An arXiv paper demonstrates that orchestrating multiple small, free-to-use language models can outperform a single large LLM for analyzing malware detonation reports covering filesystem, network, and process behaviors. The approach enables rapid interpretation of complex security data without expensive proprietary models.
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arXiv:2607.20216v1 Announce Type: new Abstract: Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours. While large language models (LLMs) demonstrate impressive capabilities for technical artifact interpretation, the opacity and escalating API costs of closed-weight frontier models motivate exploration of open-weight altern
Small, Free, And Effective: Orchestrating Open-Weight Small Language Models To Outperform Single LLM For Malware Analysis https:// packetstorm.news/files/226638 # paper