A dynamic mechanism for recovering from buffer overflow attacks

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Abstract

We examine the problem of containing buffer overflow attacks in a safe and efficient manner. Briefly, we automatically augment source code to dynamically catch stack and heap-based buffer overflow and underflow attacks, and recover from them by al lowing the program to continue execution. Our hypothesis is that we can treat each code function as a transaction that can be aborted when an attack is detected, without affecting the application's ability to correctly execute. Our approach allows us to enable selectively or disable components of this defensive mechanism in response to external events, allowing for a direct tradeoff between security and performance. We combine our defensive mechanism with a honcypot-like configuration to detect previously unknown attacks, automatically adapt an application's defensive posture at a negligible performance cost, and help determine worm signatures. Our scheme provides low impact on application performance, the ability to respond to attacks without human intervention, the capacity to handle previously unknown vulnerabilities, and the preservation of service availability. We implement a stand-alone tool, DYBOC, which we use to instrument a number of vulnerable applications. Our performance benchmarks indicate a slow-down of 20% for Apache in full-protection mode, and 1.2% with selective protection. We provide preliminary evidence towards the validity of our transactional hypothesis via two experiments: first, by applying our scheme to 17 vulnerable applications, successfully fixing 14 of them; second, by examining the behavior of Apache when each of 154 potentially vulnerable routines are made to fail, resulting in correct behavior in 139 cases (90%), with similar results for sshd (89%) and Bind (88%). © Springer-Verlag Berlin Heidelberg 2005.

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APA

Sidiroglou, S., Giovanidis, G., & Keromytis, A. D. (2005). A dynamic mechanism for recovering from buffer overflow attacks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3650 LNCS, pp. 1–15). Springer Verlag. https://doi.org/10.1007/11556992_1

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