The Android platform team is committed to securing Android for every user across every device. In addition to monthly security updates to patch vulnerabilities reported to us through our Vulnerability Rewards Program (VRP), we also proactively architect Android to protect against undiscovered vulnerabilities through hardening measures such as applying compiler-based mitigations and improving sandboxing. This post focuses on the decision-making process that goes into these proactive measures: in particular, how we choose which hardening techniques to deploy and where they are deployed. As device capabilities vary widely within the Android ecosystem, these decisions must be made carefully, guided by data available to us to maximize the value to the ecosystem as a whole.
The overall approach to Android Security is multi-pronged and leverages several principles and techniques to arrive at data-guided solutions to make future exploitation more difficult. In particular, when it comes to hardening the platform, we try to answer the following questions:
By shedding some light on the process we use to choose security features for Android, we hope to provide a better understanding of Android's overall approach to protecting our users.
We use a variety of sources to determine what areas of the platform would benefit the most from different types of security mitigations. The Android Vulnerability Rewards Program (VRP) is one very informative source: all vulnerabilities submitted through this program are analyzed by our security engineers to determine the root cause of each vulnerability and its overall severity (based on these guidelines). Other sources are internal and external bug-reports, which identify vulnerable components and reveal coding practices that commonly lead to errors. Knowledge of problematic code patterns combined with the prevalence and severity of the vulnerabilities they cause can help inform decisions about which mitigations are likely to be the most beneficial.
Types of Critical and High severity vulnerabilities fixed in Android Security Bulletins in 2019
Relying purely on vulnerability reports is not sufficient as the data are inherently biased: often, security researchers flock to "hot" areas, where other researchers have already found vulnerabilities (e.g. Stagefright). Or they may focus on areas where readily-available tools make it easier to find bugs (for instance, if a security research tool is posted to Github, other researchers commonly utilize that tool to explore deeper).
To ensure that mitigation efforts are not biased only toward areas where bugs and vulnerabilities have been reported, internal Red Teams analyze less scrutinized or more complex parts of the platform. Also, continuous automated fuzzers run at-scale on both Android virtual machines and physical devices. This also ensures that bugs can be found and fixed early in the development lifecycle. Any vulnerabilities uncovered through this process are also analyzed for root cause and severity, which inform mitigation deployment decisions.
The Android VRP rewards submissions of full exploit-chains that demonstrate a full end-to-end attack. These exploit-chains, which generally utilize multiple vulnerabilities, are very informative in demonstrating techniques that attackers use to chain vulnerabilities together to accomplish their goals. Whenever a researcher submits a full exploit chain, a team of security engineers analyzes and documents the overall approach, each link in the chain, and any innovative attack strategies used. This analysis informs which exploit mitigation strategies could be employed to prevent pivoting directly from one vulnerability to another (some examples include Address Space Layout Randomization and Control-Flow Integrity) and whether the process’s attack surface could be reduced if it has unnecessary access to resources.
There are often multiple different ways to use a collection of vulnerabilities to create an exploit chain. Therefore a defense-in-depth approach is beneficial, with the goal of reducing the usefulness of some vulnerabilities and lengthening exploit chains so that successful exploitation requires more vulnerabilities. This increases the cost for an attacker to develop a full exploit chain.
Keeping up with developments in the wider security community helps us understand the current threat landscape, what techniques are currently used for exploitation, and what future trends look like. This involves but is not limited to:
All of these data sources provide feedback for the overall security hardening strategy, where new mitigations should be deployed, and what existing security mitigations should be improved.
Analyzing the data reveals areas where broader mitigations can eliminate entire classes of vulnerabilities. For instance, if parts of the platform show a large number of vulnerabilities due to integer overflow bugs, they are good candidates to enable Undefined Behavior Sanitizer (UBSan) mitigations such as the Integer Overflow Sanitizer. When common patterns in memory access vulnerabilities appear, they inform efforts to build hardened memory allocators (enabled by default in Android 11) and implement mitigations (such as CFI) against exploitation techniques that provide better resilience against memory overflows or Use-After-Free vulnerabilities.
