MARS: Malware Analysis with Rule-Based Scoring of LLM Claims
Large language models can triage malware through direct verdicts or behavioral claims scored by an external policy. We present MARS, a malware triage framework, and compare direct classification with single-pass claim scoring using the same evidence collector and identical static evidence bundles for each model. The evaluation covers 1,195 PE and ELF binaries grouped into 1,001 near-duplicate clusters and six language models, with deterministic rules providing a baseline. Direct classification is more accurate for all six models. On samples with usable outputs from both paths, its accuracy advantage ranges from 3.7 to 20.9 percentage points, with all 95% cluster-bootstrap confidence inter...
| Authors | Hyeongjun Choi |
|---|---|
| Primary category | cs.CR |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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