LATIDIA · Ciberseguridad

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arXiv:2611.02788v1 Anuncio Tipo: nuevo Resumen: La ingeniería inversa de binarios ofuscados es una tarea fundamental en el análisis de malware y la auditoría de seguridad de software. Si bien los descompiladores neuronales basados ​​en grandes modelos de lenguaje (LLM) han

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arXiv:2611.02788v1 Announce Type: new Abstract: Reverse engineering obfuscated binaries is a core task in malware analysis and software security auditing. While neural decompilers based on Large Language Models (LLMs) have improved the readability and functional fidelity of recovered code over heuristic tools such as Ghidra and IDA Pro, a substantial gap remains between best-sample performance and practical single-output accuracy, particularly under code obfuscation. We study this gap through the lens of candidate generation and candidate selection. Using the LLM4Decompile architecture, we show that ground-truth tokens lie within the model's top-five next-token predictions in 95.6% of positions on average and that multi-sample decoding reaches a pass@100 of 93% on HumanEval-Decompile, indicating