Tag Archives: Security

Prevent Generative AI Data Leaks with Chrome Enterprise DLP

Generative AI has emerged as a powerful and popular tool to automate content creation and simple tasks. From customized content creation to source code generation, it can increase both our productivity and creative potential.

Businesses want to leverage the power of LLMs, like Gemini, but many may have security concerns and want more control around how employees make sure of these new tools. For example, companies may want to ensure that various forms of sensitive data, such as Personally Identifiable Information (PII), financial records and internal intellectual property, is not to be shared publicly on Generative AI platforms. Security leaders face the challenge of finding the right balance — enabling employees to leverage AI to boost efficiency, while also safeguarding corporate data.

In this blog post, we'll explore reporting and enforcement policies that enterprise security teams can implement within Chrome Enterprise Premium for data loss prevention (DLP).

1. View login events* to understand usage of Generative AI services within the organization. With Chrome Enterprise's Reporting Connector, security and IT teams can see when a user successfully signs into a specific domain, including Generative AI websites. Security Operations teams can further leverage this telemetry to detect anomalies and threats by streaming the data into Chronicle or other third-party SIEMs at no additional cost.

2. Enable URL Filtering to warn users about sensitive data policies and let them decide whether or not they want to navigate to the URL, or to block users from navigating to certain groups of sites altogether.


For example, with Chrome Enterprise URL Filtering, IT admins can create rules that warn developers not to submit source code to specific Generative AI apps or tools, or block them.

3. Warn, block or monitor sensitive data actions within Generative AI websites with dynamic content-based rules for actions like paste, file uploads/downloads, and print. Chrome Enterprise DLP rules give IT admins granular control over browser activities, such as entering financial information in Gen AI websites. Admins can customize DLP rules to restrict the type and amount of data entered into these websites from managed browsers.

For most organizations, safely leveraging Generative AI requires a certain amount of control. As enterprises work through their policies and processes involving GenAI, Chrome Enterprise Premium empowers them to strike the balance that works best. Hear directly from security leaders at Snap on their use of DLP for Gen AI in this recording here.

Learn more about how Chrome Enterprise can secure businesses just like yours here.

*Available at no additional cost in Chrome Enterprise Core

How we built the new Find My Device network with user security and privacy in mind

Keeping people safe and their data secure and private is a top priority for Android. That is why we took our time when designing the new Find My Device, which uses a crowdsourced device-locating network to help you find your lost or misplaced devices and belongings quickly – even when they’re offline. We gave careful consideration to the potential user security and privacy challenges that come with device finding services.

During development, it was important for us to ensure the new Find My Device was secure by default and private by design. To build a private, crowdsourced device-locating network, we first conducted user research and gathered feedback from privacy and advocacy groups. Next, we developed multi-layered protections across three main areas: data safeguards, safety-first protections, and user controls. This approach provides defense-in-depth for Find My Device users.

How location crowdsourcing works on the Find My Device network

The Find My Device network locates devices by harnessing the Bluetooth proximity of surrounding Android devices. Imagine you drop your keys at a cafe. The keys themselves have no location capabilities, but they may have a Bluetooth tag attached. Nearby Android devices participating in the Find My Device network report the location of the Bluetooth tag. When the owner realizes they have lost their keys and logs into the Find My Device mobile app, they will be able to see the aggregated location contributed by nearby Android devices and locate their keys.

Find My Device network protections


Let’s dive into key details of the multi-layered protections for the Find My Device network:

  • Data Safeguards: We’ve implemented protections that help ensure the privacy of everyone participating in the network and the crowdsourced location data that powers it.
    • Location data is end-to-end encrypted. When Android devices participating in the network report the location of a Bluetooth tag, the location is end-to-end encrypted using a key that is only accessible to the Bluetooth tag owner and anyone the owner has shared the tag with in the Find My Device app. Only the Bluetooth tag owner (and those they’ve chosen to share access with) can decrypt and view the tag’s location. With end-to-end encrypted location data, Google cannot decrypt, see, or otherwise use the location data.
    • Private, crowdsourced location reports. These end-to-end encrypted locations are contributed to the Find My Device network in a manner that does not allow Google to identify the owners of the nearby Android devices that provided the location data. And when the Find My Device network shows the location and timestamp to the Bluetooth tag’s owner to help them find their belongings, no other information about the nearby Android devices that contributed the data is included.
    • Minimizing network data. End-to-end encrypted location data is minimally buffered and frequently overwritten. In addition, if the network can help find a Bluetooth tag using the owner’s nearby devices (e.g., if their own phone detects the tag), the network will discard crowdsourced reports for the tag.
  • Safety-first Protections: The Find My Device network protects against risks such as use of an unknown Bluetooth tag to stalk or identify another user, including:
    • Aggregation by default. This is a first-of-its-kind safety protection that makes unwanted tracking to a private location, like your home, more difficult. By default, the Find My Device network requires multiple nearby Android devices to detect a tag before reporting its location to the tag's owner. Our research found that the Find My Device network is most valuable in public settings like cafes and airports, where there are likely many devices nearby. By implementing aggregation before showing a tag’s location to its owner, the network can take advantage of its biggest strength – over a billion Android devices that can participate. This helps tag owners find their lost devices in these busier locations while prioritizing safety from unwanted tracking near private locations. In less busy areas, last known location and Nest finding are reliable ways to locate items.
    • At home protection. If a user has chosen to save their home address in their Google Account, their Android device will also ensure that it does not contribute crowdsourced location reports to the Find My Device network when it is near the user’s home. This provides additional protection on top of aggregation by default against unwanted tracking near private locations.
    • Rate limiting and throttling. The Find My Device network limits the number of times that a nearby Android device can contribute a location report for a particular Bluetooth tag. The network also throttles how frequently the owner of a Bluetooth tag can request an updated location for the tag. We've found that lost items are typically left behind in stationary spots. For example, you lose your keys at the cafe, and they stay at the table where you had your morning coffee. Meanwhile, a malicious user is often trying to engage in real-time tracking of a person. By applying rate limiting and throttling to reduce how often the location of a device is updated, the network continues to be helpful for finding items, like your lost checked baggage on a trip, while helping mitigate the risk of real-time tracking.
    • Unknown tracker alerts. The Find My Device network is also compliant with the integration version of the joint industry standard for unwanted tracking. Being compliant with the integration version of the standard means that both Android and iOS users will receive unknown tracker alerts if the on-device algorithm detects that someone may be using a Find My Device network-compatible tag to track them without their knowledge, proactively alerting the user through a notification on their phone.
  • User Controls: Android users always have full control over which of their devices participate in the Find My Device network and how those devices participate. Users can either stick with the default and contribute to aggregated location reporting, opt into contributing non-aggregated locations, or turn the network off altogether. Find My Device also provides the ability to secure or erase data from a lost device.

