Control-Flow Integrity: Attacks and Protections

Despite the intense efforts to prevent programmers from writing code with memory errors, memory corruption vulnerabilities are still a major security threat. Consequently, control-flow integrity has received significant attention in the research community, and software developers to combat control code execution attacks in the presence of type of faults. Control-flow Integrity (CFI) is a large family of techniques that aims to eradicate memory error exploitation by ensuring that the instruction pointer (IP) of a running process cannot be controlled by a malicious attacker. In this paper, we assess the effectiveness of 14 CFI techniques against the most popular exploitation techniques, including code reuse attacks, return-to-user, return-to-libc, and replay attacks. We also classify these techniques based on their security, robustness, and implementation complexity. Our study indicates that the majority of the CFI techniques are primarily focused on restricting indirect branch instructions and cannot prevent all forms of vulnerability exploitation. We conclude that the performance overhead introduced, jointly with the partial attack coverage, is discouraging the industry from adopting most of them

Keeping Context In Mind: Automating Mobile App Access Control with User Interface Inspection

Recent studies observe that app foreground is the most striking component that influences the access control decisions in mobile platform, as users tend to deny permission requests lacking visible evidence. However, none of the existing permission models provides a systematic approach that can automatically answer the question: Is the resource access indicated by app foreground? In this work, we present the design, implementation, and evaluation of COSMOS, a context-aware mediation system that bridges the semantic gap between foreground interaction and background access, in order to protect system integrity and user privacy. Specifically, COSMOS learns from a large set of apps with similar functionalities and user interfaces to construct generic models that detect the outliers at runtime. It can be further customized to satisfy specific user privacy preference by continuously evolving with user decisions. Experiments show that COSMOS achieves both high precision and high recall in detecting malicious requests. We also demonstrate the effectiveness of COSMOS in capturing specific user preferences using the decisions collected from 24 users and illustrate that COSMOS can be easily deployed on smartphones as a real-time guard with a very low performance overhead.Comment: Accepted for publication in IEEE INFOCOM'201