Tag Archives: Android-Security

Combating Potentially Harmful Applications with Machine Learning at Google: Datasets and Models

Posted by Mo Yu, Android Security & Privacy Team

In a previous blog post, we talked about using machine learning to combat Potentially Harmful Applications (PHAs). This blog post covers how Google uses machine learning techniques to detect and classify PHAs. We'll discuss the challenges in the PHA detection space, including the scale of data, the correct identification of PHA behaviors, and the evolution of PHA families. Next, we will introduce two of the datasets that make the training and implementation of machine learning models possible, such as app analysis data and Google Play data. Finally, we will present some of the approaches we use, including logistic regression and deep neural networks.

Using machine learning to scale

Detecting PHAs is challenging and requires a lot of resources. Our security experts need to understand how apps interact with the system and the user, analyze complex signals to find PHA behavior, and evolve their tactics to stay ahead of PHA authors. Every day, Google Play Protect (GPP) analyzes over half a million apps, which makes a lot of new data for our security experts to process.

Leveraging machine learning helps us detect PHAs faster and at a larger scale. We can detect more PHAs just by adding additional computing resources. In many cases, machine learning can find PHA signals in the training data without human intervention. Sometimes, those signals are different than signals found by security experts. Machine learning can take better advantage of this data, and discover hidden relationships between signals more effectively.

There are two major parts of Google Play Protect's machine learning protections: the data and the machine learning models.

Data sources

The quality and quantity of the data used to create a model are crucial to the success of the system. For the purpose of PHA detection and classification, our system mainly uses two anonymous data sources: data from analyzing apps and data from how users experience apps.

App data

Google Play Protect analyzes every app that it can find on the internet. We created a dataset by decomposing each app's APK and extracting PHA signals with deep analysis. We execute various processes on each app to find particular features and behaviors that are relevant to the PHA categories in scope (for example, SMS fraud, phishing, privilege escalation). Static analysis examines the different resources inside an APK file while dynamic analysis checks the behavior of the app when it's actually running. These two approaches complement each other. For example, dynamic analysis requires the execution of the app regardless of how obfuscated its code is (obfuscation hinders static analysis), and static analysis can help detect cloaking attempts in the code that may in practice bypass dynamic analysis-based detection. In the end, this analysis produces information about the app's characteristics, which serve as a fundamental data source for machine learning algorithms.

Google Play data

In addition to analyzing each app, we also try to understand how users perceive that app. User feedback (such as the number of installs, uninstalls, user ratings, and comments) collected from Google Play can help us identify problematic apps. Similarly, information about the developer (such as the certificates they use and their history of published apps) contribute valuable knowledge that can be used to identify PHAs. All these metrics are generated when developers submit a new app (or new version of an app) and by millions of Google Play users every day. This information helps us to understand the quality, behavior, and purpose of an app so that we can identify new PHA behaviors or identify similar apps.

In general, our data sources yield raw signals, which then need to be transformed into machine learning features for use by our algorithms. Some signals, such as the permissions that an app requests, have a clear semantic meaning and can be directly used. In other cases, we need to engineer our data to make new, more powerful features. For example, we can aggregate the ratings of all apps that a particular developer owns, so we can calculate a rating per developer and use it to validate future apps. We also employ several techniques to focus in on interesting data.To create compact representations for sparse data, we use embedding. To help streamline the data to make it more useful to models, we use feature selection. Depending on the target, feature selection helps us keep the most relevant signals and remove irrelevant ones.

By combining our different datasets and investing in feature engineering and feature selection, we improve the quality of the data that can be fed to various types of machine learning models.


Building a good machine learning model is like building a skyscraper: quality materials are important, but a great design is also essential. Like the materials in a skyscraper, good datasets and features are important to machine learning, but a great algorithm is essential to identify PHA behaviors effectively and efficiently.

We train models to identify PHAs that belong to a specific category, such as SMS-fraud or phishing. Such categories are quite broad and contain a large number of samples given the number of PHA families that fit the definition. Alternatively, we also have models focusing on a much smaller scale, such as a family, which is composed of a group of apps that are part of the same PHA campaign and that share similar source code and behaviors. On the one hand, having a single model to tackle an entire PHA category may be attractive in terms of simplicity but precision may be an issue as the model will have to generalize the behaviors of a large number of PHAs believed to have something in common. On the other hand, developing multiple PHA models may require additional engineering efforts, but may result in better precision at the cost of reduced scope.

We use a variety of modeling techniques to modify our machine learning approach, including supervised and unsupervised ones.

One supervised technique we use is logistic regression, which has been widely adopted in the industry. These models have a simple structure and can be trained quickly. Logistic regression models can be analyzed to understand the importance of the different PHA and app features they are built with, allowing us to improve our feature engineering process. After a few cycles of training, evaluation, and improvement, we can launch the best models in production and monitor their performance.

For more complex cases, we employ deep learning. Compared to logistic regression, deep learning is good at capturing complicated interactions between different features and extracting hidden patterns. The millions of apps in Google Play provide a rich dataset, which is advantageous to deep learning.

In addition to our targeted feature engineering efforts, we experiment with many aspects of deep neural networks. For example, a deep neural network can have multiple layers and each layer has several neurons to process signals. We can experiment with the number of layers and neurons per layer to change model behaviors.

We also adopt unsupervised machine learning methods. Many PHAs use similar abuse techniques and tricks, so they look almost identical to each other. An unsupervised approach helps define clusters of apps that look or behave similarly, which allows us to mitigate and identify PHAs more effectively. We can automate the process of categorizing that type of app if we are confident in the model or can request help from a human expert to validate what the model found.

