From Billion-Scale to Million-Scale Language Models: Operational Capabilities for Embedded Post-Exploitation
Small Language Models (SLMs) are increasingly attractive for resource-constrained environments due to their low computational footprint. These characteristics also make them candidates for future autonomous malware, where connectivity, latency, privacy, or operational constraints may preclude cloud-based inference. Yet the ability of ultra-compact models to support malware during post-exploitation remains poorly understood. We systematically evaluate seven open-source SLMs (20M-1.5B parameters) on two post-exploitation code-generation tasks: Directory Enumeration and Backdoor Generation. We compare Python (high-resource) and Lua (low-resource) across prompting strategies, task formulation...
| Authors | Gabriel Hortea, Simon Pietro Romano, Juan Tapiador et al. |
|---|---|
| Primary category | cs.CR |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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