In addition to careful security architectural design, the new Find My Device network has undergone internal Android red team testing. The Find My Device network has also been added to the Android security vulnerability rewards program to take advantage of Android’s global ecosystem of security researchers. We’re also engaging with select researchers through our private grant program to encourage more targeted research.

Prioritizing user safety on Find My Device

Together, these multi-layered user protections help mitigate potential risks to user privacy and safety while allowing users to effectively locate and recover lost devices.

As bad actors continue to look for new ways to exploit users, our work to help keep users safe on Android is never over. We have an unwavering commitment to continue to improve user protections on Find My Device and prioritize user safety.


For more information about Find My Device on Android, please visit our help center. You can read the Find My Device Network Accessory specification here.

OSV and helping developers fix known vulnerabilities

In 2021, we launched the OSV project with a goal of enabling easy management of known vulnerabilities in open source software dependencies. To achieve this, we started by building an open source, comprehensive database (https://osv.dev) that accurately describes all known OSS vulnerabilities in the easy-to-use OpenSSF OSV Schema.

Over time, we worked with numerous open source communities to adopt the OSV Schema (totalling over 24 ecosystems), and introduced open source tools like our API and OSV-Scanner to directly make this database useful to developers.

The OSV project takes a very developer-focused approach to vulnerability management, as we realize that day-to-day developers are often the ones who bear the burden of managing dependency updates and triaging vulnerabilities in their dependencies.

Today the OSV team is excited to announce some exciting updates to the work we’ve been doing, and share how the OSV project as a whole helps developers with vulnerability management today.


Announcing guided remediation

Developers are often faced with an overwhelming number of vulnerabilities reported against their dependencies. To tackle this, we’re announcing a tool as part of OSV-Scanner to enable developers to both interactively and automatically prioritize and fix the vulnerabilities that matter in an easy way.

The basic usage of the tool provides a simple command for developers to run which will automatically fix as many vulnerabilities as possible by upgrading their project’s dependencies.

For developers who need or want finer control over vulnerability remediation, the tool also provides the more advanced interactive mode. In the interactive mode, developers can preview and make informed decisions on which packages to upgrade or which vulnerabilities they want to prioritize based on metrics such as vulnerability severity, dependency depth, or dependency type.

Filtering by all these advanced metrics are also available via CLI flags for running the tool non-interactively, which enables integration of guided remediation into automated workflows. For example, developers can connect the tool with their CI/test pipelines to determine the set of non-breaking dependency upgrades.

Currently, the guided remediation tool supports npm package.json and package-lock.json dependencies, but we’ll be adding support for more ecosystems in the future.

Check out our detailed documentation for more information or if you would like to try it out for yourself!


OSV-Scanner GitHub action

We’ve also recently launched the OSV-Scanner GitHub action, which provides an easy way for developers to integrate vulnerability scanning using OSV.dev into their CI/CD pipelines. This is currently being used by Tensorflow and Flutter to provide continuous scanning of their dependencies.

Our GitHub Action can be configured to do the following:

  • Regular vulnerability scan workflow. A common use case is to set a schedule to regularly scan the repository, with the workflow failing if a new vulnerability is found. Another use case can be to block release workflows if a vulnerability is found.
  • Trigger a differential vulnerability scan to run when a pull request is opened. This workflow can determine if your changes introduce new vulnerabilities and can be configured to block pull requests when the action fails. Enabling just this feature can allow you to stop new vulnerabilities from being introduced, while not breaking your existing workflows.

Head over to our documentation to see a quick and easy guide on how to get started integrating the OSV-Scanner action into your GitHub repository.


Other OSV features

Guided remediation and the GitHub actions support form is one piece of enabling our goal of making vulnerability management easier.

OSV also provides a broad suite of features:


What’s next?

We still have a lot more exciting work planned! A remaining challenge for dealing with known vulnerabilities in dependencies is remediation and dealing with false positives. Much of our work is focused on improving data quality and providing accurate and actionable results that lead to easy remediation.

These include:

  • Iterating on guided remediation: by addressing user feedback and adding support for additional ecosystems.
  • Improving container scanning. OSV-Scanner has so far focused on source repository scanning. One important gap we aim to fill is to provide better support for container scanning, in a way that provides actionable and useful remediation guidance, while minimizing false positives.
  • Continue to improve matching and data quality. A continuing focus for OSV-Scanner is making sure that our scanning is comprehensive and accurate. Accuracy is especially important for us, as one of our core goals is to minimize false positives and vulnerability noise for developers at the receiving end of the scanners through things such as reachability analysis.

Interested in using OSV in your project? Check out our OSV-Scanner and OSV.dev documentation for how to get started. Please share any feedback or bugs you encounter via our GitHub issue tracker.

By Michael Kedar, Rex Pan, and Oliver Chang – Google Open Source Security Team

Battling Impersonation Scams: Monzo’s Innovative Approach

Posted by Todd Burner – Developer Relations Engineer

Cybercriminals continue to invest in advanced financial fraud scams, costing consumers more than $1 trillion in losses. According to the 2023 Global State of Scams Report by the Global Anti-Scam Alliance, 78 percent of mobile users surveyed experienced at least one scam in the last year. Of those surveyed, 45 percent said they’re experiencing more scams in the last 12 months.

ALT TEXT

The Global Scam Report also found that phone calls are the top method to initiate a scam. Scammers frequently employ social engineering tactics to deceive mobile users.

The key place these scammers want individuals to take action are in the tools that give access to their money. This means financial services are frequently targeted. As cybercriminals push forward with more scams, and their reach extends globally, it’s important to innovate in the response.

One such innovator is Monzo, who have been able to tackle scam calls through a unique impersonation detection feature in their app.

Monzo’s Innovative Approach

Founded in 2015, Monzo is the largest digital bank in the UK with presence in the US as well. Their mission is to make money work for everyone with an ambition to become the one app customers turn to to manage their entire financial lives.

