Evaluating Context Segmentation in Locally Deployable SLMs for Cybersecurity CTF Tasks

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 this https URL.

Accepted at the RAISE 2026 Workshop (ESORICS 2026). Non-archival

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2609.12839 [cs.CR]

(or arXiv:2609.12839v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2609.12839 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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