# Can AI Lower the Barrier to Cybersecurity? A Human-Centered Mixed-Methods Study of Novice CTF Learning

arXiv: https://arxiv.org/abs/2602.18172 · HTML: https://mlsanity.com/paper/2602.18172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 20 Feb 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 9 of 9)

- [Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models](https://mlsanity.com/paper/2408.08926/) (2024)
- [Getting pwn'd by AI: Penetration Testing with Large Language Models](https://mlsanity.com/paper/2308.00121/) (2023)
- [How AI Impacts Skill Formation](https://mlsanity.com/paper/2601.20245/) (2026)
- [Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design](https://mlsanity.com/paper/2504.10112/) (2025)
- [Cybersecurity AI Benchmark (CAIBench): A Meta-Benchmark for Evaluating Cybersecurity AI Agents](https://mlsanity.com/paper/2510.24317/) (2025)
- [On the Surprising Efficacy of LLMs for Penetration-Testing](https://mlsanity.com/paper/2507.00829/) (2025)
- [Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges](https://mlsanity.com/paper/2101.01421/) (2021)
- [Evaluating AI cyber capabilities with crowdsourced elicitation](https://mlsanity.com/paper/2505.19915/) (2025)
- [The Architecture of Cognitive Amplification: Enhanced Cognitive Scaffolding as a Resolution to the Comfort-Growth Paradox in Human-AI Cognitive Integration](https://mlsanity.com/paper/2507.19483/) (2025)

## Abstract (arXiv)

Capture-the-Flag (CTF) competitions serve as gateways into offensive cybersecurity, yet they often present steep barriers for novices due to complex toolchains and opaque workflows. Recently, agentic AI frameworks for cybersecurity promise to lower these barriers by automating and coordinating penetration testing tasks. However, their role in shaping novice learning remains underexplored. We present a human-centered, mixed-methods case study examining how agentic AI frameworks -- here Cybersecurity AI (CAI) -- mediates novice entry into CTF-based penetration testing. An undergraduate student without prior hacking experience attempted to approach performance benchmarks from a national cybersecurity challenge using CAI. Quantitative performance metrics were complemented by structured reflective analysis of learning progression and AI interaction patterns. Our thematic analysis suggest that agentic AI reduces initial entry barriers by providing overview, structure and guidance, thereby lowering the cognitive workload during early engagement. Quantitatively, the observed extensive exploration of strategies and low per-strategy execution time potetially facilitatates cybersecurity training on meta, i.e. strategic levels. At the same time, AI-assisted cybersecurity education introduces new challenges related to trust, dependency, and responsible use. We discuss implications for human-centered AI-supported cybersecurity education and outline open questions for future research.