Before discussing how the data can be used, it is important to understand how we classify our overall efforts in hardening the platform. There are a few broadly defined buckets that hardening techniques and mitigations fit into (though sometimes a particular mitigation may not fit cleanly into any single one):
With the broad arsenal of mitigation techniques available, which of these to employ and where to apply them depends on the type of problem being solved. For instance, a monolithic process that handles a lot of untrusted data and does complex parsing would be a good candidate for all of these. The media frameworks provide an excellent historical example where an architectural decomposition enabled incrementally turning on more exploit mitigations and deprivileging.
Architectural decomposition and isolation of the Media Frameworks over time
Remotely reachable attack surfaces such as NFC, Bluetooth, WiFi, and media components have historically housed the most severe vulnerabilities, and as such these components are also prioritized for hardening. These components often contain some of the most common vulnerability root causes that are reported in the VRP, and we have recently enabled sanitizers in all of them.
Libraries and processes that enforce or sit at security boundaries, such as libbinder, and widely-used core libraries such as libui, libcore, and libcutils are good targets for exploit mitigations since these are not process-specific. However, due to performance and stability sensitivities around these core libraries, mitigations need to be supported by strong evidence of their security impact.
Finally, the kernel’s high level of privilege makes it an important target for hardening as well. Because different codebases have different characteristics and functionality, susceptibility to and prevalence of certain kinds of vulnerabilities will differ. Stability and performance of mitigations here are exceptionally important to avoid negatively impacting the user experience, and some mitigations that make sense to deploy in user space may not be applicable or effective. Therefore our considerations for which hardening strategies to employ in the kernel are based on a separate analysis of the available kernel-specific data.
This data-driven approach has led to tangible and measurable results. Starting in 2015 with Stagefright, a large number of Critical severity vulnerabilities were reported in Android's media framework. These were especially sensitive because many of these vulnerabilities were remotely reachable. This led to a large architectural decomposition effort in Android Nougat, followed by additional efforts to improve our ability to patch media vulnerabilities quickly. Thanks to these changes, in 2020 we had no internet-reachable Critical severity vulnerabilities reported to us in the media frameworks.
Some of these mitigations provide more value than others, so it is important to focus engineering resources where they are most effective. This involves weighing the performance cost of each mitigation as well as how much work is required to deploy it and support it without negatively affecting device stability or user experience.
Understanding the performance impact of a mitigation is a critical step toward enabling it. Adding too much overhead to some components or the entire system can negatively impact user experience by reducing battery life and making the device less responsive. This is especially true for entry-level devices, which should benefit from hardening as well. We thus want to prioritize engineering efforts on impactful mitigations with acceptable overheads.
When investigating performance, important factors include not just CPU time but also memory increase, code size, battery life, and UI jank. These factors are especially important to consider for more constrained entry-level devices, to ensure that the mitigations perform well across the entire Android ecosystem.
The system-wide performance impact of a mitigation is also dependent on where that mitigation is enabled, as certain components are more performance-sensitive than others. For example, binder is one of the most used paths for interprocess communication, so even small additional overhead could significantly impact user experience on a device. On the other hand, video players only need to ensure that frames are rendered at the source framerate; if frames are rendered much faster than the rate at which they are displayed, additional overhead may be more acceptable.
Benchmarks, if available, can be extremely useful to evaluate the performance impact of a mitigation. If there are no benchmarks for a certain component, new ones should be created, for instance by calling impacted codec code to decode a media file. If this testing reveals unacceptable overhead, there are often a few options to address it:
Most of these improvements involve changes or contributions to the LLVM project. By working with upstream LLVM, these improvements have impact and benefit beyond Android. At the same time Android benefits from upstream improvements when others in the LLVM community make improvements as well.
There is more to consider when enabling a mitigation than its security benefit and performance cost, such as the cost of short-term deployment and long-term support.
One important issue is whether a mitigation can contain false positives. For example, if the Bounds Sanitizer produces an error, there is definitely an out-of-bounds access (although it might not be exploitable). But the Integer Overflow Sanitizer can produce false positives, as many integer overflows are harmless or even perfectly expected and correct.
