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To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10)

TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

William Enck The Pennsylvania State University

Peter Gilbert Duke University

Byung-Gon Chun Intel Labs

Landon P. Cox Duke University

Jaeyeon Jung Intel Labs

Patrick McDaniel The Pennsylvania State University

Anmol N. Sheth Intel Labs

Abstract Today’s smartphone operating systems frequently fail

to provide users with adequate control over and visibility into how third-party applications use their private data. We address these shortcomings with TaintDroid, an ef- ficient, system-wide dynamic taint tracking and analy- sis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 14% performance overhead on a CPU-bound micro-benchmark and im- poses negligible overhead on interactive third-party ap- plications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users’ private infor- mation across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party ap- plications for phone users and valuable input for smart- phone security service firms seeking to identify misbe- having applications.

1 Introduction A key feature of modern smartphone platforms is a

centralized service for downloading third-party applica- tions. The convenience to users and developers of such “app stores” has made mobile devices more fun and use- ful, and has led to an explosion of development. Apple’s App Store alone served nearly 3 billion applications af- ter only 18 months [4]. Many of these applications com- bine data from remote cloud services with information from local sensors such as a GPS receiver, camera, mi- crophone, and accelerometer. Applications often have le- gitimate reasons for accessing this privacy sensitive data, but users would also like assurances that their data is used properly. Incidents of developers relaying private infor- mation back to the cloud [35, 12] and the privacy risks posed by seemingly innocent sensors like accelerome- ters [19] illustrate the danger.

Resolving the tension between the fun and utility of running third-party mobile applications and the privacy risks they pose is a critical challenge for smartphone plat- forms. Mobile-phone operating systems currently pro- vide only coarse-grained controls for regulating whether an application can access private information, but pro- vide little insight into how private information is actu- ally used. For example, if a user allows an application to access her location information, she has no way of knowing if the application will send her location to a location-based service, to advertisers, to the application developer, or to any other entity. As a result, users must blindly trust that applications will properly handle their private data.

This paper describes TaintDroid, an extension to the Android mobile-phone platform that tracks the flow of privacy sensitive data through third-party applications. TaintDroid assumes that downloaded, third-party appli- cations are not trusted, and monitors–in realtime–how these applications access and manipulate users’ personal data. Our primary goals are to detect when sensitive data leaves the system via untrusted applications and to facil- itate analysis of applications by phone users or external security services [33, 55].

Analysis of applications’ behavior requires sufficient contextual information about what data leaves a device and where it is sent. Thus, TaintDroid automatically labels (taints) data from privacy-sensitive sources and transitively applies labels as sensitive data propagates through program variables, files, and interprocess mes- sages. When tainted data are transmitted over the net- work, or otherwise leave the system, TaintDroid logs the data’s labels, the application responsible for transmitting the data, and the data’s destination. Such realtime feed- back gives users and security services greater insight into what mobile applications are doing, and can potentially identify misbehaving applications.

To be practical, the performance overhead of the Taint- Droid runtime must be minimal. Unlike existing so-

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10) lutions that rely on heavy-weight whole-system emula- tion [7, 57], we leveraged Android’s virtualized archi- tecture to integrate four granularities of taint propaga- tion: variable-level, method-level, message-level, and file-level. Though the individual techniques are not new, our contributions lie in the integration of these techniques and in identifying an appropriate trade-off between performance and accuracy for resource con- strained smartphones. Experiments with our prototype for Android show that tracking incurs a runtime over- head of less than 14% for a CPU-bound microbench- mark. More importantly, interactive third-party applica- tions can be monitored with negligible perceived latency.

We evaluated the accuracy of TaintDroid using 30 ran- domly selected, popular Android applications that use lo- cation, camera, or microphone data. TaintDroid correctly flagged 105 instances in which these applications trans- mitted tainted data; of the 105, we determined that 37 were clearly legitimate. TaintDroid also revealed that 15 of the 30 applications reported users’ locations to remote advertising servers. Seven applications collected the de- vice ID and, in some cases, the phone number and the SIM card serial number. In all, two-thirds of the applica- tions in our study used sensitive data suspiciously. Our findings demonstrate that TaintDroid can help expose po- tential misbehavior by third-party applications.

Like similar information-flow tracking systems [7, 57], a fundamental limitation of TaintDroid is that it can be circumvented through leaks via implicit flows. The use of implicit flows to avoid taint detection is, in and of itself, an indicator of malicious intent, and may well be detectable through other techniques such as automated static code analysis [14, 46] as we discuss in Section 8.

The rest of this paper is organized as follows: Sec- tion 2 provides a high-level overview of TaintDroid, Sec- tion 3 describes background information on the Android platform, Section 4 describes our TaintDroid design, Section 5 describes the taint sources tracked by Taint- Droid, Section 6 presents results from our Android ap- plication study, Section 7 characterizes the performance of our prototype implementation, Section 8 discusses the limitations of our approach, Section 9 describes related work, and Section 10 summarizes our conclusions.

2 Approach Overview We seek to design a framework that allows users to

monitor how third-party smartphone applications handle their private data in realtime. Many smartphone appli- cations are closed-source, therefore, static source code analysis is infeasible. Even if source code is available, runtime events and configuration often dictate informa- tion use; realtime monitoring accounts for these environ- ment specific dependencies.

Monitoring network disclosure of privacy sensitive in- formation on smartphones presents several challenges:

• Smartphones are resource constrained. The re- source limitations of smartphones precludes the use of heavyweight information tracking systems such as Panorama [57].

• Third-party applications are entrusted with several types of privacy sensitive information. The mon- itoring system must distinguish multiple informa- tion types, which requires additional computation and storage.

• Context-based privacy sensitive information is dy- namic and can be difficult to identify even when sent in the clear. For example, geographic locations are pairs of floating point numbers that frequently change and are hard to predict.

• Applications can share information. Limiting the monitoring system to a single application does not account for flows via files and IPC between applica- tions, including core system applications designed to disseminate privacy sensitive information.

We use dynamic taint analysis [57, 44, 8, 61, 39] (also called “taint tracking”) to monitor privacy sensitive in- formation on smartphones. Sensitive information is first identified at a taint source, where a taint marking indi- cating the information type is assigned. Dynamic taint analysis tracks how labeled data impacts other data in a way that might leak the original sensitive information. This tracking is often performed at the instruction level. Finally, the impacted data is identified before it leaves the system at a taint sink (usually the network interface).

Existing taint tracking approaches have several lim- itations. First and foremost, approaches that rely on instruction-level dynamic taint analysis using whole sys- tem emulation [57, 7, 26] incur high performance penal- ties. Instruction-level instrumentation incurs 2-20 times slowdown [57, 7] in addition to the slowdown introduced by emulation, which is not suitable for realtime analysis. Second, developing accurate taint propagation logic has proven challenging for the x86 instruction set [40, 48]. Implementations of instruction-level tracking can experi- ence taint explosion if the stack pointer becomes falsely tainted [49] and taint loss if complicated instructions such as CMPXCHG, REP MOV are not instrumented properly [61]. While most smartphones use the ARM instruction set, similar false positives and false negatives could arise.

Figure 1 presents our approach to taint tracking on smartphones. We leverage architectural features of vir- tual machine-based smartphones (e.g., Android, Black- Berry, and J2ME-based phones) to enable efficient, system-wide taint tracking using fine-grained labels with

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10)

Network Interface

Native System Libraries

Virtual Machine

Application Code Application CodeMsg

Secondary Storage

Message-level tracking

Variable-level tracking

Method-level tracking

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Figure 1: Multi-level approach for performance efficient taint tracking within a common smartphone architecture.

clear semantics. First, we instrument the VM interpreter to provide variable-level tracking within untrusted ap- plication code.1 Using variable semantics provided by the interpreter provides valuable context for avoiding the taint explosion observed in the x86 instruction set. Additionally, by tracking variables, we maintain taint markings only for data and not code. Second, we use message-level tracking between applications. Tracking taint on messages instead of data within messages mini- mizes IPC overhead while extending the analysis system- wide. Third, for system-provided native libraries, we use method-level tracking. Here, we run native code with- out instrumentation and patch the taint propagation on return. These methods accompany the system and have known information flow semantics. Finally, we use file- level tracking to ensure persistent information conserva- tively retains its taint markings.

To assign labels, we take advantage of the well- defined interfaces through which applications access sen- sitive data. For example, all information retrieved from GPS hardware is location-sensitive, and all informa- tion retrieved from an address book database is contact- sensitive. This avoids relying on heuristics [10] or man- ual specification [61] for labels. We expand on informa- tion sources in Section 5.

In order to achieve this tracking at multiple granulari- ties, our approach relies on the firmware’s integrity. The taint tracking system’s trusted computing base includes the virtual machine executing in userspace and any na- tive system libraries loaded by the untrusted interpreted application. However, this code is part of the firmware, and is therefore trusted. Applications can only escape the virtual machine by executing native methods. In our target platform (Android), we modified the native library loader to ensure that applications can only load native li- braries from the firmware and not those downloaded by the application. Note that an early 2010 survey of the top 50 most popular free applications in each category of the Android Market [2] (1100 applications in total) revealed that less than 4% included a .so file. A similar survey conducted in mid 2010 revealed this fraction increased to 5%, which indicates there is growth in the number of ap-

plications using native third-party libraries, but that the number of affected applications remains small.

