Title
Smokestack: thwarting DOP attacks with runtime stack layout randomization
Abstract
Memory corruption vulnerabilities in type-unsafe languages are often exploited to perform a control-flow hijacking attack, in which an attacker uses vulnerabilities to corrupt control data in the program to eventually gain control over the execution of the program. However, widespread adoption of control-flow attack defenses such as Control-flow Integrity(CFI) has led attackers to exploit memory errors to corrupt non-control data that can not be detected by these defenses. Non-control data attacks can be used to corrupt security critical data or leak sensitive information. Moreover, recent attacks such as data-oriented programming (DOP) have generalized non-control data attacks to achieve Turing-complete computation capabilities within the programmer-specified control-flow graph, leaving previously proposed control-flow protections unable to stop these attacks. In this paper, we present a stack-layout randomization scheme that can effectively thwart DOP attacks. Our approach, called Smokestack, provides each function invocation with a randomly permuted ordering of the local stack organization. In addition, we utilize true-random value sources combined with disclosure-resistant pseudo-random number generation to ensure that an adversary cannot anticipate a function’s invocation permutation of automatic variables. Our evaluation on SPEC benchmarks and various real-world applications shows that Smokestack can stop DOP attacks with minimal overhead.
Year
DOI
Venue
2019
10.1109/CGO.2019.8661202
Proceedings of the 2019 IEEE/ACM International Symposium on Code Generation and Optimization
Keywords
Field
DocType
DOP attack, stack-layout randomization
Automatic variable,Memory corruption,Computer security,Computer science,Permutation,Exploit,Real-time computing,Adversary,Spec#,Information sensitivity,Memory errors
Conference
ISSN
ISBN
Citations 
2164-2397
978-1-7281-1436-1
1
PageRank 
References 
Authors
0.35
0
2
Name
Order
Citations
PageRank
Misiker Tadesse Aga181.49
Todd M. Austin2384.71