Mobile apps can access a wide variety of secure information, such as contacts and location. However, current mobile platforms include only coarse access control mechanisms to protect such data. In this paper, we introduce interaction-based declassification policies, in which the user's interactions with the app constrain the release of sensitive information. Our policies are defined extensionally, so as to be independent of the app's implementation, based on sequences of security-relevant events that occur in app runs. Policies use LTL formulae to precisely specify which secret inputs, read at which times, may be released. We formalize a semantic security condition, interaction-based noninterference, to define our policies precisely. Finally, we describe a prototype tool that uses symbolic execution to check interaction-based declassification policies for Android, and we show that it enforces policies correctly on a set of apps.
TaintDroid @cite_23 is a run-time information-flow tracking system for Android. It monitors the usage of sensitive information and detects when that information is sent over insecure channels. Unlike , TaintDroid does not detect implicit flows.
@cite_23
2101799521 1963971515 2060692877 1849042743
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 efficient, system-wide dynamic taint tracking and analysis 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 imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users' private information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications. Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their privacy-sensitive data. We address these shortcomings with TaintDroid, an efficient, systemwide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides real-time analysis by leveraging Android's virtualized execution environment. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of misappropriation of users' location and device identification information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications. Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32p performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications. We investigate the limitations of using dynamic taint analysis for tracking privacy-sensitive information on Android-based mobile devices. Taint tracking keeps track of data as it propagates through variables, interprocess messages and files, by tagging them with taint marks. A popular taint-tracking system, TaintDroid, uses this approach in Android mobile applications to mark private information, such as device identifiers or user's contacts details, and subsequently issue warnings when this information is misused (e.g., sent to an un-desired third party). We present a collection of attacks on Android-based taint tracking. Specifically, we apply generic classes of anti-taint methods in a mobile device environment to circumvent this security technique. We have implemented the presented techniques in an Android application, ScrubDroid. We successfully tested our app with the TaintDroid implementations for Android OS versions 2.3 to 4.1.1, both using the emulator and with real devices. Finally, we evaluate the success rate and time to complete of the presented attacks. We conclude that, although taint tracking may be a valuable tool for software developers, it will not effectively protect sensitive data from the black-box code of a motivated attacker applying any of the presented anti-taint tracking methods.