In summary, we provide a novel, efficient, system- wide, multiple-marking, taint tracking design by com- bining multiple granularities of information tracking. While some techniques such as variable tracking within an interpreter have been previously proposed (see Sec- tion 9), to our knowledge, our approach is the first to extend such tracking system-wide. By choosing a mul- tiple granularity approach, we balance performance and accuracy. As we show in Sections 6 and 7, our system- wide approach is both highly efficient (∼14% CPU over- head and ∼4.4% memory overhead for simultaneously tracking 32 taint markings per data unit) and accurately detects many suspicious network packets.

3 Background: Android Android [1] is a Linux-based, open source, mobile

phone platform. Most core phone functionality is imple- mented as applications running on top of a customized middleware. The middleware itself is written in Java and C/C++. Applications are written in Java and com- piled to a custom byte-code known as the Dalvik EXe- cutable (DEX) byte-code format. Each application exe- cutes within its Dalvik VM interpreter instance. Each in- stance executes as unique UNIX user identities to isolate applications within the Linux platform subsystem. Ap- plications communicate via the binder IPC mechanism. Binder provides transparent message passing based on parcels. We now discuss topics necessary to understand our tracking system. Dalvik VM Interpreter: DEX is a register-based ma- chine language, as opposed to Java byte-code, which is stack-based. Each DEX method has its own predefined number of virtual registers (which we frequently refer to as simply “registers”). The Dalvik VM interpreter man- ages method registers with an internal execution state stack; the current method’s registers are always on the top stack frame. These registers loosely correspond to local variables in the Java method and store primitive types and object references. All computation occurs on registers, therefore values must be loaded from and stored to class fields before use and after use. Note that DEX uses class fields for all long term storage, unlike hardware register-based machine languages (e.g., x86), which store values in arbitrary memory locations. Native Methods: The Android middleware provides ac- cess to native libraries for performance optimization and third-party libraries such as OpenGL and Webkit. An- droid also uses Apache Harmony Java [3], which fre- quently uses system libraries (e.g., math routines). Na- tive methods are written in C/C++ and expose function- ality provided by the underlying Linux kernel and ser- vices. They can also access Java internals, and hence are

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10)

Dalvik VM Interpreter

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Native Methods. [WHE: say a little about how Dalvik creates a byte-array of arguments that is passed. internal VM vs JNI. significanlty more JNI than internal VM (more internal VM methods is unlikely). mention call bridge] Android contains two types of native methods: internal VM methods and JNI methods. The internal VM methods access interpreter specific structures and APIs, whereas JNI methods conform to Java native interface standards specifications [cite]. The specifications include passing Java arguments to JNI methods as separate variables, which is performed automatically by a call bridge in Dalvik. Internal VM methods do not have this luxury and manually parse arguments from a byte array of arguments created by the interpreter.

Android’s middleware Java libraries make frequent use of the Java Native Interface (JNI). The native methods are written in C and C++ and expose the POSIX functionality provided by the underlying Linux kernel and services. An- droid uses the Apache Harmony implementation of Java [12] for base Java functionality in the Dalvik VM. Portions of the Apache Harmony implementation wraps system libraries (e.g., math libraries) to provide functionality. The Android binder and parcel interfaces also make use of JNI. Fur- thermore, Android uses JNI to includes Java interfaces to third party libraries such as OpenGL and Webkit. Finally, Android provides the Native Development Toolkit (NDK) to allow third party application developers to implement and package native libraries with downloaded applications. However, NDK use is strongly discouraged, as it impedes application portability on a platform that runs on different instruction set architectures, including ARM and x86. The NDK is primarily seen as a means of providing better runtime performance.

IV. TAINTDROID ARCHITECTURE

TaintDroid is a system that performs system-wide taint tracking built upon Android. Figure 2 shows TaintDroid architecture. TaintDroid propagates taint tags within an application and between applications.

The goal of TaintDroid is to perform taint to tracking to enforce security polices to untrusted third-party applications. For correct taint tracking, TaintDroid’s trusted computing base includes the firmware, including all system applica- tions and libraries provided by the stock Android distribu- tion. Similar assumptions are made by other taint tracking systems, e.g., Panorama [4]. In addition, we assume all downloaded (i.e., unknown) code executes within the Dalvik VM. We do not allow execution of downloaded native code, which do not propagate taint tags or may maliciously modify taint tag storage.

Figure 2 shows an example of taint tracking in TaintDroid. Information is tainted (1) in a trusted application with sufficient context (e.g., the location provider). The taint interface invokes a native method (2) that interfaces with the

Trusted Application Untrusted Application

JNI Hook Binder Hook

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      �

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Figure 2. TaintDroid Architecture

Dalvik VM interpreter, storing the specified taint marking(s) in the virtual taint map. As the trusted application uses the tainted information, the Dalvik VM propagates taint tags (3) according to our data flow rules. When the trusted ap- plication uses the tainted information in an IPC transaction, the modified binder library (4) ensures the parcel message carries a taint tag reflecting the combined taint markings of all contained data. The parcel is passed transparently through the kernel (5) and received by the remote untrusted application. Note that the third-party interpreted code is untrusted. The modified binder library retrieves the taint tag from the parcel and assigns it to all values read from the parcel (6). The remote Dalvik VM instance propagates taint tags (7) identically for the untrusted application. When the untrusted application invokes a library specified as a taint sink (8), e.g., sending a data buffer over the network, the library retrieves the taint tag for the data in question (9-11) and makes a policy decision.

At a high level, TaintDroid architecture enables system- wide tracking by combining execution taint tracking, IPC taint tracking, native interface taint tracking, and secondary storage taint tracking. Variable-level taint tracking While previous approaches such as Panorama [panorama] and TaintBochs [taintbochs] provide high-accuracy taint tracking via instruction-level taint propagation, performance is sacrificed. On the other end of the spectrum, approaches such as PRECIP [precip] consider only high-level system calls into the kernel, trading off accuracy for performance; thus, they provide only nomi- nal advantage over OS permissions (e.g., those implemented in Android).

In TaintDroid, we choose a middle ground, variable- level taint tracking. TaintDroid is designed to taint primitive type variables (e.g., int, float, etc). Our taint source and sink libraries (Section VI) provide an easy interface to set and check the taint markings on primitive types. However, there are cases when object references must become tainted to ensure taint propagation operates correctly. Applications are compiled into the Dalvik EXecutable (DEX) byte-code format. Dalvik, unlike the stack-based virtual machine Java,

IV. TAINTDROID ARCHITECTURE

Trusted Application Untrusted Application

JNI Hook Binder Hook

DVM InterpreterDVM Intepreter

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      �

In t e r p r e t e d

C o d e

U s e r s p a c e

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Figure 2. TaintDroid Architecture

IV. TAINTDROID ARCHITECTURE

Trusted Application Untrusted Application

JNI Hook Binder Hook

DVM InterpreterDVM Intepreter

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      �

In te

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Figure 2. TaintDroid Architecture

In te

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Figure 2: TaintDroid architecture within Android.

included in our trusted computing base (see Section 2). Android contains two types of native methods: inter-

nal VM methods and JNI methods. The internal VM methods access interpreter-specific structures and APIs. JNI methods conform to Java native interface standards specifications [32], which requires Dalvik to separate Java arguments into variables using a JNI call bridge. Conversely, internal VM methods must manually parse arguments from the interpreter’s byte array of arguments.

Binder IPC: All Android IPC occurs through binder. Binder is a component-based processing and IPC frame- work designed for BeOS, extended by Palm Inc., and customized for Android by Google. Fundamental to binder are parcels, which serialize both active and stan- dard data objects. The former includes references to binder objects, which allows the framework to manage shared data objects between processes. A binder kernel module passes parcel messages between processes.

4 TaintDroid TaintDroid is a realization of our multiple granularity

taint tracking approach within Android. TaintDroid uses variable-level tracking within the VM interpreter. Mul- tiple taint markings are stored as one taint tag. When applications execute native methods, variable taint tags are patched on return. Finally, taint tags are assigned to parcels and propagated through binder. Note that the Technical Report [17] version of this paper contains more implementation details.

Figure 2 depicts TaintDroid’s architecture. Informa- tion is tainted (1) in a trusted application with sufficient context (e.g., the location provider). The taint inter- face invokes a native method (2) that interfaces with the Dalvik VM interpreter, storing specified taint markings in the virtual taint map. The Dalvik VM propagates taint tags (3) according to data flow rules as the trusted ap- plication uses the tainted information. Every interpreter instance simultaneously propagates taint tags. When the trusted application uses the tainted information in an IPC

transaction, the modified binder library (4) ensures the parcel has a taint tag reflecting the combined taint mark- ings of all contained data. The parcel is passed transpar- ently through the kernel (5) and received by the remote untrusted application. Note that only the interpreted code is untrusted. The modified binder library retrieves the taint tag from the parcel and assigns it to all values read from it (6). The remote Dalvik VM instance propagates taint tags (7) identically for the untrusted application. When the untrusted application invokes a library spec- ified as a taint sink (8), e.g., network send, the library retrieves the taint tag for the data in question (9) and re- ports the event.

