Wargames-Style CTF

• Wargames-style CTF is a cybersecurity exercise characterized by open-ended, level-based challenges where participants exploit system vulnerabilities to progress.
• The format supports incremental learning and authentic tool usage through hands-on interactions with live or virtualized systems, making it ideal for cyber education and training.
• Wargames CTFs are used for benchmarking performance by both human teams and autonomous agents through metrics like flag retrieval rates and time-decay scoring.

Wargames-style Capture the Flag (CTF) refers to a cybersecurity exercise format characterized by open-ended, progressively-leveled challenges in which participants interact with live systems—typically over SSH or VPN—enumerate and exploit vulnerabilities to retrieve flag strings, and advance through increasingly complex “levels.” These competitions, situated within the “Gamified/Wargames” class in the established four-fold CTF taxonomy (attack-based, defense-based, jeopardy, gamified/wargames), emphasize deep, hands-on interaction with real or simulated environments in contrast to discrete, point-in-time puzzle-solving seen in jeopardy CTFs. Wargames CTFs are widely employed for education, autonomous agent benchmarking, adversarial training, and cyber operations modeling, providing a rigorous, scalable platform for the incremental acquisition and demonstration of offensive and defensive cybersecurity skills (Lyu et al., 24 Jan 2026).

1. Formal Characteristics and Taxonomy

Wargames-style CTFs are defined by open-endedness, the absence of strict time limits, and a level-based narrative progression. Participants typically interact with a hierarchy of levels—denoted $L_1, L_2, \dots, L_n$—each containing exploitable services or files with embedded flags $F_i$. Upon successful exploitation and flag submission (via command-line or web interface), access is granted to the next level. Unlike attack-defense or king-of-the-hill competitions, which feature team-vs-team dynamics, wargames CTFs often follow a single-player or small-team model, prioritizing exploration, experimentation, and progressively deeper system compromise (Lyu et al., 24 Jan 2026).

Table: Distinguishing Features of CTF Formats

Feature	Wargames-Style	Jeopardy-Style	Attack/Defense
Time limit	None/loose	Fixed/short	Fixed/long
Progression	Level-based	Flat (one-shot)	Continuous, dynamic
Interaction	Live systems	Isolated puzzles	Real services, vs.
Competition	Non/optional	Score table	Head-to-head
Learning Model	Incremental	Burst/problem-based	Simultaneous attack

The wargames format thus supports deep system-level learning and authentic tool usage in a controlled but realistic environment (Lyu et al., 24 Jan 2026).

2. Workflow, Infrastructure, and Level Design

A canonical wargames CTF workflow includes:

• Enumeration: Starting with minimal access (e.g., an SSH session or open port), participants use reconnaissance tools (e.g., nmap, netcat, ssh) to enumerate accessible services and attack surfaces.
• Exploitation and Escalation: Identified vulnerabilities—such as misconfigured SUID binaries, outdated network services, or improper access controls—are exploited to access protected files or escalate privileges. Real-world tools (gdb, strace, wireshark) are used for local analysis and exploit development.
• Flag Submission and Progression: The retrieved flag $F_i$ is submitted. If correct, access is granted to level $i+1$; otherwise, participants are prompted to continue investigation.
• Progressive Difficulty: Initial levels reinforce foundational skills (e.g., basic shell commands, file I/O). Subsequent levels require chaining multiple exploits, pivoting between hosts, or protocol fuzzing (Lyu et al., 24 Jan 2026).

Techniques for reliable CTF infrastructure emphasize containerization (Docker, Vagrant), level isolation, automated resets, and reproducible environments with known-good snapshots to prevent stale state between attempts (Lyu et al., 24 Jan 2026, Zhuo et al., 25 Aug 2025). Automated pipelines, e.g., CTF-Forge, can provision and validate hundreds of Dockerized challenges in minutes (Zhuo et al., 25 Aug 2025).