It is thus important to consider the impact of a mitigation on the stability of the system. Whether a crash is due to a false positive or a legitimate security issue, it still disrupts the user experience and so is undesirable. This is another reason to carefully consider which components should have which mitigations, as crashes in some components are worse than others. If a mitigation causes a crash in a media codec, the user’s video playback will be stopped, but if netd crashes during an update, the phone could be bricked. For a mitigation like Bounds Sanitizer, where false positives are not an issue, we still need to perform extensive testing to ensure the device remains stable. Off-by-one errors, for example, may not crash during normal operation, but Bounds Sanitizer would abort execution and result in instability.
netd
Another consideration is whether it is possible to enumerate everything a mitigation might break. For example, it is not easy to contain the risk of the Integer Overflow Sanitizer without extensive testing, as it is difficult to determine which overflows are intentional/benign (and thus should be allowed) and which could lead to vulnerabilities.
We must consider not just issues caused by deploying mitigations but also how to support them long-term. This includes the developer time to integrate a mitigation into existing systems, enable and debug it, deploy it onto devices, and support it after launch. SELinux is a good example of this; it takes a significant amount of effort to write the policy for a new device, and even once enforcing mode is enabled, the policy must be supported for years as code changes and functionality is added or removed.
We try to make mitigations less disruptive and spread awareness of how they affect developers. This is done by making documentation available on source.android.com and by improving existing algorithms to reduce false positives. Making it easier to debug mitigations when something goes wrong reduces the developer maintenance burden that can accompany mitigations. For example, when developers found it difficult to identify UBSan errors, we enabled support for the UBSan Minimal Runtime by default in the Android build system. The minimal runtime itself was first upstreamed by others at Google specifically for this purpose. When the Integer Overflow Sanitizer crashes a program, that adds the following hint to the generic SIGABRT crash message:
Abort message: 'ubsan: sub-overflow'
Developers who see this message then know to enable diagnostics mode, which prints out details about the crash:
frameworks/native/services/surfaceflinger/SurfaceFlinger.cpp:2188:32: runtime error: unsigned integer overflow: 0 - 1 cannot be represented in type 'size_t' (aka 'unsigned long')
Similarly, upstream SELinux provides a tool called audit2allow that can be used to suggest rules to allow blocked behaviors:
adb logcat -d | audit2allow -p policy #============= rmt ============== allow rmt kmem_device:chr_file { read write };
A debugging tool does not need to be perfect to be helpful; audit2allow does not always suggest the correct options, but for developers without detailed knowledge of SELinux it provides a strong starting point.
With every Android release, our team works hard to balance security improvements that benefit the entire ecosystem with performance and stability, drawing heavily from the data that are available to us. We hope that this sheds some light on the particular challenges involved and the overall process that leads to mitigations introduced in each Android release.
Passwords help protect our online information, which is why it’s never been more important to keep them safe. But when we’re juggling dozens (if not hundreds!) of passwords across various websites—from shopping, to entertainment to personal finance—it feels like there’s always a new account to set up or manage. While it’s definitely a best practice to have a strong, unique password for each account, it can be really difficult to remember them all—that’s why we have a password manager in Chrome to back you up.
As you browse the web, on your phone, computer or tablet, Chrome can create, store and fill in your passwords with a single click. We'll warn you if your passwords have been compromised after logging in to sites, and you can always check for yourself in Chrome Settings. As we kick off the New Year, we’re excited to announce new updates that will give you even greater control over your passwords:
Easily fix weak passwords
We’ve all had moments where we’ve rushed to set up a new login, choosing a simple “name-of-your-pet” password to get set up quickly. However, weak passwords expose you to security risks and should be avoided. In Chrome 88, you can now complete a simple check to identify any weak passwords and take action easily.
To check your passwords, click on the key icon under your profile image, or type chrome://settings/passwords in your address bar.
Chrome can already prompt you to update your saved passwords when you log in to websites. However, you may want to update multiple usernames and passwords easily, in one convenient place. That’s why starting in Chrome 88, you can manage all of your passwords even faster and easier in Chrome Settings on desktop and iOS (Chrome’s Android app will be getting this feature soon, too).