Implementing this architecture requires addressing several system challenges, including: a) taint tag stor- age, b) interpreted code taint propagation, c) native code taint propagation, d) IPC taint propagation, and e) sec- ondary storage taint propagation. The remainder of this section describes our design.

4.1 Taint Tag Storage The choice of how to store taint tags influences per-

formance and memory overhead. Dynamic taint track- ing systems commonly store tags for every data byte or word [57, 7]. Tracked memory is unstructured and with- out content semantics. Frequently taint tags are stored in non-adjacent shadow memory [57] and tag maps [61]. TaintDroid uses variable semantics within the Dalvik in- terpreter. We store taint tags adjacent to variables in memory, providing spatial locality.

Dalvik has five variable types that require taint stor- age: method local variables, method arguments, class static fields, class instance fields, and arrays. In all cases, we store a 32-bit bitvector with each variable to encode the taint tag, allowing 32 different taint markings.

Dalvik stores method local variables and arguments on an internal stack. When an application invokes a method, a new stack frame is allocated for all local vari- ables. Method arguments are also passed via the internal stack. Before calling a method, the callee places the ar- guments on the top of the stack such that they become high numbered registers in the callee’s stack frame. We allocate taint tag storage by doubling the size of the stack frame allocation. Taint tags are interleaved between val- ues such that register vi originally accessed via fp[i] is accessed as fp[2 · i] after modification. Note that Dalvik stores 64-bit variables as two adjacent 32-bit registers on the internal stack. While the byte-code interprets these adjacent registers as a single 64-bit value, the interpreter manages these registers as separate values. Therefore, our modified stack transparently stores and retrieves 64- bit values to and from separate 32-bit registers (at fp[2·i] and fp[2 · i + 2]). Finally, native method targets require a slightly different stack frame organization for reasons

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10)

out1 taint tag (unused)

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v0 == local0 v0 taint tag v1 == local1 v1 taint tag v2 == in0

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variable variable taint tag

Figure 3: Modified Stack Format. Taint tags are inter- leaved between registers for interpreted method targets and appended for native methods. Dark grayed boxes represent taint tags.

discussed in Section 4.3. The modified stack format is shown in Figure 3.

Taint tags are stored adjacent to class fields and ar- rays inside the VM interpreter’s internal data structures. TaintDroid stores only one taint tag per array to minimize storage overhead. Per-value taint tag storage is severely inefficient for Java String objects, as all characters have the same tag. Unfortunately, storing one taint tag per ar- ray may result in false positives during taint propagation. For example, if untainted variable u is stored into array A at index 0 (A[0]) and tainted variable t is stored into A[1], then array A is tainted. Later, if variable v is assigned to A[0], v will be tainted, even though u was untainted. Fortunately, Java frequently uses objects, and object ref- erences are infrequently tainted (see Section 4.2), there- fore this coding practice leads to less false positives.

4.2 Interpreted Code Taint Propagation Taint tracking granularity and flow semantics influ-

ence performance and accuracy. TaintDroid implements variable-level taint tracking within the Dalvik VM in- terpreter. Variables provide valuable semantics for taint propagation, distinguishing data pointers from scalar val- ues. TaintDroid primarily tracks primitive type variables (e.g., int, float, etc); however, there are cases when object references must become tainted to ensure taint propaga- tion operates correctly; this section addresses why these cases exist. However, first we present taint tracking in the Dalvik machine language as a formal logic.

4.2.1 Taint Propagation Logic

The Dalvik VM operates on the unique DEX machine language instruction set, therefore we must design an ap-

propriate propagation logic. We use a data flow logic, as tracking implicit flows requires static analysis and causes significant performance overhead and overestimation in tracking [29] (see Section 8). We begin by defining taint markings, taint tags, variables, and taint propagation. We then present our logic rules for DEX.

Let L be the universe of taint markings for a particular system. A taint tag t is a set of taint markings, t ⊆ L. Each variable has an associated taint tag. A variable is an instance of one of the five types described in Section 4.1. We use a different representation for each type. The local and argument variables correspond to virtual registers, denoted vx. Class field variables are denoted as fx to in- dicate a field variable with class index x. Instance fields require an instance object and are denoted vy(fx), where vy is the instance object reference (note that both the ob- ject reference and the dereferenced value are variables). Static fields are denoted as fx alone, which is shorthand for S(fx), where S() is the static scope. Finally, vx[·] denotes an array, where vx is an array object reference variable.

Our virtual taint map function is τ(·). τ(v) returns the taint tag t for variable v. τ(v) is also used to assign a taint tag to a variable. Retrieval and assignment are dis- tinguished by the position of τ(·) w.r.t. the ← symbol. When τ(v) appears on the right hand side of ←, τ(v) re- trieves the taint tag for v. When τ(v) appears on the left hand side, τ(v) assigns the taint tag for v. For example, τ(v1) ← τ(v2) copies the taint tag from v2 to v1.

Table 1 captures our propagation logic. The table enu- merates abstracted versions of the byte-code instructions specified in the DEX documentation. Register variables and class fields are referenced by vX and fX , respec- tively. R and E are the return and exception variables maintained within the interpreter, respectively. A, B, and C are constants in the byte-code. The table does not list instructions that clear the taint tag of the destination reg- ister. For example, we do not consider the array-length instruction to return a tainted value even if the array is tainted. Note that the array length is sometimes used to aid direct control flow propagation (e.g., Vogt et al. [53]).

4.2.2 Tainting Object References

The propagation rules in Table 1 are straightforward with two exceptions. First, taint propagation logics com- monly include the taint tag of an array index during lookup to handle translation tables (e.g., ASCII/UNI- CODE or character case conversion). For example, con- sider a translation table from lowercase to upper case characters: if a tainted value “a” is used as an array index, the resulting “A” value should be tainted even though the “A” value in the array is not. Hence, the taint logic for aget-op uses both the array and array index taint. Sec- ond, when the array contains object references (e.g., an

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10)

Table 1: DEX Taint Propagation Logic. Register variables and class fields are referenced by vX and fX , respectively. R and E are the return and exception variables maintained within the interpreter. A, B, and C are byte-code constants.

Op Format Op Semantics Taint Propagation Description const-op vA C vA ← C τ(vA) ←∅ Clear vA taint move-op vA vB vA ← vB τ(vA) ← τ(vB) Set vA taint to vB taint move-op-R vA vA ← R τ(vA) ← τ(R) Set vA taint to return taint return-op vA R ← vA τ(R) ← τ(vA) Set return taint (∅ if void) move-op-E vA vA ← E τ(vA) ← τ(E) Set vA taint to exception taint throw-op vA E ← vA τ(E) ← τ(vA) Set exception taint unary-op vA vB vA ←⊗vB τ(vA) ← τ(vB) Set vA taint to vB taint binary-op vA vB vC vA ← vB ⊗vC τ(vA) ← τ(vB) ∪ τ(vC) Set vA taint to vB taint ∪vC taint binary-op vA vB vA ← vA ⊗vB τ(vA) ← τ(vA) ∪ τ(vB) Update vA taint with vB taint binary-op vA vB C vA ← vB ⊗C τ(vA) ← τ(vB) Set vA taint to vB taint aput-op vA vB vC vB[vC] ← vA τ(vB[·]) ← τ(vB[·]) ∪ τ(vA) Update array vB taint with vA taint aget-op vA vB vC vA ← vB[vC] τ(vA) ← τ(vB[·]) ∪ τ(vC) Set vA taint to array and index taint sput-op vA fB fB ← vA τ(fB) ← τ(vA) Set field fB taint to vA taint sget-op vA fB vA ← fB τ(vA) ← τ(fB) Set vA taint to field fB taint iput-op vA vB fC vB(fC) ← vA τ(vB(fC)) ← τ(vA) Set field fC taint to vA taint iget-op vA vB fC vA ← vB(fC) τ(vA) ← τ(vB(fC)) ∪ τ(vB) Set vA taint to field fC and object reference taint

public static Integer valueOf(int i) { if (i < -128 || i > 127) {

return new Integer(i); } return valueOfCache.CACHE [i+128];

} static class valueOfCache {

static final Integer[] CACHE = new Integer[256]; static {

for(int i=-128; i<=127; i++) { CACHE[i+128] = new Integer(i); } }

}

Figure 4: Excerpt from Android’s Integer class illustrat- ing the need for object reference taint propagation.

Integer array), the index taint tag is propagated to the ob- ject reference and not the object value. Therefore, we include the object reference taint tag in the instance get (iget-op) rule.

The code listed in Figure 4 demonstrates a real in- stance of where object reference tainting is needed. Here, valueOf() returns an Integer object for a passed int. If the int argument is between −128 and 127, valueOf() returns reference to a statically defined Integer object. valueOf() is implicitly called for conversion to an object. Consider the following definition and use of a method intProxy().

Object intProxy(int val) { return val; } int out = (Integer) intProxy(tVal);

Consider the case where tVal is an int with value 1 and taint tag TAG. When intProxy() is passed tVal, TAG is propagated to val. When intProxy() returns val, it calls Integer.valueOf() to obtain an Integer instance cor- responding to the scalar variable val. In this case, Inte- ger.valueOf() returns a reference to the static Integer ob- ject with value 1. The value field (of the Integer class) in the object has taint tag of ∅; however, since the aget-op propagation rule includes the taint of the index register, the object reference has a taint tag of TAG. Therefore,

only by including the object reference taint tag when the value field is read from the Integer (i.e., the iget-op prop- agation rule), will the correct taint tag of TAG be assigned to out.

