Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis
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 alternatives. However, many open-weight models are large, demanding significant compute resources and incurring non-trivial hosting costs that place them beyond reach for resource-constrained deployments. This paper investigates whether orchestrated ense
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- PossiblePossibly related (embedding) · 54%FreeBSoD: Leveraging Language Models to Find and Exploit Kernel Bugs (Part 2 of 2) - Security Boulevard →
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- PossiblePossibly related (embedding) · 51%Megalodon cyberattack infects 5,500 GitHub open-source repositories with malware, researchers say - Mashable →
- LinkedLinked via arxiv author · 85%Adel ElZemity →
“Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysi”
- LinkedLinked via arxiv author · 85%Shujun Li →
“Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysi”
- LinkedLinked via arxiv author · 85%Budi Arief →
“Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysi”
- PossiblePossibly related (embedding) · 46%ChainDrop worm crawls into npm supply chain, evades standard defenses →