Presented by: Tom Staley

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Presented by: Tom Staley

Introduction

 Rising security concerns in the smartphone app community  Use of private data:  Passwords  Financial records  GPS locations  Malware attacks have been found targeting smartphones

TaintDroid

 Previous attempt by presenters to address security problems  Tracks sensitive data as it flows between apps  Raises an alert when sensitive data is transmitted off the phone  Leaks are only found after the data has been lost

Current Security Methods

 Unlike PCs, the app market is highly centralized  Scan apps as they join the market  Currently applied manually, if at all  Some banned behavior still slips through the cracks

Proposed Solution

 AppInspector  Service run by market providers or by a third-party  Uses multiple virtual smartphones to run instances of apps before they reach the market  Entire process is automated to ensure thorough testing

Challenges

 Three challenges with AppInspector  How to track and log data  How to determine security violations using the logs  How to ensure all branches of code are covered

AppInspector Components

 Four main components  Input generator  Execution explorer  Information flow tracker  Security analyzer

Types of violations

 Security violation - when an app accesses data without permissions to do so  Privacy violation – when an app discloses information without prompting the user  AppInspector focuses on privacy violations

Tracking Data

 Log data about explicit and implicit data flows  Various actions also logged, like methods that access disk memory or device sensors  Action logging has to be limited to reduce overhead

Data Flows

 Explicit data flow – following data through use of data dependencies  Attach a “label” to data as it leaves the source of the data  Track the label through the program until it reaches a “sink”  Implicit data flow – when sensitive information can be found by looking at control flow  if (w == 0) x = y; else z = y;  If w is privacy-sensitive, looking at values of x and z can determine if w == 0;

Violation Detection

 Two methods to detect privacy violations  When sensitive data is disclosed:   Use data dependency graph to trace sensitive data back to source Check for user notifications or search license agreements for permissions

Input Generation

 App are event-driven  Two types of events:  UI inputs  Callback triggers from device sensors  These inputs can be randomly generated to test apps

Concrete Execution

 Randomly generating input is known as Concrete Execution  Developers tested this approach on 9 apps  Fed constant stream of input for 30 minutes  40% or lower code coverage found

Symbolic Execution

 Another type of input testing known as symbolic testing  Systematically tests all possible execution paths  Highly inefficient

Concolic Execution

 Mix of concrete and symbolic execution  Run symbolic execution on main application code  All other code (code libraries, system code, etc.) tested with concrete execution  Switch between the two methods as required during testing

Conclusion

 The app market is at risk for security and privacy violations  AppInspector developed to scan apps before they reach marketplace  Uses concolic execution to generate input  Tracks sensitive data as it propagates through app  Uses logs to determine if privacy violation has occurred

Bibliography

Peter Gilbert, "Vision: Automated Security Validation of Mobile Apps at App Markets", MCS’11, June 28, 2011, Bethesda, Maryland, USA.

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