4.3 Native Code Taint Propagation Native code is unmonitored in TaintDroid. Ideally,

we achieve the same propagation semantics as the in- terpreted counterpart. Hence, we define two necessary postconditions for accurate taint tracking in the Java- like environment: 1) all accessed external variables (i.e., class fields referenced by other methods) are assigned taint tags according to data flow rules; and 2) the re- turn value is assigned a taint tag according to data flow rules. TaintDroid achieves these postconditions through an assortment of manual instrumentation, heuristics, and method profiles, depending on situational requirements.

Internal VM Methods: Internal VM methods are called directly by interpreted code, passing a pointer to an ar- ray of 32-bit register arguments and a pointer to a return value. The stack augmentation shown in Figure 3 pro- vides access to taint tags for both Java arguments and the return value. As there are a relatively small number of internal VM methods which are infrequently added between versions,2 we manually inspected and patched them for taint propagation as needed. We identified 185 internal VM methods in Android version 2.1; however, only 5 required patching: the System.arraycopy() native method for copying array contents, and several native methods implementing Java reflection.

JNI Methods: JNI methods are invoked through the JNI call bridge. The call bridge parses Java arguments and assigns a return value using the method’s descriptor string. We patched the call bridge to provide taint propa- gation for all JNI methods. When a JNI method returns, TaintDroid consults a method profile table for tag propa-

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10) gation updates. A method profile is a list of (from,to) pairs indicating flows between variables, which may be method parameters, class variables, or return values. Enumerating the information flows for all JNI methods is a time consuming task best completed automatically using source code analysis (a task we leave for future work). We currently include an additional propagation heuristic patch. The heuristic is conservative for JNI methods that only operate on primitive and String ar- guments and return values. It assigns the union of the method argument taint tags to the taint tag of the return value. While the heuristic has false negatives for meth- ods using objects, it covers many existing methods.

We performed a survey of the JNI methods included in the official Android source code (version 2.1) to de- termine specific properties. We found 2,844 JNI meth- ods with a Java interface and C or C++ implementation.3

Of these methods, 913 did not reference objects (as argu- ments, return value, or method body) and hence are auto- matically covered by our heuristic. The remaining meth- ods may or may not have information flows that produce false negatives. Currently, we define method profiles as needed. For example, methods in the IBM NativeCon- verter class require propagation for conversion between character and byte arrays.

4.4 IPC Taint Propagation Taint tags must propagate between applications when

they exchange data. The tracking granularity affects performance and memory overhead. TaintDroid uses message-level taint tracking. A message taint tag repre- sents the upper bound of taint markings assigned to vari- ables contained in the message. We use message-level granularity to minimize performance and storage over- head during IPC.

We chose to implement message-level over variable- level taint propagation, because in a variable-level sys- tem, a devious receiver could game the monitoring by unpacking variables in a different way to acquire val- ues without taint propagation. For example, if an IPC parcel message contains a sequence of scalar values, the receiver may unpack a string instead, thereby acquiring values without propagating all the taint tags on scalar val- ues in the sequence. Hence, to prevent applications from removing taint tags in this way, the current implementa- tion protects taint tags at the message-level.

Message-level taint propagation for IPC leads to false positives. Similar to arrays, all data items in a parcel share the same taint tag. For example, Section 8 dis- cusses limitations for tracking the IMSI that results from passing as portions the value as configuration parameters in parcels. Future implementations will consider word- level taint tags along with additional consistency checks to ensure accurate propagation for unpacked variables.

However, this additional complexity will negatively im- pact IPC performance.

4.5 Secondary Storage Taint Propagation Taint tags may be lost when data is written to a file.

Our design stores one taint tag per file. The taint tag is updated on file write and propagated to data on file read. TaintDroid stores file taint tags in the file sys- tem’s extended attributes. To do this, we implemented extended attribute support for Android’s host file system (YAFFS2) and formatted the removable SDcard with the ext2 file system. As with arrays and IPC, storing one taint tag per file leads to false positives and limits the granularity of taint markings for information databases (see Section 5). Alternatively, we could track taint tags at a finer granularity at the expense of added memory and performance overhead.

4.6 Taint Interface Library Taint sources and sinks defined within the virtualized

environment must communicate taint tags with the track- ing system. We abstract the taint source and sink logic into a single taint interface library. The interface per- forms two functions: 1) add taint markings to variables; and 2) retrieve taint markings from variables. The library only provides the ability to add and not set or clear taint tags, as such functionality could be used by untrusted Java code to remove taint markings.

Adding taint tags to arrays and strings via internal VM methods is straightforward, as both are stored in data ob- jects. Primitive type variables, on the other hand, are stored on the interpreter’s internal stack and disappear after a method is called. Therefore, the taint library uses the method return value as a means of tainting primitive type variables. The developer passes a value or variable into the appropriate add taint method (e.g., addTaintInt()) and the returned variable has the same value but addition- ally has the specified taint tag. Note that the stack storage does not pose complications for taint tag retrieval.

5 Privacy Hook Placement Using TaintDroid for privacy analysis requires iden-

tifying privacy sensitive sources and instrumenting taint sources within the operating system. Historically, dy- namic taint analysis systems assume taint source and sink placement is trivial. However, complex operating sys- tems such as Android provide applications information in a variety of ways, e.g., direct access, and service inter- face. Each potential type of privacy sensitive information must be studied carefully to determine the best method of defining the taint source.

Taint sources can only add taint tags to memory for which TaintDroid provides tag storage. Currently, taint source and sink placement is limited to variables in in-

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10) terpreted code, IPC messages, and files. This section discusses how valuable taint sources and sinks can be im- plemented within these restrictions. We generalize such taint sources based on information characteristics.

Low-bandwidth Sensors: A variety of privacy sensitive information types are acquired through low-bandwidth sensors, e.g., location and accelerometer. Such informa- tion often changes frequently and is simultaneously used by multiple applications. Therefore, it is common for a smartphone OS to multiplex access to low-bandwidth sensors using a manager. This sensor manager represents an ideal point for taint source hook placement. For our analysis, we placed hooks in Android’s LocationMan- ager and SensorManager applications.

High-bandwidth Sensors: Privacy sensitive informa- tion sources such as the microphone and camera are high-bandwidth. Each request from the sensor frequently returns a large amount of data that is only used by one application. Therefore, the smartphone OS may share sensor information via large data buffers, files, or both. When sensor information is shared via files, the file must be tainted with the appropriate tag. Due to flexible APIs, we placed hooks for both data buffer and file tainting for tracking microphone and camera information.

Information Databases: Shared information such as ad- dress books and SMS messages are often stored in file- based databases. This organization provides a useful un- ambiguous taint source similar to hardware sensors. By adding a taint tag to such database files, all informa- tion read from the file will be automatically tainted. We used this technique for tracking address book informa- tion. Note that while TaintDroid’s file-level granularity was appropriate for these valuable information sources, others may exist for which files are too coarse grained. However, we have not yet encountered such sources.

Device Identifiers: Information that uniquely identifies the phone or the user is privacy sensitive. Not all per- sonally identifiable information can be easily tainted. However, the phone contains several easily tainted iden- tifiers: the phone number, SIM card identifiers (IMSI, ICC-ID), and device identifier (IMEI) are all accessed through well-defined APIs. We instrumented the APIs for the phone number, ICC-ID, and IMEI. An IMSI taint source has inherent limitations discussed in Section 8.

Network Taint Sink: Our privacy analysis identifies when tainted information transmits out the network in- terface. The VM interpreter-based approach requires the taint sink to be placed within interpreted code. Hence, we instrumented the Java framework libraries at the point the native socket library is invoked.

6 Application Study This section reports on an application study that uses

TaintDroid to analyze how 30 popular third-party An- droid applications use privacy sensitive user data. Exist- ing applications acquire a variety of user data along with permissions to access the Internet. Our study finds that two thirds of these applications expose detailed location data, the phone’s unique ID, and the phone number using the combination of the seemingly innocuous access per- missions granted at install. This finding was made possi- ble by TaintDroid’s ability to monitor runtime access of sensitive user data and to precisely relate the monitored accesses with the data exposure by applications.

6.1 Experimental Setup An early 2010 survey of the 50 most popular free ap-

plications in each category of the Android Market [2] (1,100 applications, in total) revealed that roughly a third of the applications (358 of the 1,100 applications) re- quire Internet permissions along with permissions to ac- cess either location, camera, or audio data. From this set, we randomly selected 30 popular applications (an 8.4% sample size), which span twelve categories. Table 2 enu- merates these applications along with permissions they request at install time. Note that this does not reflect ac- tual access or use of sensitive data.

We studied each of the thirty downloaded applica- tions by starting the application, performing any initial- ization or registration that was required, and then man- ually exercising the functionality offered by the appli- cation. We recorded system logs including detailed in- formation from TaintDroid: tainted binder messages, tainted file output, and tainted network messages with the remote address. The overall experiment (conducted in May 2010) lasted slightly over 100 minutes, generat- ing 22,594 packets (8.6MB) and 1,130 TCP connections. To verify our results, we also logged the network traffic using tcpdump on the WiFi interface and repeated exper- iments on multiple Nexus One phones, running the same version of TaintDroid built on Android 2.1. Though the phones used for experiments had a valid SIM card in- stalled, the SIM card was inactivate, forcing all the pack- ets to be transmitted via the WiFi interface. The packet trace was used only to verify the exposure of tainted data flagged by TaintDroid.

