national label that does not reference NGOs that serve the global community will force multiple inconsistent national labeling schemes that are prohibitively expensive for small and medium size product developers. Misaligned or non-harmonized national efforts may become a significant barrier to entry for smaller vendors and run counter to the intended goals of competition-enhancing policies in their respective markets.
Assurance maintenance: The NGO evaluation scheme must provide a mechanism for independent researchers to pressure test conformance claims made by manufacturers. Moment-in-time certifications have historically plagued security evaluation schemes, and for cost reasons, forced annual re-certifications are not the answer either. For the vast majority of consumer products, we should rely on crowdsourced research to identify weaknesses that may question a certification result. This approach has succeeded in helping to maintain the security of numerous global products and platforms and is especially needed to help monitor the results of self-attestation certifications that will be needed in any national scale labeling program. This is also an area where federal funding may be most needed; security bounty programs will add even more incentive for the security community to pressure test evaluation scheme results and hold the entire labeling program supply chain accountable. These reward programs are also a great way to recruit more people into the cybersecurity field.
3. A minimum security baseline must be coupled with flexibility above it
A minimum security baseline must be coupled with flexibility to define additional requirements and/or levels to accelerate ecosystem improvements. Security labeling is nascent, and most schemes are focused on common sense baseline requirement standards. These standards will set an important minimum bar for digital security, reducing the likelihood that consumers will be exposed to truly poor security practices. However, we should never say things like, “we need a labeling scheme to ensure that digital products are secure.” Security is not a binary state. Applying a minimum set of best practices will not magically make a product free of vulnerabilities. But it will discourage the most common security foibles. Furthermore, it is folly to expect that baseline security standards will protect against advanced persistent threat actors. Rather, they’ll hopefully provide broad protection against common opportunistic attackers. The Mirai botnet attack was so successful because so many digital products lack the most rudimentary security functionality: the ability to apply a security update in the field.
Over time we need to do better. Security evaluation schemes need to be sufficiently flexible to allow for additional security functional requirements to be measured and rated across products. For example, the current baseline security requirements do not cover things like the strength of a biometric authenticator (important for phones and a growing range of consumer digital products) nor do they provide a standardized method for comparing the relative strength of security update policies (e.g. a product that receives regular updates for five years should be valued more highly by consumers than one that receives updates for two years). Communities that focus on specific vertical markets of product families are motivated to create security functional requirement profiles (and labels) that go above and beyond the baseline and are more tailored for that product category. Labeling schemes must allow for this flexibility, as long as profile compliance is managed by high quality evaluation schemes.
Similarly, in addition to functionality such as biometrics and update frequency, labels need to allow for assurance levels, which answer the question, “how much confidence should we have in this product’s security functionality claims?” For example, emerging consumer evaluation schemes may permit a self-attestation of conformance or a lab test that validates basic security functionality. These kinds of attestations yield relatively low assurance, but still better than none. Today’s schemes do not allow for an assessment that emulates a high potential attacker trying to break the system’s security functionality. To date, due to cost and complexity, high potential attacker vulnerability assessments have been limited to a vanishingly small number of products, including secure elements and small hypervisors. Yet for a nation’s most critical systems, such as connected medical devices, cars, and applications that manage sensitive data for millions of consumers, a higher level of assurance will be needed, and any labeling scheme must not preclude future extensions that offer higher levels of assurance.
4. Broad-based transparency is just as important as the minimum bar
While it is desirable that labeling schemes provide consumers with simple guidance on safety, the desire for such a simple bar forces it to be the lowest common denominator for security capability so as not to preclude large portions of the market. It is equally important that labeling schemes increase transparency in security. So much of the discussion around labeling schemes has focused on selecting the best possible minimum bar rather than promoting transparency of security capability, regardless of what minimum bar a product may meet. This is short-sighted and fails to learn from many other consumer rating schemes (e.g. Consumer Reports) that have successfully provided transparency around a much wider range of product capabilities over time.