Building on the 2020 improvements
These new updates come on top of many improvements from last year which have all contributed to your online safety and make browsing the web even easier:
The new features with Chrome 88 will be rolled out over the coming weeks, so take advantage of the new updates to keep your passwords secure. Stay tuned for more great password features throughout 2021.
import sys
def TestOneInput(data): # Our entry point
if data == b"bad":
raise RuntimeError("Badness!")
atheris.Setup(sys.argv, TestOneInput)
atheris.Fuzz()
Atheris is a native Python extension, and uses libFuzzer to provide its code coverage and input generation capabilities. The entry point passed to atheris.Setup() is wrapped in the C++ entry point that’s actually passed to libFuzzer. This wrapper will then be invoked by libFuzzer repeatedly, with its data proxied back to Python.
Python Code Coverage
Atheris is a native Python extension, and is typically compiled with libFuzzer linked in. When you initialize Atheris, it registers a tracer with CPython to collect information about Python code flow. This tracer can keep track of every line reached and every function executed.
We need to get this trace information to libFuzzer, which is responsible for generating code coverage information. There’s a problem, however: libFuzzer assumes that the amount of code is known at compile-time. The two primary code coverage mechanisms are __sanitizer_cov_pcs_init (which registers a set of program counters that might be visited) and __sanitizer_cov_8bit_counters_init (which registers an array of booleans that are to be incremented when a basic block is visited). Both of these need to know at initialization time how many program counters or basic blocks exist. But in Python, that isn’t possible, since code isn’t loaded until well after Python starts. We can’t even know it when we start the fuzzer: it’s possible to dynamically import code later, or even generate code on the fly.
Thankfully, libFuzzer supports fuzzing shared libraries loaded at runtime. Both __sanitizer_cov_pcs_init and __sanitizer_cov_8bit_counters_init are able to be safely called from a shared library in its constructor (called when the library is loaded). So, Atheris simulates loading shared libraries! When tracing is initialized, Atheris first calls those functions with an array of 8-bit counters and completely made-up program counters. Then, whenever a new Python line is reached, Atheris allocates a PC and 8-bit counter to that line; Atheris will always report that line the same way from then on. Once Atheris runs out of PCs and 8-bit counters, it simply loads a new “shared library” by calling those functions again. Of course, exponential growth is used to ensure that the number of shared libraries doesn’t become excessive.
What's Special about Python 3.8+?
In the README, we advise users to use Python 3.8+ where possible. This is because Python 3.8 added a new feature: opcode tracing. Not only can we monitor when every line is visited and every function is called, but we can actually monitor every operation that Python performs, and what arguments it uses. This allows Atheris to find its way through if statements much better.
When a COMPARE_OP opcode is encountered, indicating a boolean comparison between two values, Atheris inspects the types of the values. If the values are bytes or Unicode, Atheris is able to report the comparison to libFuzzer via __sanitizer_weak_hook_memcmp. For integer comparison, Atheris uses the appropriate function to report integer comparisons, such as __sanitizer_cov_trace_cmp8.
In recent Python versions, a Unicode string is actually represented as an array of 1-byte, 2-byte, or 4-byte characters, based on the size of the largest character in the string. The obvious solution for coverage is to:
Starting today, the Chrome Vulnerability Rewards Program is offering a new bonus for reports which demonstrate exploitability in V8, Chrome’s JavaScript engine. We have historically had many great V8 bugs reported (thank you to all of our reporters!) but we'd like to know more about the exploitability of different V8 bug classes, and what mechanisms are effective to go from an initial bug to a full exploit. That's why we're offering this additional reward for bugs that show how a V8 vulnerability could be used as part of a real world attack.
In the past, exploits had to be fully functional to be rewarded at our highest tier, high-quality report with functional exploit. Demonstration of how a bug might be exploited is one factor that the panel may use to determine that a report is high-quality, our second highest tier, but we want to encourage more of this type of analysis. This information is very useful for us when planning future mitigations, making release decisions, and fixing bugs faster. We also know it requires a bit more effort for our reporters, and that effort should be rewarded. For the time being this only applies to V8 bugs, but we’re curious to see what our reporters come up with!
The full details are available on the Chrome VRP rules page. At a high-level, we’re offering increased reward amounts, up to double, for qualifying V8 bugs.