In addition to the network trace, we also noted whether applications acquired user consent (either explicit or im- plicit) for exporting sensitive information. This provides additional context information to identify possible pri- vacy violations. For example, by selecting the “use my location” option in a weather application, the user im- plicitly consents to disclosing geographic coordinates to the weather server.

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10)

Table 2: Applications grouped by the requested permissions (L: location, C: camera, A: audio, P: phone state). Android Market categories are indicated in parenthesis, showing the diversity of the studied applications.

Applications∗ # Permissions†

L C A P The Weather Channel (News & Weather); Cestos, Solitaire (Game); Movies (Entertainment); Babble (Social); Manga Browser (Comics)

6 x

Bump, Wertago (Social); Antivirus (Communication); ABC — Animals, Traffic Jam, Hearts, Blackjack, (Games); Horoscope (Lifestyle); Yellow Pages (Reference); 3001 Wisdom Quotes Lite, Dastelefonbuch, Astrid (Productivity), BBC News Live Stream (News & Weather); Ring- tones (Entertainment)

14 x x

Layar (Lifestyle); Knocking (Social); Coupons (Shopping); Trapster (Travel); Spongebob Slide (Game); ProBasketBall (Sports)

6 x x x

MySpace (Social); Barcode Scanner, ixMAT (Shopping) 3 x Evernote (Productivity) 1 x x x ∗ Listed names correspond to the name displayed on the phone and not necessarily the name listed in the Android Market. † All listed applications also require access to the Internet.

Table 3: Potential privacy violations by 20 of the studied applications. Note that three applications had multiple violations, one of which had a violation in all three categories.

Observed Behavior (# of apps) Details Phone Information to Content Servers (2) 2 apps sent out the phone number, IMSI, and ICC-ID along with the

geo-coordinates to the app’s content server. Device ID to Content Servers (7)∗ 2 Social, 1 Shopping, 1 Reference and three other apps transmitted

the IMEI number to the app’s content server. Location to Advertisement Servers (15) 5 apps sent geo-coordinates to ad.qwapi.com, 5 apps to admob.com,

2 apps to ads.mobclix.com (1 sent location both to admob.com and ads.mobclix.com) and 4 apps sent location† to data.flurry.com.

∗ TaintDroid flagged nine applications in this category, but only seven transmitted the raw IMEI without mentioning such practice in the EULA. †To the best of our knowledge, the binary messages contained tainted location data (see the discussion below).

6.2 Findings Table 3 summarizes our findings. TaintDroid flagged

105 TCP connections as containing tainted privacy sen- sitive information. We manually labeled each mes- sage based on available context, including remote server names and temporally relevant application log messages. We used remote hostnames as an indication of whether data was being sent to a server providing application functionality or to a third party. Frequently, messages contained plaintext that aided categorization, e.g., an HTTP GET request containing geographic coordinates. However, 21 flagged messages contained binary data. Our investigation indicates these messages were gen- erated by the Google Maps for Mobile [21] and Flur- ryAgent [20] APIs and contained tainted privacy sensi- tive data. These conclusions are supported by message transmissions immediately after the application received a tainted parcel from the system location manager. We now expand on our findings for each category and reflect on potential privacy violations.

Phone Information: Table 2 shows that 20 out of the 30 applications require permissions to read phone state and the Internet. We found that 2 of the 20 applications transmitted to their server (1) the device’s phone num-

ber, (2) the IMSI which is a unique 15-digit code used to identify an individual user on a GSM network, and (3) the ICC-ID number which is a unique SIM card serial number. We verified messages were flagged correctly by inspecting the plaintext payload.4 In neither case was the user informed that this information was transmitted off the phone.

This finding demonstrates that Android’s coarse- grained access control provides insufficient protection against third-party applications seeking to collect sensi- tive data. Moreover, we found that one application trans- mits the phone information every time the phone boots. While this application displays a terms of use on first use, the terms of use does not specify collection of this highly sensitive data. Surprisingly, this application transmits the phone data immediately after install, before first use.

Device Unique ID: The device’s IMEI was also exposed by applications. The IMEI uniquely identifies a specific mobile phone and is used to prevent a stolen handset from accessing the cellular network. TaintDroid flags indicated that nine applications transmitted the IMEI. Seven out of the nine applications either do not present an End User License Agreement (EULA) or do not spec- ify IMEI collection in the EULA. One of the seven ap-

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10) plications is a popular social networking application and another is a location-based search application. Further- more, we found two of the seven applications include the IMEI when transmitting the device’s geographic coordi- nates to their content server, potentially repurposing the IMEI as a client ID.

In comparison, two of the nine applications treat the IMEI with more care, thus we do not classify them as potential privacy violators. One application displays a privacy statement that clearly indicates that the applica- tion collects the device ID. The other uses the hash of the IMEI instead of the number itself. We verified this practice by comparing results from two different phones. Location Data to Advertisement Servers: Half of the studied applications exposed location data to third-party advertisement servers without requiring implicit or ex- plicit user consent. Of the fifteen applications, only two presented a EULA on first run; however neither EULA indicated this practice. Exposure of location informa- tion occurred both in plaintext and in binary format. The latter highlights TaintDroid’s advantages over sim- ple pattern-based packet scanning. Applications sent lo- cation data in plaintext to admob.com, ad.qwapi.com, ads.mobclix.com (11 applications) and in binary format to FlurryAgent (4 applications). The plaintext location exposure to AdMob occurred in the HTTP GET string:

...&s=a14a4a93f1e4c68&..&t=062A1CB1D476DE85 B717D9195A6722A9&d%5Bcoord%5D=47.6612278900 00006%2C-122.31589477&...

Investigating the AdMob SDK revealed the s= parameter is an identifier unique to an application publisher, and the coord= parameter provides the geographic coordinates.

For FlurryAgent, we confirmed location exposure by the following sequence of events. First, a component named “FlurryAgent” registers with the location man- ager to receive location updates. Then, TaintDroid log messages show the application receiving a tainted par- cel from the location manager. Finally, the application reports “sending report to http://data.flurry. com/aar.do” after receiving the tainted parcel.

Our experimentation indicates these fifteen applica- tions collect location data and send it to advertisement servers. In some cases, location data was transmitted to advertisement servers even when no advertisement was displayed in the application. However, we note that TaintDroid helped us verify that three of the studied ap- plications (not included in the Table 3) only transmitted location data per user’s request to pull localized content from their servers. This finding demonstrates the impor- tance of monitoring exercised functionality of an appli- cation that reflects how the application actually uses or abuses the granted permissions. Legitimate Flags: Out of 105 connections flagged by

TaintDroid, 37 were deemed clearly legitimate use. The flags resulted from four applications and the OS itself while using the Google Maps for Mobile (GMM) API. The TaintDroid logs indicate an HTTP request with the “User-Agent: GMM . . . ” header, but a binary pay- load. Given that GMM functionality includes download- ing maps based on geographic coordinates, it is obvious that TaintDroid correctly identified location information in the payload. Our manual inspection of each message along with the network packet trace confirmed that there were no false positives. We note that there is a possibil- ity of false negatives, which is difficult to verify with the lack of the source code of the third-party applications.

Summary: Our study of 30 popular applications shows the effectiveness of the TaintDroid system in accu- rately tracking applications’ use of privacy sensitive data. While monitoring these applications, TaintDroid gener- ated no false positives (with the exception of the IMSI taint source which we disabled for experiments, see Sec- tion 8). The flags raised by TaintDroid helped to identify potential privacy violations by the tested applications. Half of the studied applications share location data with advertisement servers. Approximately one third of the applications expose the device ID, sometimes with the phone number and the SIM card serial number. The anal- ysis was simplified by the taint tag provided by Taint- Droid that precisely describes which privacy relevant data is included in the payload, especially for binary pay- loads. We also note that there was almost no perceived latency while running experiments with TaintDroid.

7 Performance Evaluation We now study TaintDroid’s taint tracking overhead.

Experiments were performed on a Google Nexus One running Android OS version 2.1 modified for TaintDroid. Within the interpreted environment, TaintDroid incurs the same performance and memory overhead regardless of the existence of taint markings. Hence, we only need to ensure file access includes appropriate taint tags.

7.1 Macrobenchmarks During the application study, we anecdotally observed

limited performance overhead. We hypothesize that this is because: 1) most applications are primarily in a “wait state,” and 2) heavyweight operations (e.g., screen up- dates and webpage rendering) occur in unmonitored na- tive libraries.

To gain further insight into perceived overhead, we devised five macrobenchmarks for common high-level smartphone operations. Each experiment was measured 50 times and observed 95% confidence intervals at least an order of magnitude less than the mean. In each case, we excluded the first run to remove unrelated initializa- tion costs. Experimental results are shown in Table 4.

http://data.flurry.com/aar.do

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10)

Table 4: Macrobenchmark Results Android TaintDroid

App Load Time 63 ms 65 ms Address Book (create) 348 ms 367 ms Address Book (read) 101 ms 119 ms Phone Call 96 ms 106 ms Take Picture 1718 ms 2216 ms

Application Load Time: The application load time measures from when Android’s Activity Manager re- ceives a command to start an activity component to the time the activity thread is displayed. This time includes application resolution by the Activity Manager, IPC, and graphical display. TaintDroid adds only 3% overhead, as the operation is dominated by native graphics libraries.

