Evaluating Context Segmentation in Locally Deployable SLMs for Cybersecurity CTF Tasks
The proliferation of highly capable open-weight Small Language Models (SLMs) democratizes access to advanced cybersecurity capabilities, posing an 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 context segmentation, a two-level agentic framework that divides complex exploitation tasks into manageable, contextually isolated sub-problems. Evaluating on...
| Authors | Sebastiano Nordio, Michele Lotto |
|---|---|
| Primary category | cs.CR |
| Published | 11 September 2026 |
| Last updated at source | 15 September 2026 |
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