AsmRAG: LLM-Driven Malware Detection by Retrieving Functionally Similar Assembly Code

• URL: http://arxiv.org/abs/2604.23196v1
• Date: Sat, 25 Apr 2026 08:05:50 GMT
• Title: AsmRAG: LLM-Driven Malware Detection by Retrieving Functionally Similar Assembly Code
• Abstract summary: We introduce AsmRAG, a framework performing malware analysis through Assembly-Level Retrieval-Augmented Generation.<n>The system uses a code-specialized Large Language Model (LLM) to analyze assembly functions and convert them into semantic embeddings.<n>For inference, we propose a Density-Weighted Anchor Selection mechanism that isolates the primary unit of malicious logic within a binary to extract verifiable forensic evidence and resist evasion attempts.
• Score: 1.2183405753834562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning malware detectors achieve high classification accuracy but suffer from severe interpretability limitations, typically returning probabilistic verdicts that lack forensic context. We introduce AsmRAG, a framework performing malware analysis through Assembly-Level Retrieval-Augmented Generation. Unlike classifiers built on global statistical features, AsmRAG reformulates detection as an evidence-based retrieval task. The system uses a code-specialized Large Language Model (LLM) to analyze assembly functions and convert them into semantic embeddings. This process constructs a searchable knowledge base resilient to syntactic obfuscation. For inference, we propose a Density-Weighted Anchor Selection mechanism that isolates the primary unit of malicious logic within a binary to extract verifiable forensic evidence and resist evasion attempts. Testing on a curated dataset of 40k binaries shows AsmRAG reaching a detection F1-score of 96% alongside a family attribution F1-score of 95%. Comparisons confirm this semantic retrieval approach remains robust against metamorphic obfuscation. When holistic baselines (EMBER and ResNeXt) degrade, our methodology gives Security Operations Centers a transparent and reliable alternative.

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