Poster: Static Detection Of Api Call Vulnerabilities In Ios Executables

We propose a static analysis technique for iOS executables for checking API call vulnerabilities that can cause 1) app behaviors to be altered by malicious external inputs, and 2) sensitive user data to be illegally accessed by apps with stealthy private API calls that use string obfuscation. We identify sensitive functions that dynamically load classes/frameworks, and, for each parameter that corresponds to a dynamically loaded class/framework, we construct a dependency graph that shows the set of values that flow to that parameter. A sensitive function that has its class name or framework path parameter depending on external inputs is considered to contain a vulnerability. We further conduct string analysis on these dependency graphs to determine all potential string values that these parameters can take, which identifies the set of dynamically loaded classes/frameworks. Taking the intersection of these values with patterns that characterize Apple's API policies (such as restricted use of private/sensitive APIs), we are able to detect potential policy violations and vulnerabilities.