PHAs are constantly evolving, so our models need constant updating and monitoring. In production, models are fed with data from recent apps, which help them stay relevant. However, new abuse techniques and behaviors need to be continuously detected and fed into our machine learning models to be able to catch new PHAs and stay on top of recent trends. This is a continuous cycle of model creation and updating that also requires tuning to ensure that the precision and coverage of the system as a whole matches our detection goals.

Looking forward

As part of Google's AI-first strategy, our work leverages many machine learning resources across the company, such as tools and infrastructures developed by Google Brain and Google Research. In 2017, our machine learning models successfully detected 60.3% of PHAs identified by Google Play Protect, covering over 2 billion Android devices. We continue to research and invest in machine learning to scale and simplify the detection of PHAs in the Android ecosystem.


This work was developed in joint collaboration with Google Play Protect, Safe Browsing and Play Abuse teams with contributions from Andrew Ahn, Hrishikesh Aradhye, Daniel Bali, Hongji Bao, Yajie Hu, Arthur Kaiser, Elena Kovakina, Salvador Mandujano, Melinda Miller, Rahul Mishra, Damien Octeau, Sebastian Porst, Chuangang Ren, Monirul Sharif, Sri Somanchi, Sai Deep Tetali, Zhikun Wang, and Mo Yu.

Android Protected Confirmation: Taking transaction security to the next level

Posted by Janis Danisevskis, Information Security Engineer, Android Security

In Android Pie, we introduced Android Protected Confirmation, the first major mobile OS API that leverages a hardware protected user interface (Trusted UI) to perform critical transactions completely outside the main mobile operating system. This Trusted UI protects the choices you make from fraudulent apps or a compromised operating system. When an app invokes Protected Confirmation, control is passed to the Trusted UI, where transaction data is displayed and user confirmation of that data's correctness is obtained.

Once confirmed, your intention is cryptographically authenticated and unforgeable when conveyed to the relying party, for example, your bank. Protected Confirmation increases the bank's confidence that it acts on your behalf, providing a higher level of protection for the transaction.

Protected Confirmation also adds additional security relative to other forms of secondary authentication, such as a One Time Password or Transaction Authentication Number. These mechanisms can be frustrating for mobile users and also fail to protect against a compromised device that can corrupt transaction data or intercept one-time confirmation text messages.

Once the user approves a transaction, Protected Confirmation digitally signs the confirmation message. Because the signing key never leaves the Trusted UI's hardware sandbox, neither app malware nor a compromised operating system can fool the user into authorizing anything. Protected Confirmation signing keys are created using Android's standard AndroidKeyStore API. Before it can start using Android Protected Confirmation for end-to-end secure transactions, the app must enroll the public KeyStore key and its Keystore Attestation certificate with the remote relying party. The attestation certificate certifies that the key can only be used to sign Protected Confirmations.

There are many possible use cases for Android Protected Confirmation. At Google I/O 2018, the What's new in Android security session showcased partners planning to leverage Android Protected Confirmation in a variety of ways, including Royal Bank of Canada person to person money transfers; Duo Security, Nok Nok Labs, and ProxToMe for user authentication; and Insulet Corporation and Bigfoot Biomedical, for medical device control.

Insulet, a global leading manufacturer of tubeless patch insulin pumps, has demonstrated how they can modify their FDA cleared Omnipod DASH TM Insulin management system in a test environment to leverage Protected Confirmation to confirm the amount of insulin to be injected. This technology holds the promise for improved quality of life and reduced cost by enabling a person with diabetes to leverage their convenient, familiar, and secure smartphone for control rather than having to rely on a secondary, obtrusive, and expensive remote control device. (Note: The Omnipod DASH™ System is not cleared for use with Pixel 3 mobile device or Protected Confirmation).

This work is fulfilling an important need in the industry. Since smartphones do not fit the mold of an FDA approved medical device, we've been working with FDA as part of DTMoSt, an industry-wide consortium, to define a standard for phones to safely control medical devices, such as insulin pumps. A technology like Protected Confirmation plays an important role in gaining higher assurance of user intent and medical safety.

To integrate Protected Confirmation into your app, check out the Android Protected Confirmation training article. Android Protected Confirmation is an optional feature in Android Pie. Because it has low-level hardware dependencies, Protected Confirmation may not be supported by all devices running Android Pie. Google Pixel 3 and 3XL devices are the first to support Protected Confirmation, and we are working closely with other manufacturers to adopt this market-leading security innovation on more devices.

Building a Titan: Better security through a tiny chip

Posted by Nagendra Modadugu and Bill Richardson, Google Device Security Group

At the Made by Google event last week, we talked about the combination of AI + Software + Hardware to help organize your information. To better protect that information at a hardware level, our new Pixel 3 and Pixel 3 XL devices include a Titan M chip.We briefly introduced Titan M and some of its benefits on our Keyword Blog, and with this post we dive into some of its technical details.

Titan M is a second-generation, low-power security module designed and manufactured by Google, and is a part of the Titan family. As described in the Keyword Blog post, Titan M performs several security sensitive functions, including:

  • Storing and enforcing the locks and rollback counters used by Android Verified Boot.
  • Securely storing secrets and rate-limiting invalid attempts at retrieving them using the Weaver API.
  • Providing backing for the Android Strongbox Keymaster module, including Trusted User Presence and Protected Confirmation. Titan M has direct electrical connections to the Pixel's side buttons, so a remote attacker can't fake button presses. These features are available to third-party apps, such as FIDO U2F Authentication.
  • Enforcing factory-reset policies, so that lost or stolen phones can only be restored to operation by the authorized owner.
  • Ensuring that even Google can't unlock a phone or install firmware updates without the owner's cooperation with Insider Attack Resistance.