Address Book: We built a custom application to create, read, and delete entries for the phone’s address book, ex- ercising both file read and write. Create used three SQL transactions while read used two SQL transactions. The subsequent delete operation was lazy, returning in 0 ms, and hence was excluded from our results. TaintDroid adds approximately 5.5% and 18% overhead for address book entry creates and reads, respectively. The addi- tional overhead for reads can be attributed to file taint propagation. The data is not tainted before create, hence no file propagation is needed. Note that the user experi- ences less than 20 ms overhead when creating or viewing a contact.

Phone Call: The phone call benchmark measured the time from pressing “dial” to the point at which the audio hardware was reconfigured to “in call” mode. TaintDroid only adds 10 ms per phone call setup (∼10% overhead), which is significantly less than call setup in the network, which takes on the order of seconds.

Take Picture: The picture benchmark measures from the time the user presses the “take picture” button un- til the preview display is re-enabled. This measurement includes the time to capture a picture from the camera and save the file to the SDcard. TaintDroid adds 498 ms to the 1718 ms needed by Android to take a picture (an overhead of 29%). A portion of this overhead can be at- tributed to to additional file operations required for taint propagation (one getxattr/setxattr pair per written data buffer). Note that some of this overhead can be reduced by eliminating redundant taint propagation. That is, only the taint tag for the first data buffer written to file needs to be propagated. For example, the current taint tag could be associated with the file descriptor.

7.2 Java Microbenchmark Figure 5 shows the execution time results of a Java mi-

crobenchmark. We used an Android port of the standard CaffeineMark 3.0 [43]. CaffeineMark uses an internal

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Figure 5: Microbenchmark of Java overhead. Error bars indicate 95% confidence intervals.

scoring metric only useful for relative comparisons. The results are consistent with implementation-

specific expectations. The overhead incurred by Taint- Droid is smallest for the benchmarks dominated by arith- metic and logic operations. The taint propagation for these operations is simple, consisting of an additional copy of spatially local memory. The string benchmark, on the other hand, experiences the greatest overhead. This is most likely due to the additional memory com- parisons that occur when the JNI propagation heuristic checks for string objects in method prototypes.

The “overall” results indicate cumulative score across individual benchmarks. CaffeineMark documentation states that scores roughly correspond to the number of Java instructions executed per second. Here, the unmod- ified Android system had an average score of 1121, and TaintDroid measured 967. TaintDroid has a 14% over- head with respect to the unmodified system.

We also measured memory consumption during the CaffeineMark benchmark. The benchmark consumed 21.28 MB on the unmodified system and 22.21 MB while running on TaintDroid, indicating a 4.4% memory over- head. Note that much of an Android process’s memory is used by the zygote runtime environment. These na- tive library memory pages are shared between applica- tions to reduce the overall system memory footprint and require taint tracking. Given that TaintDroid stores 32 taint markings (4 bytes) for each 32-bit variable in the interpreted environment (regardless of taint state), this overhead is expected.

7.3 IPC Microbenchmark The IPC benchmark considers overhead due to the par-

cel modifications. For this experiment, we developed client and service applications that perform binder trans- actions as fast as possible. The service manipulates ac-

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10)

Table 5: IPC Throughput Test (10,000 msgs). Android TaintDroid

Time (s) 8.58 10.89 Memory (client) 21.06MB 21.88MB

Memory (service) 18.92MB 19.48MB

count objects (a username string and a balance integer) and provides two interfaces: setAccount() and getAc- count(). The experiment measures the time for the client to invoke each interface pair 10,000 times.

Table 5 summarizes the results of the IPC benchmark. TaintDroid was 27% slower than Android. TaintDroid only adds four bytes to each IPC object, therefore over- head due to data size is unlikely. The more likely cause of the overhead is the continual copying of taint tags as val- ues are marshalled into and out of the parcel byte buffer. Finally, TaintDroid used 3.5% more memory than An- droid, which is comparable to the consumption observed during the CaffeineMark benchmarks.

8 Discussion Approach Limitations: TaintDroid only tracks data flows (i.e., explicit flows) and does not track control flows (i.e., implicit flows) to minimize performance over- head. Section 6 shows that TaintDroid can track applica- tions’ expected data exposure and also reveal suspicious actions. However, applications that are truly malicious can game our system and exfiltrate privacy sensitive in- formation through control flows. Fully tracking control flow requires static analysis [14, 37], which is not appli- cable to analyzing third-party applications whose source code is unavailable. Direct control flows can be tracked dynamically if a taint scope can be determined [53]; however, DEX does not maintain branch structures that TaintDroid can leverage. On-demand static analysis to determine method control flow graphs (CFGs) provides this context [39]; however, TaintDroid does not currently perform such analysis in order to avoid false positives and significant performance overhead. Our data flow taint propagation logic is consistent with existing, well known, taint tracking systems [7, 57]. Finally, once in- formation leaves the phone, it may return in a network reply. TaintDroid cannot track such information.

Implementation Limitations: Android uses the Apache Harmony [3] implementation of Java with a few custom modifications. This implementation includes support for the PlatformAddress class, which contains a native ad- dress and is used by DirectBuffer objects. The file and network IO APIs include write and read “direct” vari- ants that consume the native address from a DirectBuffer. TaintDroid does not currently track taint tags on Direct- Buffer objects, because the data is stored in opaque native data structures. Currently, TaintDroid logs when a read

or write “direct” variant is used, which anecdotally oc- curred with minimal frequency. Similar implementation limitations exist with the sun.misc.Unsafe class, which also operates on native addresses.

Taint Source Limitations: While TaintDroid is very ef- fective for tracking sensitive information, it causes sig- nificant false positives when the tracked information con- tains configuration identifiers. For example, the IMSI nu- meric string consists of a Mobile Country Code (MCC), Mobile Network Code (MNC), and Mobile Station Iden- tifier Number (MSIN), which are all tainted together.5

Android uses the MCC and MNC extensively as con- figuration parameters when communicating other data. This causes all information in a parcel to become tainted, eventually resulting in an explosion of tainted informa- tion. Thus, for taint sources that contain configuration parameters, tainting individual variables within parcels is more appropriate. However, as our analysis results in Section 6 show, message-level taint tracking is effective for the majority of our taint sources.

9 Related Work Mobile phone host security is a growing concern.

OS-level protections such as Kirin [18], Saint [42], and Security-by-Contract [15] provide enhanced security mechanisms for Android and Windows Mobile. These approaches prevent access to sensitive information; how- ever, once information enters the application, no addi- tional mediation occurs. In systems with larger displays, a graphical widget [27] can help users visualize sensor access policies. Mulliner et al. [36] provide information tracking by labeling smartphone processes based on the interfaces they access, effectively limiting access to fu- ture interfaces based on acquired labels.

Decentralized information flow control (DIFC) en- hanced operating systems such as Asbestos [52] and HiS- tar [60] label processes and enforce access control based on Denning’s lattice model for information flow secu- rity [13]. Flume [30] provides similar enhancements for legacy OS abstractions. DEFCon [34] uses a logic simi- lar to these DIFC OSes, but focuses on events and modi- fies a Java runtime with lightweight isolation. Related to these system-level approaches, PRECIP [54] labels both processes and shared kernel objects such as the clipboard and display buffer. However, these process-level infor- mation flow models are coarse grained and cannot track sensitive information within untrusted applications.

Tools that analyze applications for privacy sensi- tive information leaks include Privacy Oracle [28] and TightLip [59]. These tools investigate applications while treating them as a black box, thus enabling analysis of off-the-shelf applications. However, this black-box anal- ysis tool becomes ineffective when applications use en- cryption prior to releasing sensitive information.

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10) Language-based information flow security [46] ex-

tends existing programming languages by labeling vari- ables with security attributes. Compilers use the secu- rity labels to generate security proofs, e.g., Jif [37, 38] and SLam [24]. Laminar [45] provides DIFC guarantees based on programmer defined security regions. However, these languages require careful development and are of- ten incompatible with legacy software designs [25].

Dynamic taint analysis provides information track- ing for legacy programs. The approach has been used to enhance system integrity (e.g., defend against soft- ware attacks [41, 44, 8]) and confidentiality (e.g., dis- cover privacy exposure [57, 16, 61]), as well as track Internet worms [9]. Dynamic tracking approaches range from whole-system analysis using hardware exten- sions [51, 11, 50] and emulation environments [7, 57] to per-process tracking using dynamic binary transla- tion (DBT) [6, 44, 8, 61]. The performance and mem- ory overhead associated with dynamic tracking has re- sulted in an array of optimizations, including optimizing context switches [44], on-demand tracking [26] based on hypervisor introspection, and function summaries for code with known information flow properties [61]. If source code is available, significant performance im- provements can be achieved by automatically instru- menting legacy programs with dynamic tracking func- tionality [56, 31]. Automatic instrumentation has also been performed on x86 binaries [47], providing a com- promise between source code translation and DBT. Our TaintDroid design was inspired by these prior works, but addressed different challenges unique to mobile phones. To our knowledge, TaintDroid is the first taint tracking system for a mobile phone and is the first dynamic taint analysis system to achieve practical system-wide analy- sis through the integration of tracking multiple data ob- ject granularities.