Monzo logo

Impersonation fraud is an issue that the entire industry is grappling with and Monzo decided to take action and introduce an industry-first tool. An impersonation scam is a very common social engineering tactic when a criminal pretends to be someone else so they can encourage you to send them money. These scams often involve using urgent pretenses that involve a risk to a user’s finances or an opportunity for quick wealth. With this pressure, fraudsters convince users to disable security safeguards and ignore proactive warnings for potential malware, scams, and phishing.

Call Status Feature

Android offers multiple layers of spam and phishing protection for users including call ID and spam protection in the Phone by Google app. Monzo’s team wanted to enhance that protection by leveraging their in-house telephone systems. By integrating with their mobile application infrastructure they could help their customers confirm in real time when they’re actually talking to a member of Monzo’s customer support team in a privacy preserving way.

If someone calls a Monzo customer stating they are from the bank, their users can go into the app to verify this. In the Monzo app’s Privacy & Security section, users can see the ‘Monzo Call Status’, letting them know if there is an active call ongoing with an actual Monzo team member.

“We’ve built this industry-first feature using our world-class tech to provide an additional layer of comfort and security. Our hope is that this could stop instances of impersonation scams for Monzo customers from happening in the first place and impacting customers.” 

- Priyesh Patel, Senior Staff Engineer, Monzo’s Security team

Keeping Customers Informed

If a user is not talking to a member of Monzo’s customer support team they will see that as well as some helpful information. If the ‘Monzo call status’ is showing that you are not speaking to Monzo, the call status feature tells you to hang up right away and report it to their team. Their customers can start a scam report directly from the call status feature in the app.

screen grab of Monzo call status alerting the customer that the call the customer is receiving is not coming from Monzo. The customer is being advised to end the call

If a genuine call is ongoing the customer will see the information.

screen grab of Monzo call status confirming to the customer that the call the customer is receiving is coming from Monzo.

How does it work?

Monzo has integrated a few systems together to help inform their customers. A cross functional team was put together to build a solution.

Monzo’s in-house technology stack meant that the systems that power their app and customer service phone calls can easily communicate with one another. This allowed them to link the two and share details of customer service calls with their app, accurately and in real-time.

The team then worked to identify edge cases, like when the user is offline. In this situation Monzo recommends that customers don’t speak to anyone claiming they’re from Monzo until you’re connected to the internet again and can check the call status within the app.

screen grab of Monzo call status displaying warning while the customer is offline letting the customer know the app is unable to verify whether or not the call is coming from Monzo, so it is safer not to answer.

Results and Next Steps

The feature has proven highly effective in safeguarding customers, and received universal praise from industry experts and consumer champions.

“Since we launched Call Status, we receive an average of around 700 reports of suspected fraud from our customers through the feature per month. Now that it’s live and helping protect customers, we’re always looking for ways to improve Call Status - like making it more visible and easier to find if you’re on a call and you want to quickly check that who you’re speaking to is who they say they are.” 

- Priyesh Patel, Senior Staff Engineer, Monzo’s Security team

Final Advice

Monzo continues to invest and innovate in fraud prevention. The call status feature brings together both technological innovation and customer education to achieve its success, and gives their customers a way to catch scammers in action.

A layered security approach is a great way to protect users. Android and Google Play provide layers like app sandboxing, Google Play Protect, and privacy preserving permissions, and Monzo has built an additional one in a privacy-preserving way.

To learn more about Android and Play’s protections and to further protect your app check out these resources:

Google Public DNS’s approach to fight against cache poisoning attacks



The Domain Name System (DNS) is a fundamental protocol used on the Internet to translate human-readable domain names (e.g., www.example.com) into numeric IP addresses (e.g., 192.0.2.1) so that devices and servers can find and communicate with each other. When a user enters a domain name in their browser, the DNS resolver (e.g. Google Public DNS) locates the authoritative DNS nameservers for the requested name, and queries one or more of them to obtain the IP address(es) to return to the browser.

When DNS was launched in the early 1980s as a trusted, content-neutral infrastructure, security was not yet a pressing concern, however, as the Internet grew DNS became vulnerable to various attacks. In this post, we will look at DNS cache poisoning attacks and how Google Public DNS addresses the risks associated with them.

DNS Cache Poisoning Attacks

DNS lookups in most applications are forwarded to a caching resolver (which could be local or an open resolver like. Google Public DNS). The path from a client to the resolver is usually on a local network or can be protected using encrypted transports like DoH, DoT. The resolver queries authoritative DNS servers to obtain answers for user queries. This communication primarily occurs over UDP, an insecure connectionless protocol, in which messages can be easily spoofed including the source IP address. The content of DNS queries may be sufficiently predictable that even an off-path attacker can, with enough effort, forge responses that appear to be from the queried authoritative server. This response will be cached if it matches the necessary fields and arrives before the authentic response. This type of attack is called a cache poisoning attack, which can cause great harm once successful. According to RFC 5452, the probability of success is very high without protection. Forged DNS responses can lead to denial of service, or may even compromise application security. For an excellent introduction to cache poisoning attacks, please see “An Illustrated Guide to the Kaminsky DNS Vulnerability”.

Cache poisoning mitigations in Google Public DNS

Improving DNS security has been a goal of Google Public DNS since our launch in 2009. We take a multi-pronged approach to protect users against DNS cache-poisoning attacks. There is no silver bullet or countermeasure that entirely solves the problem, but in combination they make successful attacks substantially more difficult.


RFC 5452 And DNS Cookies

We have implemented the basic countermeasures outlined in RFC 5452 namely randomizing query source ports and query IDs. But these measures alone are not sufficient (see page 8 of our OARC 38 presentation).

We have therefore also implemented support for RFC 7873 (DNS Cookies) which can make spoofing impractical if it’s supported by the authoritative server. Measurements indicate that the DNS Cookies do not provide sufficient coverage, even though around 40% of nameservers by IP support DNS Cookies, these account for less than 10% of overall query volume. In addition, many non-compliant nameservers return incorrect or ambiguous responses for queries with DNS Cookies, which creates further deployment obstacles. For now, we’ve enabled DNS Cookies through manual configuration, primarily for selected TLD zones.

Case Randomization (0x20)

The query name case randomization mechanism, originally proposed in a March 2008 draft “Use of Bit 0x20 in DNS Labels to Improve Transaction Identity”, however, is highly effective, because all but a small minority of nameservers are compatible with query name case randomization. We have been performing case randomization of query names since 2009 to a small set of chosen nameservers that handle only a minority of our query volume. 