3. Scoring Models, Metrics, and Benchmarking

Scoring in wargames CTFs follows two broad models:

• Non-Competitive: Success is defined as reaching the deepest level ($N$), with participants evaluated by final level achieved.
• Competitive: Leaderboards rank participants by total levels completed and aggregate time to completion. Representative formulae include

$$\mathrm{Score}(u) = \sum_{i=1}^L \frac{w_i}{t_{u,i}}$$

and

$P_i(u) = p_i \cdot e^{-\alpha \cdot \tau_i(u)}$

with base points $p_i$, elapsed time $\tau_i(u)$, and $\alpha$ controlling time-decay steepness. Total score is obtained via $F_i$0 (Lyu et al., 24 Jan 2026).

For benchmarking automated agents, partial-credit schemes decompose each challenge into checkpoints guided by solution write-ups. The DeepRed framework, for example, benchmarks LLM agents on realistic VM-based challenges, computing normalized score

$F_i$1

where $F_i$2 indicates checkpoint completion (Al-Kaswan et al., 21 Apr 2026). LLM-based scoring frameworks such as CTFJudge further rate candidate submissions against human expert trajectories across multi-factor indices, e.g., vulnerability understanding, exploitation methodology, and adaptability, aggregated into a CTF Competency Index (CCI) (Shao et al., 5 Aug 2025).

4. Pedagogical Outcomes and Training Use Cases

Wargames-style CTFs are integrated into curricula and cyber training pipelines owing to their effectiveness in developing deep, system-level competencies:

• Learning Objectives: Mastery of Linux internals, standard attack chains, reverse engineering, privilege escalation, pivoting, and network analysis. Participants become fluent with real-world tools in authentic but safe contexts (Lyu et al., 24 Jan 2026).
• Design Patterns: Progressive skill scaffolding (objective sequence $F_i$3), incremental hints (unlocked after $F_i$4 or on demand), and post-level writeups to reinforce learning and model solutions foster engagement and retention (Lyu et al., 24 Jan 2026).
• Exemplars: OverTheWire Bandit introduces layers of command-line and scripting tasks; SmashTheStack Protostar focuses on buffer overflows; VulnHub’s Kioptrix series requires multi-host enumeration and post-exploitation cleanup (Lyu et al., 24 Jan 2026).
• Assessment: Empirical studies show gains in keystroke accuracy and frequency of engagement across MITRE ATT&CK phases, enabling fine-grained performance measurement and adaptive scaffolding for human learners and agents alike (Savin et al., 2023).

Wargames CTFs are also used in scenario-based training for cloud security (Thunder CTF), secure-coding awareness, and red team/blue team adversarial simulations (Springer et al., 2021, Gasiba et al., 2021).

5. Autonomous Agent and Benchmarking Paradigms

The wargames CTF format is a de facto standard for benchmarking executable-agent learning, particularly for LLM-based agents:

• Execution-Grounded Benchmarks: Frameworks such as CTF-Dojo and DeepRed provide hundreds of containerized challenges or isolated VMs, supporting reproducibility and fine-grained step validation (Zhuo et al., 25 Aug 2025, Al-Kaswan et al., 21 Apr 2026).
• Agent-Environment Interaction: LLM agent toolkits (EnIGMA, STRIATUM-CTF) expose protocol-driven interfaces for system introspection, network probing, decompilation, and runtime debugging via JSON-RPC or specialized Agent-Computer Interfaces (ACIs), enforcing schema compliance to reduce hallucination (Hugglestone et al., 23 Mar 2026, Abramovich et al., 2024).
• Assessment and Leaderboards: CTFusion proposes live-streamed evaluation on unreleased CTF challenges to address contamination and web-RAG cheating, using Model Context Protocol (MCP) servers to unify agent and event interfaces (Lee et al., 12 May 2026).
• Performance Characteristics: LLM fine-tuning on curated, high-quality trajectories yields pass@1 rates approaching or exceeding strong baselines (e.g., Qwen3-Coder-32B: 31.9% average). Performance is highly sensitive to agent reasoning depth, context compression, tool chaining, and robustness to novel or dynamic environments. Human teams are still routinely outperformed in highly structured wargame events by the best autonomous agent configurations (Zhuo et al., 25 Aug 2025, Hugglestone et al., 23 Mar 2026).