Finally, dynamic taint analysis has been applied to vir- tual machines and interpreters. Haldar et al. [22] in- strument the Java String class with taint tracking to pre- vent SQL injection attacks. WASP [23] has similar mo- tivations; however, it uses positive tainting of individ- ual characters to ensure the SQL query contains only high-integrity substrings. Chandra and Franz [5] pro- pose fine-grained information flow tracking within the JVM and instrument Java byte-code to aid control flow analysis. Similarly, Nair et al. [39] instrument the Kaffe JVM. Vogt et al. [53] instrument a Javascript interpreter to prevent cross-site scripting attacks. Xu et al. [56] au- tomatically instrument the PHP interpreter source code with dynamic information tracking to prevent SQL in- jection attacks. Finally, the Resin [58] environment for PHP and Python uses data flow tracking to prevent an as- sortment of Web application attacks. When data leaves the interpreted environment, Resin implements filters

for files and SQL databases to serialize and de-serialize objects and policy with byte-level granularity. Taint- Droid’s interpreted code taint propagation bears similar- ity to some of these works. However, TaintDroid im- plements system-wide information flow tracking, seam- lessly connecting interpreter taint tracking with a range of operating system sharing mechanisms.

10 Conclusions While some mobile phone operating systems allow

users to control applications’ access to sensitive informa- tion, such as location sensors, camera images, and con- tact lists, users lack visibility into how applications use their private data. To address this, we present TaintDroid, an efficient, system-wide information flow tracking tool that can simultaneously track multiple sources of sensi- tive data. A key design goal of TaintDroid is efficiency, and TaintDroid achieves this by integrating four gran- ularities of taint propagation (variable-level, message- level, method-level, and file-level) to achieve a 14% per- formance overhead on a CPU-bound microbenchmark.

We also used our TaintDroid implementation to study the behavior of 30 popular third-party applications, cho- sen at random from the Android Marketplace. Our study revealed that two-thirds of the applications in our study exhibit suspicious handling of sensitive data, and that 15 of the 30 applications reported users’ locations to remote advertising servers. Our findings demonstrate the effec- tiveness and value of enhancing smartphone platforms with monitoring tools such as TaintDroid.

Acknowledgments We would like to thank Intel Labs, Berkeley and

Seattle for its support and feedback during the design and prototype implementation of this work. We thank Jayanth Kannon, Stuart Schechter, and Ben Greenstein for their feedback during the writing of this paper. We also thank Kevin Butler, Stephen McLaughlin, Machigar Ongtang, and the SIIS lab as a whole for their helpful comments. This material is based upon work supported by the National Science Foundation. William Enck and Patrick McDaniel were partially supported by NSF Grant No. CNS-0905447, CNS-0721579 and CNS-0643907. Landon Cox and Peter Gilbert were partially supported by NSF CAREER Award CNS-0747283. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not nec- essarily reflect the views of the National Science Foun- dation.

References [1] Android. http://www.android.com.

[2] Android Market. http://market.android.com.

[3] Apache Harmony – Open Source Java Platform. http:// harmony.apache.org.

http://www.android.com

http://market.android.com

http://harmony.apache.org

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10) [4] APPLE, INC. Apples App Store Downloads Top Three

Billion. http://www.apple.com/pr/library/2010/ 01/05appstore.html, January 2010.

[5] CHANDRA, D., AND FRANZ, M. Fine-Grained Information Flow Analysis and Enforcement in a Java Virtual Machine. In Proceedings of the 23rd Annual Computer Security Applications Conference (ACSAC) (December 2007).

[6] CHENG, W., ZHAO, Q., YU, B., AND HIROSHIGE, S. Taint- Trace: Efficient Flow Tracing with Dyanmic Binary Rewriting. In Proceedings of the IEEE Symposium on Computers and Com- munications (ISCC) (June 2006), pp. 749–754.

[7] CHOW, J., PFAFF, B., GARFINKEL, T., CHRISTOPHER, K., AND ROSENBLUM, M. Understanding Data Lifetime via Whole System Simulation. In Proceedings of the 13th USENIX Security Symposium (August 2004).

[8] CLAUSE, J., LI, W., AND ORSO, A. Dytan: A Generic Dy- namic Taint Analysis Framework. In Proceedings of the 2007 in- ternational symposium on Software testing and analysis (2007), pp. 196–206.

[9] COSTA, M., CROWCROFT, J., CASTRO, M., ROWSTRON, A., ZHOU, L., ZHANG, L., AND BARHAM, P. Vigilante: End-to- End Containment of Internet Worms. In Proceedings of the ACM Symposium on Operating Systems Principles (2005).

[10] COX, L. P., AND GILBERT, P. RedFlag: Reducing Inadvertent Leaks by Personal Machines. Tech. Rep. TR-2009-02, Duke Uni- versity, 2009.

[11] CRANDALL, J. R., AND CHONG, F. T. Minos: Control Data Attack Prevention Orthogonal to Memory Model. In Proceedings of the International Symposium on Microarchitecture (December 2004), pp. 221–232.

[12] DAVIES, C. iPhone spyware debated as app li- brary “phones home”. http://www.slashgear. com/iphone-spyware-debated-as-app- library-phones-home-1752491/, August 17, 2009.

[13] DENNING, D. E. A Lattice Model of Secure Information Flow. Communications of the ACM 19, 5 (May 1976), 236–243.

[14] DENNING, D. E., AND DENNING, P. J. Certification of Pro- grams for Secure Information Flow. Communications of the ACM 20, 7 (July 1977).

[15] DESMET, L., JOOSEN, W., MASSACCI, F., PHILIPPAERTS, P., PIESSENS, F., SIAHAAN, I., AND VANOVERBERGHE, D. Security-by-contract on the .NET platform. Information Security Technical Report 13, 1 (January 2008), 25–32.

[16] EGELE, M., KRUEGEL, C., KIRDA, E., YIN, H., AND SONG, D. Dyanmic Spyware Analysis. In Proceedings of the USENIX Annual Technical Conference (June 2007), pp. 233–246.

[17] ENCK, W., GILBERT, P., CHUN, B.-G., COX, L. P., JUNG, J., MCDANIEL, P., AND SHETH, A. N. TaintDroid: An Information-Flow Tracking System for Realtime Privacy Mon- itoring on Smartphones. Tech. Rep. NAS-TR-0120-2010, Net- work and Security Research Center, Department of Computer Science and Engineering, Pennsylvania State University, Univer- sity Park, PA, USA, August 2010.

[18] ENCK, W., ONGTANG, M., AND MCDANIEL, P. On Lightweight Mobile Phone Application Certification. In Proceed- ings of the 16th ACM Conference on Computer and Communica- tions Security (CCS) (November 2009).

[19] FITZPATRICK, M. Mobile that allows bosses to snoop on staff developed. BBC News, March 2010. http://news.bbc. co.uk/2/hi/technology/8559683.stm.

[20] Flurry Mobile Application Analytics. http://www.flurry. com/product/technical-info.html.

[21] Google Maps for Mobile. http://www.google.com/ mobile/products/maps.html.

[22] HALDAR, V., CHANDRA, D., AND FRANZ, M. Dynamic Taint Propagation for Java. In Proceedings of the 21st Annual Computer Security Applications Conference (ACSAC) (Decem- ber 2005), pp. 303–311.

[23] HALFOND, W. G., ORSO, A., AND MANOLIOS, P. WASP: Protecting Web Applications Using Positive Tainting and Syntax- Aware Evaluation. IEEE Transactions on Software Engineering 34, 1 (2008), 65–81.

[24] HEINTZE, N., AND RIECKE, J. G. The SLam Calculus: Pro- gramming with Secrecy and Integrity. In Proceedings of the Symposium on Principles of Programming Languages (POPL) (1998), pp. 365–377.

[25] HICKS, B., AHMADIZADEH, K., AND MCDANIEL, P. Under- standing practical application development in security-typed lan- guages. In 22st Annual Computer Security Applications Confer- ence (ACSAC) (2006), pp. 153–164.

[26] HO, A., FETTERMAN, M., CLARK, C., WARFIELD, A., AND HAND, S. Practical Taint-Based Protection using Demand Emu- lation. In Proceedings of the European Conference on Computer Systems (EuroSys) (2006), pp. 29–41.

[27] HOWELL, J., AND SCHECHTER, S. What You See is What they Get: Protecting users from unwanted use of microphones, cam- era, and other sensors. In Proceedings of Web 2.0 Security and Privacy Workshop (2010).

[28] JUNG, J., SHETH, A., GREENSTEIN, B., WETHERALL, D., MAGANIS, G., AND KOHNO, T. Privacy Oracle: A System for Finding Application Leaks with Black Box Differential Testing. In Proceedings of ACM CCS (2008).

[29] KING, D., HICKS, B., HICKS, M., AND JAEGER, T. Implicit Flows: Can’t Live with ’Em, Can’t Live without ’Em. In Pro- ceedings of the International Conference on Information Systems Security (2008).