Including Titan M in Pixel 3 devices substantially reduces the attack surface. Because Titan M is a separate chip, the physical isolation mitigates against entire classes of hardware-level exploits such as Rowhammer, Spectre, and Meltdown. Titan M's processor, caches, memory, and persistent storage are not shared with the rest of the phone's system, so side channel attacks like these—which rely on subtle, unplanned interactions between internal circuits of a single component—are nearly impossible. In addition to its physical isolation, the Titan M chip contains many defenses to protect against external attacks.

But Titan M is not just a hardened security microcontroller, but rather a full-lifecycle approach to security with Pixel devices in mind. Titan M's security takes into consideration all the features visible to Android down to the lowest level physical and electrical circuit design and extends beyond each physical device to our supply chain and manufacturing processes. At the physical level, we incorporated essential features optimized for the mobile experience: low power usage, low-latency, hardware crypto acceleration, tamper detection, and secure, timely firmware updates. We improved and invested in the supply chain for Titan M by creating a custom provisioning process, which provides us with transparency and control starting from the earliest silicon stages.

Finally, in the interest of transparency, the Titan M firmware source code will be publicly available soon. While Google holds the root keys necessary to sign Titan M firmware, it will be possible to reproduce binary builds based on the public source for the purpose of binary transparency.

A closer look at Titan M

Titan (left) and Titan M (right)

Titan M's CPU is an ARM Cortex-M3 microprocessor specially hardened against side-channel attacks and augmented with defensive features to detect and respond to abnormal conditions. The Titan M CPU core also exposes several control registers, which can be used to taper access to chip configuration settings and peripherals. Once powered on, Titan M verifies the signature of its flash-based firmware using a public key built into the chip's silicon. If the signature is valid, the flash is locked so it can't be modified, and then the firmware begins executing.

Titan M also features several hardware accelerators: AES, SHA, and a programmable big number coprocessor for public key algorithms. These accelerators are flexible and can either be initialized with keys provided by firmware or with chip-specific and hardware-bound keys generated by the Key Manager module. Chip-specific keys are generated internally based on entropy derived from the True Random Number Generator (TRNG), and thus such keys are never externally available outside the chip over its entire lifetime.

While implementing Titan M firmware, we had to take many system constraints into consideration. For example, packing as many security features into Titan M's 64 Kbytes of RAM required all firmware to execute exclusively off the stack. And to reduce flash-wear, RAM contents can be preserved even during low-power mode when most hardware modules are turned off.

The diagram below provides a high-level view of the chip components described here.

Better security through transparency and innovation

At the heart of our implementation of Titan M are two broader trends: transparency and building a platform for future innovation.

Transparency around every step of the design process — from logic gates to boot code to the applications — gives us confidence in the defenses we're providing for our users. We know what's inside, how it got there, how it works, and who can make changes.

Custom hardware allows us to provide new features, capabilities, and performance not readily available in off-the-shelf components. These changes allow higher assurance use cases like two-factor authentication, medical device control, P2P payments, and others that we will help develop down the road.

As more of our lives are bound up in our phones, keeping those phones secure and trustworthy is increasingly important. Google takes that responsibility seriously. Titan M is just the latest step in our continuing efforts to improve the privacy and security of all our users.

Control Flow Integrity in the Android kernel

Posted by Sami Tolvanen, Staff Software Engineer, Android Security

Android's security model is enforced by the Linux kernel, which makes it a tempting target for attackers. We have put a lot of effort into hardening the kernel in previous Android releases and in Android 9, we continued this work by focusing on compiler-based security mitigations against code reuse attacks.

Google's Pixel 3 will be the first Android device to ship with LLVM's forward-edge Control Flow Integrity (CFI) enforcement in the kernel, and we have made CFI support available in Android kernel versions 4.9 and 4.14. This post describes how kernel CFI works and provides solutions to the most common issues developers might run into when enabling the feature.

Protecting against code reuse attacks

A common method of exploiting the kernel is using a bug to overwrite a function pointer stored in memory, such as a stored callback pointer or a return address that had been pushed to the stack. This allows an attacker to execute arbitrary parts of the kernel code to complete their exploit, even if they cannot inject executable code of their own. This method of gaining code execution is particularly popular with the kernel because of the huge number of function pointers it uses, and the existing memory protections that make code injection more challenging.

CFI attempts to mitigate these attacks by adding additional checks to confirm that the kernel's control flow stays within a precomputed graph. This doesn't prevent an attacker from changing a function pointer if a bug provides write access to one, but it significantly restricts the valid call targets, which makes exploiting such a bug more difficult in practice.

Figure 1. In an Android device kernel, LLVM's CFI limits 55% of indirect calls to at most 5 possible targets and 80% to at most 20 targets.

Gaining full program visibility with Link Time Optimization (LTO)

In order to determine all valid call targets for each indirect branch, the compiler needs to see all of the kernel code at once. Traditionally, compilers work on a single compilation unit (source file) at a time and leave merging the object files to the linker. LLVM's solution to CFI is to require the use of LTO, where the compiler produces LLVM-specific bitcode for all C compilation units, and an LTO-aware linker uses the LLVM back-end to combine the bitcode and compile it into native code.

Figure 2. A simplified overview of how LTO works in the kernel. All LLVM bitcode is combined, optimized, and generated into native code at link time.