The following table shows the updated reward amounts for reports qualifying for this new bonus. These new, higher values replace the normal reward. If a bug in V8 doesn’t fit into one of these categories, it may still qualify for an increased reward at the panel’s discretion.
So what does a report need to do to demonstrate that a bug is likely exploitable? Any V8 bug report which would have previously been rewarded at the high-quality report with functional exploit level will likely qualify with no additional effort from the reporter. By definition, these demonstrate that the issue was exploitable. V8 reports at the high-quality level may also qualify if they include evidence that the bug is exploitable as part of their analysis. See the rules page for more information about our reward levels.
The following are some examples of how a report could demonstrate that exploitation is likely, but any analysis or proof of concept will be considered by the panel:
For example reports, see issues 914736 and 1076708.
We’d like to thank all of our VRP reporters for helping us keep Chrome users safe! We look forward to seeing what you find.
-The Chrome Vulnerability Rewards Panel
One year of OpenTitan and Ibex growth on GitHub: the total number of commits grew from 2,500 to over 6,100.
One year of growth in Design Verification: from 30,000 to over 65,000 lines of testing source code. Each color represents design verification for an individual IP block.
In the United States and other countries a Driver's License is not only used to convey driving privileges, it is also commonly used to prove identity or personal details.
Presenting a Driving License is simple, right? You hand over the card to the individual wishing to confirm your identity (the so-called “Relying Party” or “Verifier”); they check the security features of the plastic card (hologram, micro-printing, etc.) to ensure it’s not counterfeit; they check that it’s really your license, making sure you look like the portrait image printed on the card; and they read the data they’re interested in, typically your age, legal name, address etc. Finally, the verifier needs to hand back the plastic card.
Most people are so familiar with this process that they don’t think twice about it, or consider the privacy implications. In the following we’ll discuss how the new and soon-to-be-released ISO 18013-5 standard will improve on nearly every aspect of the process, and what it has to do with Android.
The ISO 18013-5 “Mobile driving licence (mDL) application” standard has been written by a diverse group of people representing driving license issuers (e.g. state governments in the US), relying parties (federal and state governments, including law enforcement), academia, industry (including Google), and many others. This ISO standard allows for construction of Mobile Driving License (mDL) applications which users can carry in their phone and can use instead of the plastic card.
Instead of handing over your plastic card, you open the mDL application on your phone and press a button to share your mDL. The Verifier (aka “Relying Party”) has their own device with an mDL reader application and they either scan a QR code shown in your mDL app or do an NFC tap. The QR code (or NFC tap) conveys an ephemeral cryptographic public key and hardware address the mDL reader can connect to.
Once the mDL reader obtains the cryptographic key it creates its own ephemeral keypair and establishes an encrypted and authenticated, secure wireless channel (BLE, Wifi Aware or NFC)). The mDL reader uses this secure channel to request data, such as the portrait image or what kinds of vehicles you're allowed to drive, and can also be used to ask more abstract questions such as “is the holder older than 18?”
Crucially, the mDL application can ask the user to approve which data to release and may require the user to authenticate with fingerprint or face — none of which a passive plastic card could ever do.
With this explanation in mind, let’s see how presenting an mDL application compares with presenting a plastic-card driving license:
These are some of the reasons why we think mDL is a big win for end users in terms of privacy.
One commonality between plastic-card driving licences and the mDL is how the relying party verifies that the person presenting the license is the authorized holder. In both cases, the verifier manually compares the appearance of the individual against a portrait photo, either printed on the plastic or transmitted electronically and research has shown that it’s hard for individuals to match strangers to portrait images.
The initial version of ISO 18013-5 won’t improve on this but the ISO committee working on the standard is already investigating ways to utilize on-device biometrics sensors to perform this match in a secure and privacy-protecting way. The hope is that improved fidelity in the process helps reduce unauthorized use of identity documents.
Through facilities such as hardware-based Keystore, Android already offers excellent support for security and privacy-sensitive applications and in fact it’s already possible to implement the ISO 18013-5 standard on Android without further platform changes. Many organizations participating in the ISO committee have already implemented 18013-5 Android apps.
