Evolving Assembly Code in an Adversarial Environment
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gp-bibliography.bib Revision:1.8051
- @Misc{maliukov2024evolving,
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author = "Irina Maliukov and Gera Weiss and Oded Margalit and
Achiya Elyasaf",
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title = "Evolving Assembly Code in an Adversarial Environment",
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howpublished = "ArXiv",
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year = "2024",
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month = "28 " # mar,
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note = "To appear GECCO 2024",
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keywords = "genetic algorithms, genetic programming, ANN, LLM,
assembly, code generation, cyber-security, CodeGuru
Xtreme",
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eprint = "2403.19489",
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archiveprefix = "arXiv",
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primaryclass = "cs.NE",
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URL = "https://arxiv.org/abs/2403.19489",
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size = "9 pages",
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abstract = "we evolve assembly code for the CodeGuru competition.
The competitions goal is to create a survivor, an
assembly program that runs the longest in shared
memory, by resisting attacks from adversary survivors
and finding their weaknesses. For evolving top-notch
solvers, we specify a Backus Normal Form (BNF) for the
assembly language and synthesise the code from scratch
using Genetic Programming (GP). We evaluate the
survivors by running CodeGuru games against
human-written winning survivors. Our evolved programs
found weaknesses in the programs they were trained
against and used them. In addition, we compare our
approach with a Large-Language Model, demonstrating
that the latter cannot generate a survivor that can win
at any competition. This work has important
applications for cyber-security, as we use evolution to
detect weaknesses in survivors. The assembly BNF is
domain-independent; thus, by modifying the fitness
function, it can detect code weaknesses and help fix
them. Finally, the CodeGuru competition offers a novel
platform for analysing GP and code evolution in
adversarial environments. To support further research
in this direction, we provide a thorough qualitative
analysis of the evolved survivors and the weaknesses
found.",
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notes = "See \cite{Maliukov:2024:GECCOcomp}",
- }
Genetic Programming entries for
Irina Maliukov
Gera Weiss
Oded Margalit
Achiya Elyasaf
Citations