In 2022 we started work on enabling case randomization by default, which when used, the query name in the question section is randomized and the DNS server’s response is expected to match the case-randomized query name exactly in the request. For example, if “ExaMplE.CoM” is the name sent in the request, the name in the question section of the response must also be “ExaMplE.CoM” rather than, e.g., “example.com.” Responses that fail to preserve the case of the query name may be dropped as potential cache poisoning attacks (and retried over TCP).

We are happy to announce that we’ve already enabled and deployed this feature globally by default. It covers over 90% of our UDP traffic to nameservers, significantly reducing the risk of cache poisoning attacks.

Meanwhile, we maintain an exception list and implement fallback mechanisms to prevent potential issues with non-conformant nameservers. However we strongly recommend that nameserver implementations preserve the query case in the response.

DNS-over-TLS

In addition to case randomization, we’ve deployed DNS-over-TLS to authoritative nameservers (ADoT), following procedures described in RFC 9539 (Unilateral Opportunistic Deployment of Encrypted Recursive-to-Authoritative DNS). Real world measurements show that ADoT has a higher success rate and comparable latency to UDP. And ADoT is in use for around 6% of egress traffic. At the cost of some CPU and memory, we get both security and privacy for nameserver queries without DNS compliance issues.

Summary

Google Public DNS takes security of our users seriously. Through multiple countermeasures to cache poisoning attacks, we aim to provide a more secure and reliable DNS resolution service, enhancing the overall Internet experience for users worldwide. With the measures described above we are able to provide protection against passive attacks for over 90% of authoritative queries.


To enhance DNS security, we recommend that DNS server operators support one or more of the  security mechanisms described here. We are also working with the DNS community to improve DNS security. Please see our presentations at DNS-OARC 38 and 40 for more technical details.

Address Sanitizer for Bare-metal Firmware

With steady improvements to Android userspace and kernel security, we have noticed an increasing interest from security researchers directed towards lower level firmware. This area has traditionally received less scrutiny, but is critical to device security. We have previously discussed how we have been prioritizing firmware security, and how to apply mitigations in a firmware environment to mitigate unknown vulnerabilities.

In this post we will show how the Kernel Address Sanitizer (KASan) can be used to proactively discover vulnerabilities earlier in the development lifecycle. Despite the narrow application implied by its name, KASan is applicable to a wide-range of firmware targets. Using KASan enabled builds during testing and/or fuzzing can help catch memory corruption vulnerabilities and stability issues before they land on user devices. We've already used KASan in some firmware targets to proactively find and fix 40+ memory safety bugs and vulnerabilities, including some of critical severity.

Along with this blog post we are releasing a small project which demonstrates an implementation of KASan for bare-metal targets leveraging the QEMU system emulator. Readers can refer to this implementation for technical details while following the blog post.

Address Sanitizer (ASan) overview

Address sanitizer is a compiler-based instrumentation tool used to identify invalid memory access operations during runtime. It is capable of detecting the following classes of temporal and spatial memory safety bugs:

  • out-of-bounds memory access
  • use-after-free
  • double/invalid free
  • use-after-return

ASan relies on the compiler to instrument code with dynamic checks for virtual addresses used in load/store operations. A separate runtime library defines the instrumentation hooks for the heap memory and error reporting. For most user-space targets (such as aarch64-linux-android) ASan can be enabled as simply as using the -fsanitize=address compiler option for Clang due to existing support of this target both in the toolchain and in the libclang_rt runtime.

However, the situation is rather different for bare-metal code which is frequently built with the none system targets, such as arm-none-eabi. Unlike traditional user-space programs, bare-metal code running inside an embedded system often doesn’t have a common runtime implementation. As such, LLVM can’t provide a default runtime for these environments.

To provide custom implementations for the necessary runtime routines, the Clang toolchain exposes an interface for address sanitization through the -fsanitize=kernel-address compiler option. The KASan runtime routines implemented in the Linux kernel serve as a great example of how to define a KASan runtime for targets which aren’t supported by default with -fsanitize=address. We'll demonstrate how to use the version of address sanitizer originally built for the kernel on other bare-metal targets.

KASan 101

Let’s take a look at the KASan major building blocks from a high-level perspective (a thorough explanation of how ASan works under-the-hood is provided in this whitepaper).

The main idea behind KASan is that every memory access operation, such as load/store instructions and memory copy functions (for example, memmove and memcpy), are instrumented with code which performs verification of the destination/source memory regions. KASan only allows the memory access operations which use valid memory regions. When KASan detects memory access to a memory region which is invalid (that is, the memory has been already freed or access is out-of-bounds) then it reports this violation to the system.

The state of memory regions covered by KASan is maintained in a dedicated area called shadow memory. Every byte in the shadow memory corresponds to a single fixed-size memory region covered by KASan (typically 8-bytes) and encodes its state: whether the corresponding memory region has been allocated or freed and how many bytes in the memory region are accessible.

Therefore, to enable KASan for a bare-metal target we would need to implement the instrumentation routines which verify validity of memory regions in memory access operations and report KASan violations to the system. In addition we would also need to implement shadow memory management to track the state of memory regions which we want to be covered with KASan.

Enabling KASan for bare-metal firmware

KASan shadow memory

The very first step in enabling KASan for firmware is to reserve a sufficient amount of DRAM for shadow memory. This is a memory region where each byte is used by KASan to track the state of an 8-byte region. This means accommodating the shadow memory requires a dedicated memory region equal to 1/8th the size of the address space covered by KASan.

KASan maps every 8-byte aligned address from the DRAM region into the shadow memory using the following formula:

shadow_address = (target_address >> 3 ) + shadow_memory_base where target_address is the address of a 8-byte memory region which we want to cover with KASan and shadow_memory_base is the base address of the shadow memory area.

Implement a KASan runtime

Once we have the shadow memory tracking the state of every single 8-byte memory region of DRAM we need to implement the necessary runtime routines which KASan instrumentation depends on. For reference, a comprehensive list of runtime routines needed for KASan can be found in the linux/mm/kasan/kasan.h Linux kernel header. However, it might not be necessary to implement all of them and in the following text we focus on the ones which were needed to enable KASan for our target firmware as an example.

Memory access check

The routines __asan_loadXX_noabort, __asan_storeXX_noabort perform verification of memory access at runtime. The symbol XX denotes size of memory access and goes as a power of 2 starting from 1 up to 16. The toolchain instruments every memory load and store operations with these functions so that they are invoked before the memory access operation happens. These routines take as input a pointer to the target memory region to check it against the shadow memory.

