Abstract-Memory corruption attacks continue to be a major vector of attack for compromising modern systems. Numerous defenses have been proposed against memory corruption attacks, but they all have their limitations and weaknesses. Stronger defenses such as complete memory safety for legacy languages (C/C++) incur a large overhead, while weaker ones such as practical control flow integrity have been shown to be ineffective. A recent technique called code pointer integrity (CPI) promises to balance security and performance by focusing memory safety on code pointers thus preventing most control-hijacking attacks while maintaining low overhead. CPI protects access to code pointers by storing them in a safe region that is protected by instruction level isolation. On x86-32, this isolation is enforced by hardware; on x86-64 and ARM, isolation is enforced by information hiding. We show that, for architectures that do not support segmentation in which CPI relies on information hiding, CPI's safe region can be leaked and then maliciously modified by using data pointer overwrites. We implement a proofof-concept exploit against Nginx and successfully bypass CPI implementations that rely on information hiding in 6 seconds with 13 observed crashes. We also present an attack that generates no crashes and is able to bypass CPI in 98 hours. Our attack demonstrates the importance of adequately protecting secrets in security mechanisms and the dangers of relying on difficulty of guessing without guaranteeing the absence of memory leaks.
Moving target defenses based on dynamic platforms have been proposed as a way to make systems more resistant to attacks by changing the properties of the deployed platforms. Unfortunately, little work has been done on discerning effective strategies for the utilization of these systems, instead relying on two generally false premises: simple randomization leads to diversity and platforms are independent. In this paper, we study the strategic considerations of deploying a dynamic platform system by specifying a relevant threat model and applying game theory and statistical analysis to discover optimal usage strategies. We show that preferential selection of platforms based on optimizing platform diversity approaches the statistically optimal solution and significantly outperforms simple randomization strategies. Counter to popular belief, this deterministic strategy leverages fewer platforms than may be generally available, which increases system security.
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