Again, while a common baseline is a good place to start, we must also encourage the use of more comprehensive requirement specifications developed by high-quality NGO standards bodies and/or schemes against which products can be assessed. The goal of this method is not to mandate every requirement above the baseline, but rather to mandate transparency of compliance against those requirements. Similar to many other consumer rating schemes, the transparency across a wide range of important capabilities (e.g. the biometrics example above) will enable easy side-by-side comparison during purchasing decisions, which will act as the tide to raise all boats, driving product developers to compete with each other in security. This already happens with speeds and feeds, battery life, energy consumption, and many other features that people care about. For example, the requirement for transparency could classify the strength of the biometric based on spoof / presentation attack detection rate, which we measure for Android. If we develop more comprehensive transparency in our labeling scheme, consumers will learn and care about a wider range of security capabilities that today remain below the veil; that awareness will drive demand for product developers to do better.
5. Labeling schemes are useless without adoption incentive
Transparency is the core concept that can raise demand and improve supply of better security across the IoT. However, what will cause products to be evaluated so that security capability data will be published and made easily consumable? After thirty years of the world wide web and connected digital technology, it is clear that simply expecting product developers to “do the right thing” for security is insufficient.
“Voluntary” regimes will attract the same developers that are already doing good security work and depend on doing so for their customers and brands. Security is, on average, poor across the IoT market because product developers optimize for profitability, and the economic impact of poor security is usually not sufficiently high to move the needle. Many avenues can lead to increased economic incentives for improved security. That means a mix of carrots and sticks will be necessary to incentivize developers to increase the security of their products.
National labeling schemes should focus on a few of the biggest market movers, in order of decreasing impact:
National mandate: Some national governments are moving towards legislation or executive orders that will require common baseline security requirements to be met, with corresponding labeling to differentiate compliant products from those not covered by the mandate. National mandates can drive improved behavior at scale. However, mandating a poor labeling scheme can do more harm than good. For example, if every nation creates a bespoke evaluation scheme, small and medium size developers would be priced out of the market due to the need to recertify and label their products across all these schemes. Not only will non-harmonized approaches harm industry financially, it will also inhibit innovation as developers create less inclusive products to avoid nations with painful labeling regimes.
National mandates and labeling schemes must reference broadly applicable, high quality, NGO standards and schemes (as described above) so that they can be reused across multiple national labeling schemes. Global normalization and cross-recognition is not a nice-to-have, national schemes will fail if they do not solve for this important economic reality upfront. Ideally, government officials who care about a successful national labeling scheme should be involved to nurture and guide the NGO schemes that are trying to solve this problem globally.
Retailers: Retailers of digital products could have a huge impact by preferencing baseline standards compliance for digital products. In its most impactful form, the retailer would mandate compliance for all products listed for sale. The larger the retailer, the more impact is possible. Less broad, but still extremely impactful, would be providing visual labeling and/or search and discovery preferences for products that meet the requirements specified in high quality security evaluation schemes.
Platform developers: Many digital products exist as part of platforms, such as devices built on the Android Open Source Project (AOSP) platform or apps published on the Google Play app store platform. In addition, interoperability standards such as Matter and Bluetooth act as platforms, certifying products that meet those interoperability standards. All of these platform developers may use security compliance within larger certification, compliance, and business incentive programs that can drive adoption at
scale. The impact depends on the size and scale of the platform and whether the carrots provided by platform providers are sufficiently attractive.
Continuing to Strive For Collaboration, Standardization, and Transparency
Our goal is to increase transparency against the full baseline of security criteria for the IoT over time. This will help drive “competition” in security and push manufacturers to offer products with more robust security protections. But we don’t want to stop at just increasing transparency. We will also strive to build realistic higher levels of assurance. As labeling efforts gain steam, we are hopeful that public sector and industry can work together to drive global harmonization to prevent fragmentation, and we hope to provide our expertise and act as a valued partner to governments as they develop policies to help their countries stay ahead of the latest threats in IoT. We look forward to our continued partnership with governments and industry to reduce complexity and increase innovation while improving global cybersecurity.
