🤖 AI Summary
研究通过引入上下文分割方法，解决了本地部署的小型语言模型在处理网络安全CTF任务时因上下文膨胀导致的问题。
📝 Abstract
The proliferation of highly capable open-weight Small Language Models (SLMs) democratizes access to advanced cybersecurity capabilities, posing a escalating risk as these models can bypass proprietary API guardrails when deployed locally. However, SLMs deployed as autonomous agents often struggle with long-horizon, exploratory tasks like cybersecurity Capture The Flag (CTF) challenges due to context bloat and cognitive degradation from accumulated tool-call outputs. To understand and mitigate this cybersecurity threat, we introduce \textit{context segmentation}, a two-level agentic framework that divides complex exploitation tasks into manageable, contextually isolated sub-problems. Evaluating on the \texttt{picoCTF} dataset using memory-constrained \texttt{gemma-4} models, we demonstrate that for the E4B model, our strategy acts as an intelligent search, achieving competitive rewards with superior token efficiency compared to brute-force retries, and successfully solving 18.52\% of tasks that standard agentic execution fails to complete. Code is available at https://github.com/9xeb/context-segmentation.