Linux has used the GNU toolchain for assembling, compiling, and linking the kernel for decades. While we continue to use the GNU assembler for stand-alone assembly code, LTO requires us to switch to LLVM's integrated assembler for inline assembly, and either GNU gold or LLVM's own lld as the linker. Switching to a relatively untested toolchain on a huge software project will lead to compatibility issues, which we have addressed in our arm64 LTO patch sets for kernel versions 4.9 and 4.14.

In addition to making CFI possible, LTO also produces faster code due to global optimizations. However, additional optimizations often result in a larger binary size, which may be undesirable on devices with very limited resources. Disabling LTO-specific optimizations, such as global inlining and loop unrolling, can reduce binary size by sacrificing some of the performance gains. When using GNU gold, the aforementioned optimizations can be disabled with the following additions to LDFLAGS:

LDFLAGS += -plugin-opt=-inline-threshold=0 \

Note that flags to disable individual optimizations are not part of the stable LLVM interface and may change in future compiler versions.

Implementing CFI in the Linux kernel

LLVM's CFI implementation adds a check before each indirect branch to confirm that the target address points to a valid function with a correct signature. This prevents an indirect branch from jumping to an arbitrary code location and even limits the functions that can be called. As C compilers do not enforce similar restrictions on indirect branches, there were several CFI violations due to function type declaration mismatches even in the core kernel that we have addressed in our CFI patch sets for kernels 4.9 and 4.14.

Kernel modules add another complication to CFI, as they are loaded at runtime and can be compiled independently from the rest of the kernel. In order to support loadable modules, we have implemented LLVM's cross-DSO CFI support in the kernel, including a CFI shadow that speeds up cross-module look-ups. When compiled with cross-DSO support, each kernel module contains information about valid local branch targets, and the kernel looks up information from the correct module based on the target address and the modules' memory layout.

Figure 3. An example of a cross-DSO CFI check injected into an arm64 kernel. Type information is passed in X0 and the target address to validate in X1.

CFI checks naturally add some overhead to indirect branches, but due to more aggressive optimizations, our tests show that the impact is minimal, and overall system performance even improved 1-2% in many cases.

Enabling kernel CFI for an Android device

CFI for arm64 requires clang version >= 5.0 and binutils >= 2.27. The kernel build system also assumes that the LLVMgold.so plug-in is available in LD_LIBRARY_PATH. Pre-built toolchain binaries for clang and binutils are available in AOSP, but upstream binaries can also be used.

The following kernel configuration options are needed to enable kernel CFI:


Using CONFIG_CFI_PERMISSIVE=y may also prove helpful when debugging a CFI violation or during device bring-up. This option turns a violation into a warning instead of a kernel panic.

As mentioned in the previous section, the most common issue we ran into when enabling CFI on Pixel 3 were benign violations caused by function pointer type mismatches. When the kernel runs into such a violation, it prints out a runtime warning that contains the call stack at the time of the failure, and the call target that failed the CFI check. Changing the code to use a correct function pointer type fixes the issue. While we have fixed all known indirect branch type mismatches in the Android kernel, similar problems may be still found in device specific drivers, for example.

CFI failure (target: [<fffffff3e83d4d80>] my_target_function+0x0/0xd80):
------------[ cut here ]------------
kernel BUG at kernel/cfi.c:32!
Internal error: Oops - BUG: 0 [#1] PREEMPT SMP
Call trace:
[<ffffff8752d00084>] handle_cfi_failure+0x20/0x28
[<ffffff8752d00268>] my_buggy_function+0x0/0x10

Figure 4. An example of a kernel panic caused by a CFI failure.

Another potential pitfall are address space conflicts, but this should be less common in driver code. LLVM's CFI checks only understand kernel virtual addresses and any code that runs at another exception level or makes an indirect call to a physical address will result in a CFI violation. These types of failures can be addressed by disabling CFI for a single function using the __nocfi attribute, or even disabling CFI for entire code files using the $(DISABLE_CFI) compiler flag in the Makefile.

static int __nocfi address_space_conflict()
      void (*fn)(void);
/* branching to a physical address trips CFI w/o __nocfi */
 fn = (void *)__pa_symbol(function_name);

Figure 5. An example of fixing a CFI failure caused by an address space conflict.

Finally, like many hardening features, CFI can also be tripped by memory corruption errors that might otherwise result in random kernel crashes at a later time. These may be more difficult to debug, but memory debugging tools such as KASAN can help here.


We have implemented support for LLVM's CFI in Android kernels 4.9 and 4.14. Google's Pixel 3 will be the first Android device to ship with these protections, and we have made the feature available to all device vendors through the Android common kernel. If you are shipping a new arm64 device running Android 9, we strongly recommend enabling kernel CFI to help protect against kernel vulnerabilities.

LLVM's CFI protects indirect branches against attackers who manage to gain access to a function pointer stored in kernel memory. This makes a common method of exploiting the kernel more difficult. Our future work involves also protecting function return addresses from similar attacks using LLVM's Shadow Call Stack, which will be available in an upcoming compiler release.

Android and Google Play Security Rewards Programs surpass $3M in payouts

Posted by Jason Woloz and Mayank Jain, Android Security & Privacy Team

Our Android and Play security reward programs help us work with top researchers from around the world to improve Android ecosystem security every day. Thank you to all the amazing researchers who submitted vulnerability reports.

Android Security Rewards

In the ASR program's third year, we received over 470 qualifying vulnerability reports from researchers and the average pay per researcher jumped by 23%. To date, the ASR program has rewarded researchers with over $3M, paying out roughly $1M per year.