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See also: Google testimony on security labeling and evaluation schemes in UK ParliamentSee also: Google participated in a White House strategic discussion on IoT Security Labeling
Supply chain security is at the fore of the industry’s collective consciousness. We’ve recently seen a significant rise in software supply chain attacks, a Log4j vulnerability of catastrophic severity and breadth, and even an Executive Order on Cybersecurity.
It is against this background that Google is seeking contributors to a new open source project called GUAC (pronounced like the dip). GUAC, or Graph for Understanding Artifact Composition, is in the early stages yet is poised to change how the industry understands software supply chains. GUAC addresses a need created by the burgeoning efforts across the ecosystem to generate software build, security, and dependency metadata. True to Google’s mission to organize and make the world’s information universally accessible and useful, GUAC is meant to democratize the availability of this security information by making it freely accessible and useful for every organization, not just those with enterprise-scale security and IT funding.
Thanks to community collaboration in groups such as OpenSSF, SLSA, SPDX, CycloneDX, and others, organizations increasingly have ready access to:
These data are useful on their own, but it’s difficult to combine and synthesize the information for a more comprehensive view. The documents are scattered across different databases and producers, are attached to different ecosystem entities, and cannot be easily aggregated to answer higher-level questions about an organization’s software assets.
To help address this issue we’ve teamed up with Kusari, Purdue University, and Citi to create GUAC, a free tool to bring together many different sources of software security metadata. We’re excited to share the project’s proof of concept, which lets you query a small dataset of software metadata including SLSA provenance, SBOMs, and OpenSSF Scorecards.
Graph for Understanding Artifact Composition (GUAC) aggregates software security metadata into a high fidelity graph database—normalizing entity identities and mapping standard relationships between them. Querying this graph can drive higher-level organizational outcomes such as audit, policy, risk management, and even developer assistance.
Conceptually, GUAC occupies the “aggregation and synthesis” layer of the software supply chain transparency logical model:
GUAC has four major areas of functionality:
A CISO or compliance officer in an organization wants to be able to reason about the risk of their organization. An open source organization like the Open Source Security Foundation wants to identify critical libraries to maintain and secure. Developers need richer and more trustworthy intelligence about the dependencies in their projects.
The good news is, increasingly one finds the upstream supply chain already enriched with attestations and metadata to power higher-level reasoning and insights. The bad news is that it is difficult or impossible today for software consumers, operators, and administrators to gather this data into a unified view across their software assets.
To understand something complex like the blast radius of a vulnerability, one needs to trace the relationship between a component and everything else in the portfolio—a task that could span thousands of metadata documents across hundreds of sources. In the open source ecosystem, the number of documents could reach into the millions.
GUAC aggregates and synthesizes software security metadata at scale and makes it meaningful and actionable. With GUAC in hand, we will be able to answer questions at three important stages of software supply chain security:
Get Involved
GUAC is an Open Source project on Github, and we are excited to get more folks involved and contributing (read the contributor guide to get started)! The project is still in its early stages, with a proof of concept that can ingest SLSA, SBOM, and Scorecard documents and support simple queries and exploration of software metadata. The next efforts will focus on scaling the current capabilities and adding new document types for ingestion. We welcome help and contributions of code or documentation.
Since the project will be consuming documents from many different sources and formats, we have put together a group of “Technical Advisory Members'' to help advise the project. These members include representation from companies and groups such as SPDX, CycloneDX Anchore, Aquasec, IBM, Intel, and many more. If you’re interested in participating as a contributor or advisor representing end users’ needs—or the sources of metadata GUAC consumes—you can register your interest in the relevant GitHub issue.
The GUAC team will be showcasing the project at Kubecon NA 2022 next week. Come by our session if you’ll be there and have a chat with us—we’d be happy to talk in person or virtually!
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