If the region state provided by shadow memory doesn’t reveal a violation, then these functions return to the caller. But if any violations (for example, the memory region is accessed after it has been deallocated or there is an out-of-bounds access) are revealed, then these functions report the KASan violation by:

  • Generating a call-stack.
  • Capturing context around the memory regions.
  • Logging the error.
  • Aborting/crashing the system (optional)

Shadow memory management

The routine __asan_set_shadow_YY is used to poison shadow memory for a given address. This routine is used by the toolchain instrumentation to update the state of memory regions. For example, the KASan runtime would use this function to mark memory for local variables on the stack as accessible/poisoned in the epilogue/prologue of the function respectively.

This routine takes as input a target memory address and sets the corresponding byte in shadow memory to the value of YY. Here is an example of some YY values for shadow memory to encode state of 8-byte memory regions:

  • 0x00 -- the entire 8-byte region is accessible
  • 0x01-0x07 -- only the first bytes in the memory region are accessible
  • 0xf1 -- not accessible: stack left red zone
  • 0xf2 -- not accessible: stack mid red zone
  • 0xf3 -- not accessible: stack right red zone
  • 0xfa -- not accessible: globals red zone
  • 0xff -- not accessible

Covering global variables

The routines __asan_register_globals, __asan_unregister_globals are used to poison/unpoison memory for global variables. The KASan runtime calls these functions while processing global constructors/destructors. For instance, the routine __asan_register_globals is invoked for every global variable. It takes as an argument a pointer to a data structure which describes the target global variable: the structure provides the starting address of the variable, its size not including the red zone and size of the global variable with the red zone.

The red zone is extra padding the compiler inserts after the variable to increase the likelihood of detecting an out-of-bounds memory access. Red zones ensure there is extra space between adjacent global variables. It is the responsibility of __asan_register_globals routine to mark the corresponding shadow memory as accessible for the variable and as poisoned for the red zone.

As the readers could infer from its name, the routine __asan_unregister_globals is invoked while processing global destructors and is intended to poison shadow memory for the target global variable. As a result, any memory access to such a global will cause a KASan violation.

Memory copy functions

The KASan compiler instrumentation routines __asan_loadXX_noabort, __asan_storeXX_noabort discussed above are used to verify individual memory load and store operations such as, reading or writing an array element or dereferencing a pointer. However, these routines don't cover memory access in bulk-memory copy functions such as memcpy, memmove, and memset. In many cases these functions are provided by the runtime library or implemented in assembly to optimize for performance.

Therefore, in order to be able to catch invalid memory access in these functions, we would need to provide sanitized versions of memcpy, memmove, and memset functions in our KASan implementation which would verify memory buffers to be valid memory regions.

Avoiding false positives for noreturn functions

Another routine required by KASan is __asan_handle_no_return, to perform cleanup before a noreturn function and avoid false positives on the stack. KASan adds red zones around stack variables at the start of each function, and removes them at the end. If a function does not return normally (for example, in case of longjmp-like functions and exception handling), red zones must be removed explicitly with __asan_handle_no_return.

Hook heap memory allocation routines

Bare-metal code in the vast majority of cases provides its own heap implementation. It is our responsibility to implement an instrumented version of heap memory allocation and freeing routines which enable KASan to detect memory corruption bugs on the heap.

Essentially, we would need to instrument the memory allocator with the code which unpoisons KASan shadow memory corresponding to the allocated memory buffer. Additionally, we may want to insert an extra poisoned red zone memory (which accessing would then generate a KASan violation) to the end of the allocated buffer to increase the likelihood of catching out-of-bounds memory reads/writes.

Similarly, in the memory deallocation routine (such as free) we would need to poison the shadow memory corresponding to the free buffer so that any subsequent access (such as, use-after-free) would generate a KASan violation.

We can go even further by placing the freed memory buffer into a quarantine instead of immediately returning the free memory back to the allocator. This way, the freed memory buffer is suspended in quarantine for some time and will have its KASan shadow bytes poisoned for a longer period of time, increasing the probability of catching a use-after-free access to this buffer.

Enable KASan for heap, stack and global variables

With all the necessary building blocks implemented we are ready to enable KASan for our bare-metal code by applying the following compiler options while building the target with the LLVM toolchain.

The -fsanitize=kernel-address Clang option instructs the compiler to instrument memory load/store operations with the KASan verification routines.

We use the -asan-mapping-offset LLVM option to indicate where we want our shadow memory to be located. For instance, let’s assume that we would like to cover address range 0x40000000 - 0x4fffffff and we want to keep shadow memory at address 0x4A700000. So, we would use -mllvm -asan-mapping-offset=0x42700000 as 0x40000000 >> 3 + 0x42700000 == 0x4A700000.

To cover globals and stack variables with KASan we would need to pass additional options to the compiler: -mllvm -asan-stack=1 -mllvm -asan-globals=1. It’s worth mentioning that instrumenting both globals and stack variables will likely result in an increase in size of the corresponding memory which might need to be accounted for in the linker script.

Finally, to prevent significant increase in size of the code section due to KASan instrumentation we instruct the compiler to always outline KASan checks using the -mllvm -asan-instrumentation-with-call-threshold=0 option. Otherwise, the compiler might inline

__asan_loadXX_noabort, __asan_storeXX_noabort routines for load/store operations resulting in bloating the generated object code.

LLVM has traditionally only supported sanitizers with runtimes for specific targets with predefined runtimes, however we have upstreamed LLVM sanitizer support for bare-metal targets under the assumption that the runtime can be defined for the particular target. You’ll need the latest version of Clang to benefit from this.

Conclusion

Following these steps we managed to enable KASan for a firmware target and use it in pre-production test builds. This led to early discovery of memory corruption issues that were easily remediated due to the actionable reports produced by KASan. These builds can be used with fuzzers to detect edge case bugs that normal testing fails to trigger, yet which can have significant security implications.

Our work with KASan is just one example of the multiple techniques the Android team is exploring to further secure bare-metal firmware in the Android Platform. Ideally we want to avoid introducing memory safety vulnerabilities in the first place so we are working to address this problem through adoption of memory-safe Rust in bare-metal environments. The Android team has developed Rust training which covers bare-metal Rust extensively. We highly encourage others to explore Rust (or other memory-safe languages) as an alternative to C/C++ in their firmware.

If you have any questions, please reach out – we’re here to help!

