小模型本地执行提权攻击成功率仅8%，通过五项技术提升至67%。

Cloud-based Large Language Models (LLMs) can perform autonomous penetration-testing sub-tasks such as Linux privilege escalation, but raise security, privacy, and sovereignty concerns. Locally hosted open-weight models avoid these issues, yet prior work reports that small open-weight models succeed on only 8-16% of standardized privilege-escalation tasks, far below frontier cloud models. This paper is an empirical study of why small models fail at this task and which engineering techniques close the gap. From execution traces we distill six recurring failure modes, map each to an established enhancement technique, and evaluate five (chain-of-thought prompting, retrieval-augmented generation, structured prompting, history compression, and reflective analysis) as reproducible extensions to the open-source hackingBuddyGPT framework. Under a single shared harness and matched conditions, the set of techniques we evaluate raise two SLMs (Llama3.1 8B, Qwen2.5 7B) from 8% to 67% with guidance, matching guided GPT-4o. A larger open-weight reference model (Llama3.1 70B) reaches 83%. A full-factorial ablation shows that reflection-based techniques contribute most and reveals vulnerability discovery, not exploitation, as the main constraint for local models. We report these as transferable lessons for building reliable local offensive agents as well as to inform defenders.