[30] KROHN, M., YIP, A., BRODSKY, M., CLIFFER, N., KAASHOEK, M. F., KOHLER, E., AND MORRIS, R. Informa- tion Flow Control for Standard OS Abstractions. In Proceedings of ACM Symposium on Operating Systems Principles (2007).

[31] LAM, L. C., AND CKER CHIUEH, T. A General Dynamic Infor- mation Flow Tracking Framework for Security Applications. In Proceedings of the Annual Computer Security Applications Con- ference (ACSAC) (2006).

[32] LIANG, S. Java Native Interface: Programmer’s Guide and Specification. Prentice Hall PTR, 1999.

[33] LOOKOUT. Introducing the App Genome Project. http://blog.mylookout.com/2010/07/ introducing-the-app-genome-project/, July 2010.

[34] MIGLIAVACCA, M., PAPAGIANNIS, I., EYERS, D. M., SHAND, B., BACON, J., AND PIETZUCH, P. DEFCon: High-Performance Event Processing with Information Security. In PROCEEDINGS of the USENIX Annual Technical Conference (2010).

[35] MOREN, D. Retrievable iPhone numbers mean potential privacy issues. http://www.macworld.com/article/ 143047/2009/09/phone_hole.html, September 29, 2009.

[36] MULLINER, C., VIGNA, G., DAGON, D., AND LEE, W. Us- ing Labeling to Prevent Cross-Service Attacks Against Smart Phones. In Proceedings of Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA) (2006).

[37] MYERS, A. C. JFlow: Practical Mostly-Static Information Flow Control. In Proceedings of the ACM Symposium on Principles of Programming Langauges (POPL) (January 1999).

http://www.apple.com/pr/library/2010/01/05appstore.html

http://www.slashgear.com/iphone-spyware-debated-as-app-

library-phones-home-1752491/

http://news.bbc.co.uk/2/hi/technology/8559683.stm

http://www.flurry.com/product/technical-info.html

http://www.google.com/mobile/products/maps.html

http://blog.mylookout.com/2010/07/introducing-the-app-genome-project/

http://www.macworld.com/article/143047/2009/09/phone_hole.html

To appear at the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10) [38] MYERS, A. C., AND LISKOV, B. Protecting Privacy Using the

Decentralized Label Model. ACM Transactions on Software En- gineering and Methodology 9, 4 (October 2000), 410–442.

[39] NAIR, S. K., SIMPSON, P. N., CRISPO, B., AND TANENBAUM, A. S. A Virtual Machine Based Information Flow Control Sys- tem for Policy Enforcement. In the 1st International Workshop on Run Time Enforcement for Mobile and Distributed Systems (REM) (2007).

[40] NEWSOME, J., MCCAMANT, S., AND SONG, D. Measuring channel capacity to distinguish undue influence. In ACM SIG- PLAN Workshop on Programming Languages and Analysis for Security (2009).

[41] NEWSOME, J., AND SONG, D. Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Ex- ploits on Commodity Software. In Proc. of Network and Dis- tributed System Security Symposium (2005).

[42] ONGTANG, M., MCLAUGHLIN, S., ENCK, W., AND MC- DANIEL, P. Semantically Rich Application-Centric Security in Android. In Proceedings of the 25th Annual Computer Security Applications Conference (ACSAC) (2009).

[43] PENDRAGON SOFTWARE CORPORATION. CaffeineMark 3.0. http://www.benchmarkhq.ru/cm30/.

[44] QIN, F., WANG, C., LI, Z., SEOP KIM, H., ZHOU, Y., AND WU, Y. LIFT: A Low-Overhead Practical Information Flow Tracking System for Detecting Security Attacks. In Proceedings of the 39th Annual IEEE/ACM International Symposium on Mi- croarchitecture (2006), pp. 135–148.

[45] ROY, I., PORTER, D. E., BOND, M. D., MCKINLEY, K. S., AND WITCHEL, E. Laminar: Practical Fine-Grained Decentral- ized Information Flow Control. In Proceedings of Programming Language Design and Implementation (2009).

[46] SABELFELD, A., AND MYERS, A. C. Language-based information-flow security. IEEE Journal on Selected Areas in Communication 21, 1 (January 2003), 5–19.

[47] SAXENA, P., SEKAR, R., AND PURANIK, V. Efficient Fine- Grained Binary Instrumentation with Applications to Taint- Tracking. In Proceedings of the IEEE/ACM symposium on Code Generation and Optimization (CGO) (2008).

[48] SCHWARTZ, E. J., AVGERINOS, T., AND BRUMLEY, D. All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but might have been afraid to ask). In IEEE Symposium on Security and Privacy (2010).

[49] SLOWINSKA, A., AND BOS, H. Pointless Tainting? Evaluating the Practicality of Pointer Tainting. In Proceedings of the Euro- pean Conference on Computer Systems (EuroSys) (April 2009), pp. 61–74.

[50] SUH, G. E., LEE, J. W., ZHANG, D., AND DEVADAS, S. Se- cure Program Execution via Dynamic Information Flow Track- ing. In Proceedings of Architectural Support for Programming Languages and Operating Systems (2004).

[51] VACHHARAJANI, N., BRIDGES, M. J., CHANG, J., RANGAN, R., OTTONI, G., BLOME, J. A., REIS, G. A., VACHHARA- JANI, M., AND AUGUST, D. I. RIFLE: An Architectural Frame- work for User-Centric Information-Flow Security. In Proceed- ings of the 37th annual IEEE/ACM International Symposium on Microarchitecture (2004), pp. 243–254.

[52] VANDEBOGART, S., EFSTATHOPOULOS, P., KOHLER, E., KROHN, M., FREY, C., ZIEGLER, D., KAASHOEK, F., MOR- RIS, R., AND MAZIÈRES, D. Labels and Event Processes in the Asbestos Operating System. ACM Transactions on Computer Systems (TOCS) 25, 4 (December 2007).

[53] VOGT, P., NENTWICH, F., JOVANOVIC, N., KIRDA, E., KRUEGEL, C., AND VIGNA, G. Cross-Site Scripting Preven- tion with Dynamic Data Tainting and Static Analysis. In Proc. of Network & Distributed System Security (2007).

[54] WANG, X., LI, Z., LI, N., AND CHOI, J. Y. PRECIP: Towards Practical and Retrofittable Confidential Information Protection. In Proceedings of 15th Network and Distributed System Security Symposium (NDSS) (2008).

[55] WhatApp. http://www.whatapp.org. Accessed April 2010.

[56] XU, W., BHATKAR, S., AND SEKAR, R. Taint-Enhanced Pol- icy Enforcement: A Practical Approach to Defeat a Wide Range of Attacks. In Proceedings of the USENIX Security Symposium (August 2006), pp. 121–136.

[57] YIN, H., SONG, D., EGELE, M., KRUEGEL, C., AND KIRDA, E. Panorama: Capturing System-wide Information Flow for Mal- ware Detection and Analysis. In Proceedings of ACM Computer and Communications Security (2007).

[58] YIP, A., WANG, X., ZELDOVICH, N., AND KAASHOEK, M. F. Improving Application Security with Data Flow Assertions. In Proceedings of the ACM Symposium on Operating Systems Prin- ciples (Oct. 2009).

[59] YUMEREFENDI, A. R., MICKLE, B., AND COX, L. P. TightLip: Keeping Applications from Spilling the Beans. In Proceedings of the 4th USENIX Symposium on Network Systems Design & Implementation (NSDI) (2007).

[60] ZELDOVICH, N., BOYD-WICKIZER, S., KOHLER, E., AND MAZIÈRES, D. Making Information Flow Explicit in HiStar. In Proceedings of the 7th symposium on Operating Systems Design and Implementation (OSDI) (2006).

[61] ZHU, D., JUNG, J., SONG, D., KOHNO, T., AND WETHERALL, D. Privacy Scope: A Precise Information Flow Tracking Sys- tem For Finding Application Leaks. Tech. Rep. EECS-2009-145, Department of Computer Science, UC Berkeley, 2009.

Notes 1A similar approach can be applied to just-in-time compilation by

inserting tracking code within the generated binary. 2Only 11 internal VM methods were added between versions 1.5

and 2.1 (primarily for debugging and profiling) 3There was a relatively small number of JNI methods that did not

either have a Java interface or C/C++ implementation. These unusable methods were excluded from our survey.

4Because of the limitation of the IMSI taint source as discussed in Section 8, we disabled the IMSI taint source for experiments. Nonethe- less, TaintDroid’s flag of the ICC-ID and the phone number led us to find the IMSI contained in the same payload.

5Regardless of the string separation, the MCC and MNC are identi- fiers that warrant taint sources.

http://www.benchmarkhq.ru/cm30/

http://www.whatapp.org

Introduction
Approach Overview
Background: Android
TaintDroid

Taint Tag Storage
Interpreted Code Taint Propagation

Taint Propagation Logic
Tainting Object References

Native Code Taint Propagation
IPC Taint Propagation
Secondary Storage Taint Propagation
Taint Interface Library

Privacy Hook Placement
Application Study

Experimental Setup
Findings

Performance Evaluation

Macrobenchmarks
Java Microbenchmark
IPC Microbenchmark

Discussion
Related Work
Conclusions