Acknowledgements: Thank you to Roger Piqueras Jover for contributions to this post, and to Evgenii Stepanov for upstreaming LLVM support for bare-metal sanitizers. Special thanks also to our colleagues who contribute and support our firmware security efforts: Sami Tolvanen, Stephan Somogyi, Stephan Chen, Dominik Maier, Xuan Xing, Farzan Karimi, Pirama Arumuga Nainar, Stephen Hines.

Real-time, privacy-preserving URL protection

For more than 15 years, Google Safe Browsing has been protecting users from phishing, malware, unwanted software and more, by identifying and warning users about potentially abusive sites on more than 5 billion devices around the world. As attackers grow more sophisticated, we've seen the need for protections that can adapt as quickly as the threats they defend against. That’s why we're excited to announce a new version of Safe Browsing that will provide real-time, privacy-preserving URL protection for people using the Standard protection mode of Safe Browsing in Chrome.

Current landscape

Chrome automatically protects you by flagging potentially dangerous sites and files, hand in hand with Safe Browsing which discovers thousands of unsafe sites every day and adds them to its lists of harmful sites and files.

So far, for privacy and performance reasons, Chrome has first checked sites you visit against a locally-stored list of known unsafe sites which is updated every 30 to 60 minutes – this is done using hash-based checks.


Hash-based check overview

But unsafe sites have adapted — today, the majority of them exist for less than 10 minutes, meaning that by the time the locally-stored list of known unsafe sites is updated, many have slipped through and had the chance to do damage if users happened to visit them during this window of opportunity. Further, Safe Browsing’s list of harmful websites continues to grow at a rapid pace. Not all devices have the resources necessary to maintain this growing list, nor are they always able to receive and apply updates to the list at the frequency necessary to benefit from full protection.

Safe Browsing’s Enhanced protection mode already stays ahead of such threats with technologies such as real-time list checks and AI-based classification of malicious URLs and web pages. We built this mode as an opt-in to give users the choice of sharing more security-related data in order to get stronger security. This mode has shown that checking lists in real time brings significant value, so we decided to bring that to the default Standard protection mode through a new API – one that doesn't share the URLs of sites you visit with Google.

Introducing real-time, privacy-preserving Safe Browsing

How it works

In order to transition to real-time protection, checks now need to be performed against a list that is maintained on the Safe Browsing server. The server-side list can include unsafe sites as soon as they are discovered, so it is able to capture sites that switch quickly. It can also grow as large as needed because the Safe Browsing server is not constrained in the same way that user devices are.

Behind the scenes, here's what is happening in Chrome:

  1. When you visit a site, Chrome first checks its cache to see if the address (URL) of the site is already known to be safe (see the “Staying speedy and reliable” section for details).
  2. If the visited URL is not in the cache, it may be unsafe, so a real-time check is necessary.
  3. Chrome obfuscates the URL by following the URL hashing guidance to convert the URL into 32-byte full hashes.
  4. Chrome truncates the full hashes into 4-byte long hash prefixes.
  5. Chrome encrypts the hash prefixes and sends them to a privacy server (see the “Keeping your data private” section for details).
  6. The privacy server removes potential user identifiers and forwards the encrypted hash prefixes to the Safe Browsing server via a TLS connection that mixes requests with many other Chrome users.
  7. The Safe Browsing server decrypts the hash prefixes and matches them against the server-side database, returning full hashes of all unsafe URLs that match one of the hash prefixes sent by Chrome.
  8. After receiving the unsafe full hashes, Chrome checks them against the full hashes of the visited URL.
  9. If any match is found, Chrome will show a warning.

Keeping your data private

In order to preserve user privacy, we have partnered with Fastly, an edge cloud platform that provides content delivery, edge compute, security, and observability services, to operate an Oblivious HTTP (OHTTP) privacy server between Chrome and Safe Browsing – you can learn more about Fastly's commitment to user privacy on their Customer Trust page. With OHTTP, Safe Browsing does not see your IP address, and your Safe Browsing checks are mixed amongst those sent by other Chrome users. This means Safe Browsing cannot correlate the URL checks you send as you browse the web.

Before hash prefixes leave your device, Chrome encrypts them using a public key from Safe Browsing. These encrypted hash prefixes are then sent to the privacy server. Since the privacy server doesn’t know the private key, it cannot decrypt the hash prefixes, which offers privacy from the privacy server itself.

The privacy server then removes potential user identifiers such as your IP address and forwards the encrypted hash prefixes to the Safe Browsing server. The privacy server is operated independently by Fastly, meaning that Google doesn’t have access to potential user identifiers (including IP address and User Agent) from the original request. Once the Safe Browsing server receives the encrypted hash prefixes from the privacy server, it decrypts the hash prefixes with its private key and then continues to check the server-side list.

Ultimately, Safe Browsing sees the hash prefixes of your URL but not your IP address, and the privacy server sees your IP address but not the hash prefixes. No single party has access to both your identity and the hash prefixes. As such, your browsing activity remains private.

Real-time check overview

Staying speedy and reliable

Compared with the hash-based check, the real-time check requires sending a request to a server, which adds additional latency. We have employed a few techniques to make sure your browsing experience continues to be smooth and responsive.

First, before performing the real-time check, Chrome checks against a global and local cache on your device to avoid unnecessary delay.

  • The global cache is a list of hashes of known-safe URLs that is served by Safe Browsing. Chrome fetches it in the background. If any full hash of the URL is found in the global cache, Chrome will consider it less risky and perform a hash-based check instead.
  • The local cache, on the other hand, is a list of full hashes that are saved from previous Safe Browsing checks. If there is a match in the local cache, and the cache has not yet expired, Chrome will not send a real-time request to the Safe Browsing server.

Both caches are stored in memory, so it is much faster to check them than sending a real-time request over the network.

In addition, Chrome follows a fallback mechanism in case of unsuccessful or slow requests. If the real-time request fails consecutively, Chrome will enter a back-off mode and downgrade the checks to hash-based checks for a certain period.

We are also in the process of introducing an asynchronous mechanism, which will allow the site to load while the real-time check is in progress. This will improve the user experience, as the real-time check won’t block page load.

What real-time, privacy-preserving URL protection means for you

Chrome users

With the latest release of Chrome for desktop, Android, and iOS, we’re upgrading the Standard protection mode of Safe Browsing so it will now check sites using Safe Browsing’s real-time protection protocol, without sharing your browsing history with Google. You don't need to take any action to benefit from this improved functionality.

