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Seyedali Mohammadi

2 accepted papers

2025

Can LLMs Obfuscate Code? A Systematic Analysis of Large Language Models into Assembly Code Obfuscation

AAAI 2025technical

Malware authors often employ code obfuscations to make their malware harder to detect. Existing tools for generating obfuscated code often require access to the original source code (e.g., C++ or Java), and adding new obfuscations is a non-trivial, labor-intensive process. In this study, we ask the…

2025

Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions

EMNLP 2025

Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across multiple explanation benchmark datasets (general and domain-specific) and label definition conditions, including expert-cu

Cited by 0SourcePDFScholar