Here are some of the highlights from the Android Security Rewards program's third year:

  • There were no payouts for our highest possible reward: a complete remote exploit chain leading to TrustZone or Verified Boot compromise.
  • 99 individuals contributed one or more fixes.
  • The ASR program's reward averages were $2,600 per reward and $12,500 per researcher.
  • Guang Gong received our highest reward amount to date: $105,000 for his submission of a remote exploit chain.

As part of our ongoing commitment to security we regularly update our programs and policies based on ecosystem feedback. We also updated our severity guidelines for evaluating the impact of reported security vulnerabilities against the Android platform.

Google Play Security Rewards

In October 2017, we rolled out the Google Play Security Reward Program to encourage security research into popular Android apps available on Google Play. So far, researchers have reported over 30 vulnerabilities through the program, earning a combined bounty amount of over $100K.

If undetected, these vulnerabilities could have potentially led to elevation of privilege, access to sensitive data and remote code execution on devices.

Keeping devices secure

In addition to rewarding for vulnerabilities, we continue to work with the broad and diverse Android ecosystem to protect users from issues reported through our program. We collaborate with manufacturers to ensure that these issues are fixed on their devices through monthly security updates. Over 250 device models have a majority of their deployed devices running a security update from the last 90 days. This table shows the models with a majority of deployed devices running a security update from the last three months:

Manufacturer Device
Asus ZenFone 5Z (ZS620KL/ZS621KL), ZenFone Max Plus M1 (ZB570TL), ZenFone 4 Pro (ZS551KL), ZenFone 5 (ZE620KL), ZenFone Max M1 (ZB555KL), ZenFone 4 (ZE554KL), ZenFone 4 Selfie Pro (ZD552KL), ZenFone 3 (ZE552KL), ZenFone 3 Zoom (ZE553KL), ZenFone 3 (ZE520KL), ZenFone 3 Deluxe (ZS570KL), ZenFone 4 Selfie (ZD553KL), ZenFone Live L1 (ZA550KL), ZenFone 5 Lite (ZC600KL), ZenFone 3s Max (ZC521TL)
BlackBerry BlackBerry MOTION, BlackBerry KEY2
Blu Grand XL LTE, Vivo ONE, R2_3G, Grand_M2, BLU STUDIO J8 LTE
bq Aquaris V Plus, Aquaris V, Aquaris U2 Lite, Aquaris U2, Aquaris X, Aquaris X2, Aquaris X Pro, Aquaris U Plus, Aquaris X5 Plus, Aquaris U lite, Aquaris U
Docomo F-04K, F-05J, F-03H
Essential Products PH-1
Fujitsu F-01K
General Mobile GM8, GM8 Go
Google Pixel 2 XL, Pixel 2, Pixel XL, Pixel
HTC U12+, HTC U11+
Huawei Honor Note10, nova 3, nova 3i, Huawei Nova 3I, 荣耀9i, 华为G9青春版, Honor Play, G9青春版, P20 Pro, Honor V9, huawei nova 2, P20 lite, Honor 10, Honor 8 Pro, Honor 6X, Honor 9, nova 3e, P20, PORSCHE DESIGN HUAWEI Mate RS, FRD-L02, HUAWEI Y9 2018, Huawei Nova 2, Honor View 10, HUAWEI P20 Lite, Mate 9 Pro, Nexus 6P, HUAWEI Y5 2018, Honor V10, Mate 10 Pro, Mate 9, Honor 9, Lite, 荣耀9青春版, nova 2i, HUAWEI nova 2 Plus, P10 lite, nova 青春版本, FIG-LX1, HUAWEI G Elite Plus, HUAWEI Y7 2018, Honor 7S, HUAWEI P smart, P10, Honor 7C, 荣耀8青春版, HUAWEI Y7 Prime 2018, P10 Plus, 荣耀畅玩7X, HUAWEI Y6 2018, Mate 10 lite, Honor 7A, P9 Plus, 华为畅享8, honor 6x, HUAWEI P9 lite mini, HUAWEI GR5 2017, Mate 10
Itel P13
Kyocera X3
Lanix Alpha_950, Ilium X520
Lava Z61, Z50
LGE LG Q7+, LG G7 ThinQ, LG Stylo 4, LG K30, V30+, LG V35 ThinQ, Stylo 2 V, LG K20 V, ZONE4, LG Q7, DM-01K, Nexus 5X, LG K9, LG K11
Motorola Moto Z Play Droid, moto g(6) plus, Moto Z Droid, Moto X (4), Moto G Plus (5th Gen), Moto Z (2) Force, Moto G (5S) Plus, Moto G (5) Plus, moto g(6) play, Moto G (5S), moto e5 play, moto e(5) play, moto e(5) cruise, Moto E4, Moto Z Play, Moto G (5th Gen)
Nokia Nokia 8, Nokia 7 plus, Nokia 6.1, Nokia 8 Sirocco, Nokia X6, Nokia 3.1
OnePlus OnePlus 6, OnePlus5T, OnePlus3T, OnePlus5, OnePlus3
Oppo CPH1803, CPH1821, CPH1837, CPH1835, CPH1819, CPH1719, CPH1613, CPH1609, CPH1715, CPH1861, CPH1831, CPH1801, CPH1859, A83, R9s Plus
Positivo Twist, Twist Mini
Samsung Galaxy A8 Star, Galaxy J7 Star, Galaxy Jean, Galaxy On6, Galaxy Note9, Galaxy J3 V, Galaxy A9 Star, Galaxy J7 V, Galaxy S8 Active, Galaxy Wide3, Galaxy J3 Eclipse, Galaxy S9+, Galaxy S9, Galaxy A9 Star Lite, Galaxy J7 Refine, Galaxy J7 Max, Galaxy Wide2, Galaxy J7(2017), Galaxy S8+, Galaxy S8, Galaxy A3(2017), Galaxy Note8, Galaxy A8+(2018), Galaxy J3 Top, Galaxy J3 Emerge, Galaxy On Nxt, Galaxy J3 Achieve, Galaxy A5(2017), Galaxy J2(2016), Galaxy J7 Pop, Galaxy A6, Galaxy J7 Pro, Galaxy A6 Plus, Galaxy Grand Prime Pro, Galaxy J2 (2018), Galaxy S6 Active, Galaxy A8(2018), Galaxy J3 Pop, Galaxy J3 Mission, Galaxy S6 edge+, Galaxy Note Fan Edition, Galaxy J7 Prime, Galaxy A5(2016)
Sharp シンプルスマホ4, AQUOS sense plus (SH-M07), AQUOS R2 SH-03K, X4, AQUOS R SH-03J, AQUOS R2 SHV42, X1, AQUOS sense lite (SH-M05)
Sony Xperia XZ2 Premium, Xperia XZ2 Compact, Xperia XA2, Xperia XA2 Ultra, Xperia XZ1 Compact, Xperia XZ2, Xperia XZ Premium, Xperia XZ1, Xperia L2, Xperia X
Tecno F1, CAMON I Ace
Vestel Vestel Z20
Vivo vivo 1805, vivo 1803, V9 6GB, Y71, vivo 1802, vivo Y85A, vivo 1726, vivo 1723, V9, vivo 1808, vivo 1727, vivo 1724, vivo X9s Plus, Y55s, vivo 1725, Y66, vivo 1714, 1609, 1601
Vodafone Vodafone Smart N9
Xiaomi Mi A2, Mi A2 Lite, MI 8, MI 8 SE, MIX 2S, Redmi 6Pro, Redmi Note 5 Pro, Redmi Note 5, Mi A1, Redmi S2, MI MAX 2, MI 6X