If you want more protection, we still encourage you to turn on the Enhanced protection mode of Safe Browsing. You might wonder why you need enhanced protection when you'll be getting real-time URL protection in Standard protection – this is because in Standard protection mode, the real-time feature can only protect you from sites that Safe Browsing has already confirmed to be unsafe. On the other hand, Enhanced protection mode is able to use additional information together with advanced machine learning models to protect you from sites that Safe Browsing may not yet have confirmed to be unsafe, for example because the site was only very recently created or is cloaking its true behavior to Safe Browsing’s detection systems.

Enhanced protection also continues to offer protection beyond real-time URL checks, for example by providing deep scans for suspicious files and extra protection from suspicious Chrome extensions.

Enterprises

The real-time feature of the Standard protection mode of Safe Browsing is on by default for Chrome. If needed, it may be configured using the policy SafeBrowsingProxiedRealTimeChecksAllowed. It is also worth noting that in order for this feature to work in Chrome, enterprises may need to explicitly allow traffic to the Fastly privacy server. If the server is not reachable, Chrome will downgrade the checks to hash-based checks.

Developers

While Chrome is the first surface where these protections are available, we plan to make them available to eligible developers for non-commercial use cases via the Safe Browsing API. Using the API, developers and privacy server operators can partner to better protect their products’ users from fast-moving malicious actors in a privacy-preserving manner. To learn more, keep an eye out for our upcoming developer documentation to be published on the Google for Developers site.

Vulnerability Reward Program: 2023 Year in Review

Last year, we again witnessed the power of community-driven security efforts as researchers from around the world contributed to help us identify and address thousands of vulnerabilities in our products and services. Working with our dedicated bug hunter community, we awarded $10 million to our 600+ researchers based in 68 countries.

New Resources and Improvements

Just like every year, 2023 brought a series of changes and improvements to our vulnerability reward programs:

  • Through our new Bonus Awards program, we now periodically offer time-limited, extra rewards for reports to specific VRP targets.
  • We expanded our exploit reward program to Chrome and Cloud through the launch of v8CTF, a CTF focused on V8, the JavaScript engine that powers Chrome.
  • We launched Mobile VRP which focuses on first-party Android applications.
  • Our new Bughunters blog shared ways in which we make the internet, as a whole, safer, and what that journey entails. Take a look at our ever-growing repository of posts!
  • To further our engagement with top security researchers, we also hosted our yearly security conference ESCAL8 in Tokyo. It included live hacking events and competitions, student training with our init.g workshops, and talks from researchers and Googlers. Stay tuned for details on ESCAL8 2024.

As in past years, we are sharing our 2023 Year in Review statistics across all of our programs. We would like to give a special thank you to all of our dedicated researchers for their continued work with our programs - we look forward to more collaboration in the future!

Android and Google Devices

In 2023, the Android VRP achieved significant milestones, reflecting our dedication to securing the Android ecosystem. We awarded over $3.4 million in rewards to researchers who uncovered remarkable vulnerabilities within Android and increased our maximum reward amount to $15,000 for critical vulnerabilities. We also saw a sharpened focus on higher severity issues as a result of our changes to incentivize report quality and increasing rewards for high and critical severity issues.

Expanding our program’s scope, Wear OS has been added to the program to further incentivize research in new wearable technology to ensure users’ safety.

Working closely with top researchers at the ESCAL8 conference, we also hosted a live hacking event for Wear OS and Android Automotive OS which resulted in $70,000 rewarded to researchers for finding over 20 critical vulnerabilities!

We would also like to spotlight the hardwear.io security conferences. Hardwear.io gave us a platform to engage with top hardware security researchers who uncovered over 50 vulnerabilities in Nest, Fitbit, and Wearables, and received a total of $116,000 last year!

The Google Play Security Reward Program continued to foster security research across popular Android apps on Google Play.

A huge thank you to the researchers who made our program such a success. A special shout out to Zinuo Han (@ele7enxxh) of OPPO Amber Security Lab and Yu-Cheng Lin (林禹成) (@AndroBugs) for your hard work and continuing to be some of the top researchers contributing to Android VRPs!

Chrome

2023 was a year of changes and experimentation for the Chrome VRP. In Chrome Milestone 116, MiraclePtr was launched across all Chrome platforms. This resulted in raising the difficulty of discovery of fully exploitable non-renderer UAFs in Chrome and resulted in lower reward amounts for MiraclePtr-protected UAFs, as highly mitigated security bugs. While code and issues protected by MiraclePtr are expected to be resilient to the exploitation of non-renderer UAFs, the Chrome VRP launched the MiraclePtr Bypass Reward to incentivize research toward discovering potential bypasses of this protection.

The Chrome VRP also launched the Full Chain Exploit Bonus, offering triple the standard full reward amount for the first Chrome full-chain exploit reported and double the standard full reward amount for any follow-up reports. While both of these large incentives have gone unclaimed, we are leaving the door open in 2024 for any researchers looking to take on these challenges.

In 2023, Chrome VRP also introduced increased rewards for V8 bugs in older channels of Chrome, with an additional bonus for bugs existing before M105. This resulted in a few very impactful reports of long-existing V8 bugs, including one report of a V8 JIT optimization bug in Chrome since at least M91, which resulted in a $30,000 reward for that researcher.

All of this resulted in $2.1M in rewards to security researchers for 359 unique reports of Chrome Browser security bugs. We were also able to meet some of our top researchers from previous years who were invited to participate in bugSWAT as part of Google’s ESCAL8 event in Tokyo in October. We capped off the year by publicly announcing our 2023 Top 20 Chrome VRP reporters who received a bonus reward for their contributions.

Thank you to the Chrome VRP security researcher community for your contributions and efforts to help us make Chrome more secure for everyone!

Generative AI

Last year, we also ran a bugSWAT live-hacking event targeting LLM products. Apart from fun, sun, and a lot to do, we also got 35 reports, totaling more than $87,000 - and discovered issues like Johann, Joseph, and Kai’s “Hacking Google Bard - From Prompt Injection to Data Exfiltration” and Roni, Justin, and Joseph’s “We Hacked Google A.I. for $50,000”.

To help AI-focused bughunters know what’s in scope and what’s not, we recently published our criteria for bugs in AI products. This criteria aims to facilitate testing for traditional security vulnerabilities as well as risks specific to AI systems, and is one way that we are implementing the voluntary AI commitments that Google made at the White House in July.

