Local LLMs Can Be Enhanced for Linux Privilege Escalation

A new study from arXiv (2604.27143) systematically evaluates whether targeted system-level and prompting interventions can improve the performance of small open-weight Large Language Models (LLMs) on autonomous Linux privilege escalation tasks. The research addresses security, privacy, and sovereignty concerns associated with cloud-based restricted-weight models by focusing on locally hosted alternatives. The authors analyze failure modes of open-weight models in this context and map them to established enhancement techniques. They implement five concrete interventions—chain-of-thought prompting, retrieval-augmented generation, structured prompts, history compression, and reflective analysis—as extensions to an existing framework. The study aims to bridge the performance gap between small open-weight models and their larger cloud-based counterparts, potentially enabling more secure and private autonomous penetration testing.