Thank you to everyone internally and externally who helped make Android safer and stronger in the past year. Together, we made a huge investment in security research that helps Android users everywhere. If you want to get involved to make next year even better, check out our detailed program rules. For tips on how to submit complete reports, see Bug Hunter University.

Better Biometrics in Android P

Posted by Vishwath Mohan, Security Engineer

To keep users safe, most apps and devices have an authentication mechanism, or a way to prove that you're you. These mechanisms fall into three categories: knowledge factors, possession factors, and biometric factors. Knowledge factors ask for something you know (like a PIN or a password), possession factors ask for something you have (like a token generator or security key), and biometric factors ask for something you are (like your fingerprint, iris, or face).

Biometric authentication mechanisms are becoming increasingly popular, and it's easy to see why. They're faster than typing a password, easier than carrying around a separate security key, and they prevent one of the most common pitfalls of knowledge-factor based authentication—the risk of shoulder surfing.

As more devices incorporate biometric authentication to safeguard people's private information, we're improving biometrics-based authentication in Android P by:

  • Defining a better model to measure biometric security, and using that to functionally constrain weaker authentication methods.
  • Providing a common platform-provided entry point for developers to integrate biometric authentication into their apps.

A better security model for biometrics

Currently, biometric unlocks quantify their performance today with two metrics borrowed from machine learning (ML): False Accept Rate (FAR), and False Reject Rate (FRR).

In the case of biometrics, FAR measures how often a biometric model accidentally classifies an incorrect input as belonging to the target user—that is, how often another user is falsely recognized as the legitimate device owner. Similarly, FRR measures how often a biometric model accidentally classifies the user's biometric as incorrect—that is, how often a legitimate device owner has to retry their authentication. The first is a security concern, while the second is problematic for usability.

Both metrics do a great job of measuring the accuracy and precision of a given ML (or biometric) model when applied to random input samples. However, because neither metric accounts for an active attacker as part of the threat model, they do not provide very useful information about its resilience against attacks.

In Android 8.1, we introduced two new metrics that more explicitly account for an attacker in the threat model: Spoof Accept Rate (SAR) and Imposter Accept Rate (IAR). As their names suggest, these metrics measure how easily an attacker can bypass a biometric authentication scheme. Spoofing refers to the use of a known-good recording (e.g. replaying a voice recording or using a face or fingerprint picture), while impostor acceptance means a successful mimicking of another user's biometric (e.g. trying to sound or look like a target user).

Strong vs. Weak Biometrics

We use the SAR/IAR metrics to categorize biometric authentication mechanisms as either strong or weak. Biometric authentication mechanisms with an SAR/IAR of 7% or lower are strong, and anything above 7% is weak. Why 7% specifically? Most fingerprint implementations have a SAR/IAR metric of about 7%, making this an appropriate standard to start with for other modalities as well. As biometric sensors and classification methods improve, this threshold can potentially be decreased in the future.

This binary classification is a slight oversimplification of the range of security that different implementations provide. However, it gives us a scalable mechanism (via the tiered authentication model) to appropriately scope the capabilities and the constraints of different biometric implementations across the ecosystem, based on the overall risk they pose.

While both strong and weak biometrics will be allowed to unlock a device, weak biometrics:

  • require the user to re-enter their primary PIN, pattern, password or a strong biometric to unlock a device after a 4-hour window of inactivity, such as when left at a desk or charger. This is in addition to the 72-hour timeout that is enforced for both strong and weak biometrics.
  • are not supported by the forthcoming BiometricPrompt API, a common API for app developers to securely authenticate users on a device in a modality-agnostic way.
  • can't authenticate payments or participate in other transactions that involve a KeyStore auth-bound key.
  • must show users a warning that articulates the risks of using the biometric before it can be enabled.