6. Design Challenges, Cognitive Factors, and Best Practices

Wargames-style CTFs entail nontrivial challenges in infrastructure, onboarding, and participant psychology:

• Entry Barriers and Infrastructure: Novices may require onboarding (first-level tutorials, supportive hints); hosting and resetting live environments demand substantial sysadmin effort (Lyu et al., 24 Jan 2026). Containerization and automated reset logic are essential.
• Hint and Feedback Systems: Hints per level and configurable unlock timers ($F_i$5) prevent frustration and stagnation, with penalties calibrated to be small relative to base points (typically 5-10% per hint) (Lyu et al., 24 Jan 2026, Gasiba et al., 2021).
• Cognitive Biases: Human decision-making in CTFs is affected by Satisfaction of Search and Loss Aversion. Empirical findings show SoS can reduce flag discovery by ~25%, motivating deliberate design of decoys, ambiguous flag counts, and risk-reward choices to elicit or mitigate specific attacker behaviors (Yang et al., 17 May 2025).
• Deceptive Dynamics: In attack-defense variants, deceptive behavior such as payload re-use, false flag submission, and honeypots are prevalent. Game-theoretic models (Markov games, Bayesian Stackelberg equilibria, adversarial/dueling knapsack) formalize optimal defense strategies in the presence of deception and resource constraints (Bhambri et al., 2022, Goohs et al., 2024, Nunes et al., 2015).
• Teamwork and Soft Skills: Optimal team sizes (usually ≤4), effective communication, and ethical conduct are correlated with higher performance, suggesting value in incorporating soft-skill assessment into wargame-style events (Goetgheluck et al., 2024).

Table: Example Design and Assessment Elements

Element	Application	Reference
Level hints ($F_i$6)	Scaffolding, remediation	(Lyu et al., 24 Jan 2026)
Time penalty ($F_i$7)	Incentivizing efficiency	(Lyu et al., 24 Jan 2026)
Decoy flags	SoS manipulation	(Yang et al., 17 May 2025)
MCP/ACI schemas	Hallucination mitigation (LLM)	(Hugglestone et al., 23 Mar 2026, Abramovich et al., 2024)
Checkpoint rubric	Partial-credit agent scoring	(Al-Kaswan et al., 21 Apr 2026, Shao et al., 5 Aug 2025)

7. Extensions and Research Directions

Wargames-style CTFs are a foundation for advancing both cyber pedagogy and the development of autonomous cyber operators. Ongoing and emerging research focuses on:

• Distributed and Cloud-native CTFs: Scenario-based cloud challenges (Thunder CTF) extend wargames logic to cloud-native architectures, integrating real-world misconfiguration case studies (Springer et al., 2021).
• Live Streaming Agent Benchmarks: Dynamic evaluation frameworks (CTFusion) and MCP protocols address reproducibility, fairness, and contamination in LLM agent evaluation (Lee et al., 12 May 2026).
• Game-Theoretic Modeling: Dueling/adversarial knapsack and Markov game models formalize attacker-defender resource constraints, allowing secondary reasoning and empirical validation in CTF environments (Bhambri et al., 2022, Goohs et al., 2024).
• Real-Time Human and Agent Metrics: Incorporation of keystroke accuracy, action-type labeling (MITRE ATT&CK), and partial-credit benchmarks bridges the gap between black-box metrics and instructional insight (Savin et al., 2023, Al-Kaswan et al., 21 Apr 2026, Shao et al., 5 Aug 2025).
• Cognitive Engineering: Bias-aware CTF design, dynamic hinting, and adaptive challenge difficulty contribute to realistic attacker/defender simulation and more effective measurement (Yang et al., 17 May 2025).

Systematic adoption of these best practices and research frameworks enables wargames-style CTFs to remain at the forefront of cybersecurity education, skills assessment, and autonomous agent evaluation. By combining real-world systems, robust automation, scalable design, and nuanced assessment, wargames-style CTFs deliver a uniquely powerful and evolving platform for the study and practice of offensive and defensive cyber operations (Lyu et al., 24 Jan 2026, Zhuo et al., 25 Aug 2025, Hugglestone et al., 23 Mar 2026, Lee et al., 12 May 2026).