NDLI: Heap Taichi: exploiting memory allocation granularity in heap-spraying attacks

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Heap Taichi: exploiting memory allocation granularity in heap-spraying attacks

Content Provider	ACM Digital Library
Author	Wang, TieLei Ding, Yu Zou, Wei Wei, Tao Liang, Zhenkai
Abstract	Heap spraying is an attack technique commonly used in hijacking browsers to download and execute malicious code. In this attack, attackers first fill a large portion of the victim process's heap with malicious code. Then they exploit a vulnerability to redirect the victim process's control to attackers' code on the heap. Because the location of the injected code is not exactly predictable, traditional heap-spraying attacks need to inject a huge amount of executable code to increase the chance of success. Injected executable code usually includes lots of NOP-like instructions leading to attackers' shellcode. Targeting this attack characteristic, previous solutions detect heap-spraying attacks by searching for the existence of such large amount of NOP sled and other shellcode. In this paper, we analyze the implication of modern operating systems' memory allocation granularity and present Heap Taichi, a new heap spraying technique exploiting the weakness in memory alignment. We describe four new heap object structures that can evade existing detection tools, as well as proof-of-concept heap-spraying code implementing our technique. Our research reveals that a large amount of NOP sleds is not necessary for a reliable heap-spraying attack. In our experiments, we showed that our heap-spraying attacks are a realistic threat by evading existing detection mechanisms. To detect and prevent the new heap-spraying attacks, we propose enhancement to existing approaches and propose to use finer memory allocation granularity at memory managers of all levels. We also studied the impact of our solution on system performance.
Starting Page	327
Ending Page	336
Page Count	10
File Format	PDF
ISBN	9781450301336
DOI	10.1145/1920261.1920310
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-12-06
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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