These measures are intended to allow weaker biometrics, while reducing the risk of unauthorized access.

BiometricPrompt API

Starting in Android P, developers can use the BiometricPrompt API to integrate biometric authentication into their apps in a device and biometric agnostic way. BiometricPrompt only exposes strong modalities, so developers can be assured of a consistent level of security across all devices their application runs on. A support library is also provided for devices running Android O and earlier, allowing applications to utilize the advantages of this API across more devices .

Here's a high-level architecture of BiometricPrompt.

The API is intended to be easy to use, allowing the platform to select an appropriate biometric to authenticate with instead of forcing app developers to implement this logic themselves. Here's an example of how a developer might use it in their app:


Biometrics have the potential to both simplify and strengthen how we authenticate our digital identity, but only if they are designed securely, measured accurately, and implemented in a privacy-preserving manner.

We want Android to get it right across all three. So we're combining secure design principles, a more attacker-aware measurement methodology, and a common, easy to use biometrics API that allows developers to integrate authentication in a simple, consistent, and safe manner.

Acknowledgements: This post was developed in joint collaboration with Jim Miller

Keeping 2 billion Android devices safe with machine learning

Posted by Sai Deep Tetali, Software Engineer, Google Play Protect

At Google I/O 2017, we introduced Google Play Protect, our comprehensive set of security services for Android. While the name is new, the smarts powering Play Protect have protected Android users for years.

Google Play Protect's suite of mobile threat protections are built into more than 2 billion Android devices, automatically taking action in the background. We're constantly updating these protections so you don't have to think about security: it just happens. Our protections have been made even smarter by adding machine learning elements to Google Play Protect.

Security at scale

Google Play Protect provides in-the-moment protection from potentially harmful apps (PHAs), but Google's protections start earlier.

Before they're published in Google Play, all apps are rigorously analyzed by our security systems and Android security experts. Thanks to this process, Android devices that only download apps from Google Play are 9 times less likely to get a PHA than devices that download apps from other sources.

After you install an app, Google Play Protect continues its quest to keep your device safe by regularly scanning your device to make sure all apps are behaving properly. If it finds an app that is misbehaving, Google Play Protect either notifies you, or simply removes the harmful app to keep your device safe.

Our systems scan over 50 billion apps every day. To keep on the cutting edge of security, we look for new risks in a variety of ways, such as identifying specific code paths that signify bad behavior, investigating behavior patterns to correlate bad apps, and reviewing possible PHAs with our security experts.

In 2016, we added machine learning as a new detection mechanism and it soon became a critical part of our systems and tools.

Training our machines

In the most basic terms, machine learning means training a computer algorithm to recognize a behavior. To train the algorithm, we give it hundreds of thousands of examples of that behavior.

In the case of Google Play Protect, we are developing algorithms that learn which apps are "potentially harmful" and which are "safe." To learn about PHAs, the machine learning algorithms analyze our entire catalog of applications. Then our algorithms look at hundreds of signals combined with anonymized data to compare app behavior across the Android ecosystem to find PHAs. They look for behavior common to PHAs, such as apps that attempt to interact with other apps on the device, access or share your personal data, download something without your knowledge, connect to phishing websites, or bypass built-in security features.

When we find apps exhibit similar malicious behavior, we group them into families. Visualizing these PHA families helps us uncover apps that share similarities to known bad apps, but have yet remained under our radar.

After we identify a new PHA, we confirm our findings with expert security reviews. If the app in question is a PHA, Google Play Protect takes action on the app and then we feed information about that PHA back into our algorithms to help find more PHAs.

Doubling down on security

So far, our machine learning systems have successfully detected 60.3% of the malware identified by Google Play Protect in 2017.

In 2018, we're devoting a massive amount of computing power and talent to create, maintain and improve these machine learning algorithms. We're constantly leveraging artificial intelligence and our highly skilled researchers and engineers from all across Google to find new ways to keep Android devices safe and secure. In addition to our talented team, we work with the foremost security experts and researchers from around the world. These researchers contribute even more data and insights to keep Google Play Protect on the cutting edge of mobile security.

To check out Google Play Protect, open the Google Play app and tap Play Protect in the left panel.

Acknowledgements: This work was developed in joint collaboration with Google Play Protect, Safe Browsing and Play Abuse teams with contributions from Andrew Ahn, Hrishikesh Aradhye, Daniel Bali, Hongji Bao, Yajie Hu, Arthur Kaiser, Elena Kovakina, Salvador Mandujano, Melinda Miller, Rahul Mishra, Damien Octeau, Sebastian Porst, Chuangang Ren, Monirul Sharif, Sri Somanchi, Sai Deep Tetali, Zhikun Wang, and Mo Yu.

Protecting WebView with Safe Browsing

Posted by Nate Fischer, Software Engineer

Since 2007, Google Safe Browsing has been protecting users across the web from phishing and malware attacks. It protects over three billion devices from an increasing number of threats, now also including unwanted software across desktop and mobile platforms. Today, we're announcing that Google Play Protect is bringing Safe Browsing to WebView by default, starting in April 2018 with the release of WebView 66.

Developers of Android apps using WebView no longer have to make any changes to benefit from this protection. Safe Browsing in WebView has been available since Android 8.0 (API level 26), using the same underlying technology as Chrome on Android. When Safe Browsing is triggered, the app will present a warning and receive a network error. Apps built for API level 27 and above can customize this behavior with new APIs for Safe Browsing.

