CryptoEval: Evaluating the Risk of Cryptographic Misuses in Android Apps with Data-Flow Analysis

TL;DR

CryptoEval is a comprehensive framework that detects cryptographic misuses in Android apps, assesses their security risks through data-flow analysis, and guides mitigation strategies using machine learning, based on extensive empirical evaluation.

Contribution

This paper introduces an extensible framework combining unified detection, data-flow analysis, and risk assessment for cryptographic misuses in Android apps, with a novel threat classification approach.

Findings

High detection accuracy demonstrated on over 40,000 apps

Cryptographic misuses pose significant security risks in popular apps

Data-flow analysis effectively links misuses to potential vulnerabilities

Abstract

The misunderstanding and incorrect configurations of cryptographic primitives have exposed severe security vulnerabilities to attackers. Due to the pervasiveness and diversity of cryptographic misuses, a comprehensive and accurate understanding of how cryptographic misuses can undermine the security of an Android app is critical to the subsequent mitigation strategies but also challenging. Although various approaches have been proposed to detect cryptographic misuse in Android apps, studies have yet to focus on estimating the security risks of cryptographic misuse. To address this problem, we present an extensible framework for deciding the threat level of cryptographic misuse in Android apps. Firstly, we propose a general and unified specification for representing cryptographic misuses to make our framework extensible and develop adapters to unify the detection results of the state-of-the-art cryptographic misuse detectors, resulting in an adapter-based detection tool chain for a more comprehensive list of cryptographic misuses. Secondly, we employ a misuse-originating data-flow analysis to connect each cryptographic misuse to a set of data-flow sinks in an app, based on which we propose a quantitative data-flow-driven metric for assessing the overall risk of the app introduced by cryptographic misuses. To make the per-app assessment more useful for app vetting at the app-store level, we apply unsupervised learning to predict and classify the top risky threats to guide more efficient subsequent mitigation. In the experiments on an instantiated implementation of the framework, we evaluate the accuracy of our detection and the effect of data-flow-driven risk assessment of our framework. Our empirical study on over 40,000 apps and the analysis of popular apps reveal important security observations on the real threats of cryptographic misuse in Android apps.