Exploitation Techniques and Defenses for Data-Oriented Attacks
Long Cheng, Hans Liljestrand, Salman Ahmed, Thomas Nyman, Trent Jaeger, N. Asokan, Danfeng Daphne Yao · 2019
Data-oriented attacks manipulate non-control data to alter a program's benign behavior without violating its control-flow integrity. It has been shown that such attacks can cause significant damage even in the presence of control-flow defense mechanisms. However, these threats have not been adequately addressed. In this systematization of knowledge (SoK) paper, we first map data-oriented exploits, including Data-Oriented Programming (DOP) and Block-Oriented Programming attacks, to their assumptions/requirements and attack capabilities. We also compare known defenses against these attacks, in terms of approach, detection capabilities, overhead, and compatibility. Then we discuss the possible frequency anomalies of data-oriented attacks, especially the frequency anomalies of DOP attacks with experimental proofs. It is generally believed that control flows may not be useful for data-oriented security. How-ever, the frequency anomalies show that data-oriented attacks (especially DOP attacks) may generate side-effects on control-flow behavior in multiple dimensions. In the end, we discuss challenges for building deployable data-oriented defenses and open research questions.