Evaluating Context Segmentation in Locally Deployable SLMs for Cybersecurity CTF Tasks
Context segmentation framework boosts memory-constrained gemma-4 agents on picoCTF, solving 18.52% of tasks standard execution fails, highlighting local SLM offensive risk.
The paper introduces context segmentation, a two-level agentic framework that divides long-horizon CTF exploitation tasks into contextually isolated sub-problems to counter context bloat and cognitive degradation from accumulated tool-call outputs. It evaluates memory-constrained gemma-4 models on the picoCTF dataset; the E4B model achieves competitive rewards with superior token efficiency compared to brute-force retries. It solves 18.52% of tasks that standard agentic execution fails to complete. The work frames locally deployed open-weight SLMs as an escalating risk since they bypass proprietary API guardrails; code is released on GitHub.
- Two-level agentic framework splits CTF tasks into isolated sub-problems.
- gemma-4 E4B achieves competitive rewards with superior token efficiency.
- Solves 18.52% of picoCTF tasks standard agentic execution fails.
- Locally deployed open-weight SLMs bypass proprietary API guardrails.
From the original article
1 min read at arxiv.orgThe 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.
By Sebastiano Nordio · arxiv.org
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