Beyond Classification: Evaluating LLMs for Fine-Grained Automatic Malware Behavior Auditing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908544971833344 |
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| author | Zheng, Xinran Qian, Xingzhi He, Yiling Yang, Shuo Cavallaro, Lorenzo |
| author_facet | Zheng, Xinran Qian, Xingzhi He, Yiling Yang, Shuo Cavallaro, Lorenzo |
| contents | Automated malware classification has achieved strong detection performance. Yet, malware behavior auditing seeks causal and verifiable explanations of malicious activities -- essential not only to reveal what malware does but also to substantiate such claims with evidence. This task is challenging, as adversarial intent is often hidden within complex, framework-heavy applications, making manual auditing slow and costly. Large Language Models (LLMs) could help address this gap, but their auditing potential remains largely unexplored due to three limitations: (1) scarce fine-grained annotations for fair assessment; (2) abundant benign code obscuring malicious signals; and (3) unverifiable, hallucination-prone outputs undermining attribution credibility. To close this gap, we introduce MalEval, a comprehensive framework for fine-grained Android malware auditing, designed to evaluate how effectively LLMs support auditing under real-world constraints. MalEval provides expert-verified reports and an updated sensitive API list to mitigate ground truth scarcity and reduce noise via static reachability analysis. Function-level structural representations serve as intermediate attribution units for verifiable evaluation. Building on this, we define four analyst-aligned tasks -- function prioritization, evidence attribution, behavior synthesis, and sample discrimination -- together with domain-specific metrics and a unified workload-oriented score. We evaluate seven widely used LLMs on a curated dataset of recent malware and misclassified benign apps, offering the first systematic assessment of their auditing capabilities. MalEval reveals both promising potential and critical limitations across audit stages, providing a reproducible benchmark and foundation for future research on LLM-enhanced malware behavior auditing. MalEval is publicly available at https://github.com/ZhengXR930/MalEval.git |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14335 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Classification: Evaluating LLMs for Fine-Grained Automatic Malware Behavior Auditing Zheng, Xinran Qian, Xingzhi He, Yiling Yang, Shuo Cavallaro, Lorenzo Cryptography and Security Artificial Intelligence Software Engineering Automated malware classification has achieved strong detection performance. Yet, malware behavior auditing seeks causal and verifiable explanations of malicious activities -- essential not only to reveal what malware does but also to substantiate such claims with evidence. This task is challenging, as adversarial intent is often hidden within complex, framework-heavy applications, making manual auditing slow and costly. Large Language Models (LLMs) could help address this gap, but their auditing potential remains largely unexplored due to three limitations: (1) scarce fine-grained annotations for fair assessment; (2) abundant benign code obscuring malicious signals; and (3) unverifiable, hallucination-prone outputs undermining attribution credibility. To close this gap, we introduce MalEval, a comprehensive framework for fine-grained Android malware auditing, designed to evaluate how effectively LLMs support auditing under real-world constraints. MalEval provides expert-verified reports and an updated sensitive API list to mitigate ground truth scarcity and reduce noise via static reachability analysis. Function-level structural representations serve as intermediate attribution units for verifiable evaluation. Building on this, we define four analyst-aligned tasks -- function prioritization, evidence attribution, behavior synthesis, and sample discrimination -- together with domain-specific metrics and a unified workload-oriented score. We evaluate seven widely used LLMs on a curated dataset of recent malware and misclassified benign apps, offering the first systematic assessment of their auditing capabilities. MalEval reveals both promising potential and critical limitations across audit stages, providing a reproducible benchmark and foundation for future research on LLM-enhanced malware behavior auditing. MalEval is publicly available at https://github.com/ZhengXR930/MalEval.git |
| title | Beyond Classification: Evaluating LLMs for Fine-Grained Automatic Malware Behavior Auditing |
| topic | Cryptography and Security Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2509.14335 |