Looking Forward

We remain committed to fostering collaboration, innovation, and transparency with the security community. Our ongoing mission is to stay ahead of emerging threats, adapt to evolving technologies, and continue to strengthen the security posture of Google’s products and services. We look forward to continuing to drive greater advancements in the world of cybersecurity.

A huge thank you to our bug hunter community for helping to make Google products and platforms more safe and secure for our users around the world!

Thank you to Adam Bacchus, Dirk Göhmann, Eduardo Vela, Sarah Jacobus, Amy Ressler, Martin Straka, Jan Keller, Tony Mendez.

Secure by Design: Google’s Perspective on Memory Safety


Piloting new ways of protecting Android users from financial fraud

From its founding, Android has been guided by principles of openness, transparency, safety, and choice. Android gives you the freedom to choose which device best fits your needs, while also providing the flexibility to download apps from a variety of sources, including preloaded app stores such as the Google Play Store or the Galaxy Store; third-party app stores; and direct downloads from the Internet.

Keeping users safe in an open ecosystem takes sophisticated defenses. That’s why Android provides multiple layers of protections, powered by AI and backed by a large dedicated security & privacy team, to help to protect our users from security threats while continually making the platform more resilient. We also provide our users with numerous built-in protections like Google Play Protect, the world’s most widely deployed threat detection service, which actively scans over 125 billion apps on devices every day to monitor for harmful behavior. That said, our data shows that a disproportionate amount of bad actors take advantage of select APIs and distribution channels in this open ecosystem.

Elevating app security in an open ecosystem

While users have the flexibility to download apps from many sources, the safety of an app can vary depending on the download source. Google Play, for example, carries out rigorous operational reviews to ensure app safety, including proper high-risk API use and permissions handling. Other app stores may also follow established policies and procedures that help reduce risks to users and their data. These protections often include requirements for developers to declare which permissions their apps use and how developers plan to use app data. Conversely, standalone app distribution sources like web browsers, messaging apps or file managers – which we commonly refer to as Internet-sideloading – do not offer the same rigorous requirements and operational reviews. Our data demonstrates that users who download from these sources today face unusually high security risks due to these missing protections.

We recently launched enhanced Google Play Protect real-time scanning to help better protect users against novel malicious Internet-sideloaded apps. This enhancement is designed to address malicious apps that leverage various methods, such as AI, to avoid detection. This feature, now deployed on Android devices with Google Play Services in India, Thailand, Singapore and Brazil, has already made a significant impact on user safety.

As a result of the real-time scanning enhancement, Play Protect has identified 515,000 new malicious apps and issued more than 3.1 million warnings or blocks of those apps. Play Protect is constantly improving its detection capabilities with each identified app, allowing us to strengthen our protections for the entire Android ecosystem.

A new pilot to combat financial fraud


Cybercriminals continue to invest in advanced financial fraud scams, costing consumers more than $1 trillion in losses. According to the 2023 Global State of Scams Report by the Global Anti-Scam Alliance, 78 percent of mobile users surveyed experienced at least one scam in the last year. Of those surveyed, 45 percent said they’re experiencing more scams in the last 12 months. The Global Scam Report also found that scams were most often initiated by sending scam links via various messaging platforms to get users to install malicious apps and very often paired with a phone call posing to be from a valid entity.

Scammers frequently employ social engineering tactics to deceive mobile users. Using urgent pretenses that often involve a risk to a user’s finances or an opportunity for quick wealth, cybercriminals convince users to disable security safeguards and ignore proactive warnings for potential malware, scams, and phishing. We’ve seen a large percentage of users ignore, or are tricked into dismissing, these proactive Android platform warnings and proceed with installing malicious apps. This can lead to users ultimately disclosing their security codes, passwords, financial information and/or transferring funds unknowingly to a fraudster.

To help better protect Android users from these financial fraud attacks, we are piloting enhanced fraud protection with Google Play Protect. As part of a continued strategic partnership with the Cyber Security Agency of Singapore (CSA), we will launch this first pilot in Singapore in the coming weeks to help keep Android users safe from mobile financial fraud.

This enhanced fraud protection will analyze and automatically block the installation of apps that may use sensitive permissions frequently abused for financial fraud when the user attempts to install the app from an Internet-sideloading source (web browsers, messaging apps or file managers). This enhancement will inspect the permissions the app declared in real-time and specifically look for four permission requests: RECEIVE_SMS, READ_SMS, BIND_Notifications, and Accessibility. These permissions are frequently abused by fraudsters to intercept one-time passwords via SMS or notifications, as well as spy on screen content. Based on our analysis of major fraud malware families that exploit these sensitive permissions, we found that over 95 percent of installations came from Internet-sideloading sources.

During the upcoming pilot, when a user in Singapore attempts to install an application from an Internet-sideloading source and any of these four permissions are declared, Play Protect will automatically block the installation with an explanation to the user.

Collaborating to combat mobile fraud

This enhanced fraud protection has undergone testing by the Singapore government and will be rolling out to Android devices with Google Play services.

“The fight against online scams is a dynamic one. As cybercriminals refine their methods, we must collaborate and innovate to stay ahead, “ said Mr Chua Kuan Seah, Deputy Chief Executive of CSA. “Through such partnerships with technology players like Google, we are constantly improving our anti-scam defenses to protect Singaporeans online and safeguard their digital assets.”

Together with CSA, we will be closely monitoring the results of the pilot program to assess its impact and make adjustments as needed. We will also support CSA by continuing to assist with malware detection and analysis, sharing malware insights and techniques, and creating user and developer education resources.

How developers can prepare

For developers distributing apps that may be affected by this pilot, please take the time to review the device permissions your app is requesting and ensure you’re following developer best practices. Your app should only request permissions that the app needs to complete an action and ensure it does not violate the Mobile Unwanted Software principles. Always ensure that your app does not engage in behavior that could be considered potentially harmful or malware.

If you find that your app is affected by the app protection pilot you can refer to our updated developer guidance for Play Protect warnings for tips on how to help fix potential issues with your app and instructions for filing an appeal if needed.

Our commitment to protecting Android users

We believe industry collaboration is essential to protect users from mobile security threats and fraud. Piloting these new protections will help us stay ahead of new attacks and evolve our solutions to defeat scammers and their expanding fraud attempt. We have an unwavering commitment to protecting our users around the world and look forward to continuing to partner with governments, ecosystem partners and other stakeholders to improve user protections.