That said, with purpose-built support in the operating system it is possible to provide better security and privacy properties. Android 11 includes the Identity Credential APIs at the Framework level along with a Hardware Abstraction Layer interface which can be implemented by Android OEMs to enable identity credential support in Secure Hardware. Using the Identity Credential API, the Trusted Computing Base of mDL applications does not include the application or even Android itself. This will be particularly important for future versions where the verifier must trust the device to identify and authenticate the user, for example through fingerprint or face matching on the holder's own device. It’s likely such a solution will require certified hardware and/or software and certification is not practical if the TCB includes the hundreds of millions of lines of code in Android and the Linux kernel.
One advantage of plastic cards is that they don't require power or network communication to be useful. Putting all your licenses on your phone could seem inconvenient in cases where your device is low on battery, or does not have enough battery life to start. The Android Identity Credential HAL therefore provides support for a mode called Direct Access, where the license is still available through an NFC tap even when the phone's battery is too low to boot it up. Device makers can implement this mode, but it will require hardware support that will take several years to roll out.
For devices without the Identity Credential HAL, we have an Android Jetpack which implements the same API and works on nearly every Android device in the world (API level 24 or later). If the device has hardware-backed Identity Credential support then this Jetpack simply forwards calls to the platform API. Otherwise, an Android Keystore-backed implementation will be used. While the Android Keystore-backed implementation does not provide the same level of security and privacy, it is perfectly adequate for both holders and issuers in cases where all data is issuer-signed. Because of this, the Jetpack is the preferred way to use the Identity Credential APIs. We also made available sample open-source mDL and mDL Reader applications using the Identity Credential APIs.
Android now includes APIs for managing and presenting with identity documents in a more secure and privacy-focused way than was previously possible. These can be used to implement ISO 18013-5 mDLs but the APIs are generic enough to be usable for other kinds of electronic documents, from school ID or bonus program club cards to passports.
Additionally, the Android Security and Privacy team actively participates in the ISO committees where these standards are written and also works with civil liberties groups to ensure it has a positive impact on our end users.
Open source software is the foundation of many modern software products. Over the years, developers increasingly have relied on reusable open source components for their applications. It is paramount that these open source components are secure and reliable, as weaknesses impact those that build upon it.
Google cares deeply about the security of the open source ecosystem and recently launched the Open Source Security Foundation with other industry partners. Fuzzing is an automated testing technique to find bugs by feeding unexpected inputs to a target program. At Google, we leverage fuzzing at scale to find tens of thousands of security vulnerabilities and stability bugs. This summer, as part of Google’s OSS internship initiative, we hosted 50 interns to improve the state of fuzz testing in the open source ecosystem.
The fuzzing interns worked towards integrating new projects and improving existing ones in OSS-Fuzz, our continuous fuzzing service for the open source community (which has 350+ projects, 22,700 bugs, 89% fixed). Several widely used open source libraries including but not limited to nginx, postgresql, usrsctp, and openexr, now have continuous fuzzing coverage as a result of these efforts.
Another group of interns focused on improving the security of the Linux kernel. syzkaller, a kernel fuzzing tool from Google, has been instrumental in finding kernel vulnerabilities in various operating systems. The interns were tasked with improving the fuzzing coverage by adding new descriptions to syzkaller like ip tunnels, io_uring, and bpf_lsm for example, refining the interface description language, and advancing kernel fault injection capabilities.
Some interns chose to write fuzzers for Android and Chrome, which are open source projects that billions of internet users rely on. For Android, the interns contributed several new fuzzers for uncovered areas - network protocols such as pppd and dns, audio codecs like monoblend, g722, and android framework. On the Chrome side, interns improved existing blackbox fuzzers, particularly in the areas: DOM, IPC, media, extensions, and added new libprotobuf-based fuzzers for Mojo.
Our last set of interns researched quite a few under-explored areas of fuzzing, some of which were fuzzer benchmarking, ML based fuzzing, differential fuzzing, bazel rules for build simplification and made useful contributions.
Over the course of the internship, our interns have reported over 150 security vulnerabilities and 750 functional bugs. Given the overall success of these efforts, we plan to continue hosting fuzzing internships every year to help secure the open source ecosystem and teach incoming open source contributors about the importance of fuzzing. For more information on the Google internship program and other student opportunities, check out careers.google.com/students. We encourage you to apply.
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