An example of a warning shown when Safe Browsing detects a dangerous site. The style and content of the warning will vary depending on the size of the WebView.

You can learn more about customizing and controlling Safe Browsing in the Android API documentation, and you can test your application today by visiting the Safe Browsing test URL (chrome://safe-browsing/match?type=malware) while using the current WebView beta.

DNS over TLS support in Android P Developer Preview

Posted by Erik Kline, Android software engineer, and Ben Schwartz, Jigsaw software engineer

The first step of almost every connection on the internet is a DNS query. A client, such as a smartphone, typically uses a DNS server provided by the Wi-Fi or cellular network. The client asks this DNS server to convert a domain name, like www.google.com, into an IP address, like 2607:f8b0:4006:80e::2004. Once the client has the IP address, it can connect to its intended destination.

When the DNS protocol was designed in the 1980s, the internet was a much smaller, simpler place. For the past few years, the Internet Engineering Task Force (IETF) has worked to define a new DNS protocol that provides users with the latest protections for security and privacy. The protocol is called "DNS over TLS" (standardized as RFC 7858).

Like HTTPS, DNS over TLS uses the TLS protocol to establish a secure channel to the server. Once the secure channel is established, DNS queries and responses can't be read or modified by anyone else who might be monitoring the connection. (The secure channel only applies to DNS, so it can't protect users from other kinds of security and privacy violations.)

DNS over TLS in P

The Android P Developer Preview includes built-in support for DNS over TLS. We added a Private DNS mode to the Network & internet settings.

By default, devices automatically upgrade to DNS over TLS if a network's DNS server supports it. But users who don't want to use DNS over TLS can turn it off.

Users can enter a hostname if they want to use a private DNS provider. Android then sends all DNS queries over a secure channel to this server or marks the network as "No internet access" if it can't reach the server. (For testing purposes, see this community-maintained list of compatible servers.)

DNS over TLS mode automatically secures the DNS queries from all apps on the system. However, apps that perform their own DNS queries, instead of using the system's APIs, must ensure that they do not send insecure DNS queries when the system has a secure connection. Apps can get this information using a new API: LinkProperties.isPrivateDnsActive().

With the Android P Developer Preview, we're proud to present built-in support for DNS over TLS. In the future, we hope that all operating systems will include secure transports for DNS, to provide better protection and privacy for all users on every new connection.

Protecting users with TLS by default in Android P

Posted by Chad Brubaker, Senior Software Engineer Android Security

Android is committed to keeping users, their devices, and their data safe. One of the ways that we keep data safe is by protecting all data that enters or leaves an Android device with Transport Layer Security (TLS) in transit. As we announced in our Android P developer preview, we're further improving these protections by preventing apps that target Android P from allowing unencrypted connections by default.

This follows a variety of changes we've made over the years to better protect Android users.To prevent accidental unencrypted connections, we introduced the android:usesCleartextTraffic manifest attribute in Android Marshmallow. In Android Nougat, we extended that attribute by creating the Network Security Config feature, which allows apps to indicate that they do not intend to send network traffic without encryption. In Android Nougat and Oreo, we still allowed cleartext connections.

How do I update my app?

If your app uses TLS for all connections then you have nothing to do. If not, update your app to use TLS to encrypt all connections. If you still need to make cleartext connections, keep reading for some best practices.

Why should I use TLS?

Android considers all networks potentially hostile and so encrypting traffic should be used at all times, for all connections. Mobile devices are especially at risk because they regularly connect to many different networks, such as the Wi-Fi at a coffee shop.

All traffic should be encrypted, regardless of content, as any unencrypted connections can be used to inject content, increase attack surface for potentially vulnerable client code, or track the user. For more information, see our past blog post and Developer Summit talk.

Isn't TLS slow?

No, it's not.

How do I use TLS in my app?

Once your server supports TLS, simply change the URLs in your app and server responses from http:// to https://. Your HTTP stack handles the TLS handshake without any more work.

If you are making sockets yourself, use an SSLSocketFactory instead of a SocketFactory. Take extra care to use the socket correctly as SSLSocket doesn't perform hostname verification. Your app needs to do its own hostname verification, preferably by calling getDefaultHostnameVerifier() with the expected hostname. Further, beware that HostnameVerifier.verify() doesn't throw an exception on error but instead returns a boolean result that you must explicitly check.

I need to use cleartext traffic to...

While you should use TLS for all connections, it's possibly that you need to use cleartext traffic for legacy reasons, such as connecting to some servers. To do this, change your app's network security config to allow those connections.

We've included a couple example configurations. See the network security config documentation for a bit more help.

Allow cleartext connections to a specific domain

If you need to allow connections to a specific domain or set of domains, you can use the following config as a guide:

    <domain-config cleartextTrafficPermitted="true">
        <domain includeSubdomains="true">insecure.example.com</domain>
        <domain includeSubdomains="true">insecure.cdn.example.com</domain>

Allow connections to arbitrary insecure domains

If your app supports opening arbitrary content from URLs over insecure connections, you should disable cleartext connections to your own services while supporting cleartext connections to arbitrary hosts. Keep in mind that you should be cautious about the data received over insecure connections as it could have been tampered with in transit.

    <domain-config cleartextTrafficPermitted="false">
        <domain includeSubdomains="true">example.com</domain>
        <domain includeSubdomains="true">cdn.example2.com</domain>
    <base-config cleartextTrafficPermitted="true" />

How do I update my library?

If your library directly creates secure/insecure connections, make sure that it honors the app's cleartext settings by checking isCleartextTrafficPermitted before opening any cleartext connection.