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On the effectiveness of address-space randomization

Shacham, Hovav, Page, Matthew, Pfaff, Ben, Goh, Eu-Jin, Modadugu, Nagendra, Boneh, Dan·2004·Proceedings of the 11th ACM conference on Computer and communications security

Abstract

Address-space randomization is a technique used to fortify systems against buffer overflow attacks. The idea is to introduce artificial diversity by randomizing the memory location of certain system components. This mechanism is available for both Linux (via PaX ASLR) and OpenBSD. We study the effectiveness of address-space randomization and find that its utility on 32-bit architectures is limited by the number of bits available for address randomization. In particular, we demonstrate a derandomization attack that will convert any standard buffer-overflow exploit into an exploit that works against systems protected by address-space randomization. The resulting exploit is as effective as the original exploit, although it takes a little longer to compromise a target machine: on average 216 seconds to compromise Apache running on a Linux PaX ASLR system. The attack does not require running code on the stack.

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Metadata

Authors
Shacham, H., Page, M., Pfaff, B., Goh, E.-J., Modadugu, N., & Boneh, D.
Venue
Proceedings of the 11th ACM conference on Computer and communications security
Publisher
ACM
Published
2004-10-25
Pages
298-307
ISSN
1543-7221 (ISSN-L)
OpenAlex
W2098010707
Cited by
861
References
0
FWCI
14.53
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: On the effectiveness of address-space randomization. https://app.cochat.ai/papers/10.1145/1030083.1030124
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Shacham, H., Page, M., Pfaff, B., Goh, E.-J., Modadugu, N., & Boneh, D. (2004). On the effectiveness of address-space randomization. Proceedings of the 11th ACM conference on Computer and communications security. https://doi.org/10.1145/1030083.1030124

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.