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Countering code-injection attacks with instruction-set randomization

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Kc, Gaurav S., Keromytis, Angelos D., Prevelakis, Vassilis·2003·Proceedings of the 10th ACM conference on Computer and communications security

Abstract

We describe a new, general approach for safeguarding systems against any type of code-injection attack. We apply Kerckhoff's principle, by creating process-specific randomized instruction sets (e.g., machine instructions) of the system executing potentially vulnerable software. An attacker who does not know the key to the randomization algorithm will inject code that is invalid for that randomized processor, causing a runtime exception. To determine the difficulty of integrating support for the proposed mechanism in the operating system, we modified the Linux kernel, the GNU binutils tools, and the bochs-x86 emulator. Although the performance penalty is significant, our prototype demonstrates the feasibility of the approach, and should be directly usable on a suitable-modified processor (e.g., the Transmeta Crusoe).Our approach is equally applicable against code-injecting attacks in scripting and interpreted languages, e.g., web-based SQL injection. We demonstrate this by modifying the Perl interpreter to permit randomized script execution. The performance penalty in this case is minimal. Where our proposed approach is feasible (i.e., in an emulated environment, in the presence of programmable or specialized hardware, or in interpreted languages), it can serve as a low-overhead protection mechanism, and can easily complement other mechanisms.

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Metadata

Authors
Kc, G. S., Keromytis, A. D., & Prevelakis, V.
Venue
Proceedings of the 10th ACM conference on Computer and communications security
Publisher
ACM
Published
2003-10-27
Pages
272-280
ISSN
1543-7221 (ISSN-L)
OpenAlex
W2101699859
Cited by
646
References
0
FWCI
20.62
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Countering code-injection attacks with instruction-set randomization. https://app.cochat.ai/papers/10.1145/948109.948146
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Kc, G. S., Keromytis, A. D., & Prevelakis, V. (2003). Countering code-injection attacks with instruction-set randomization. Proceedings of the 10th ACM conference on Computer and communications security. https://doi.org/10.1145/948109.948146

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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.