CTF Battlegrounds: Attack & Defense

• Attack/defense CTF battlegrounds are competitive environments where adversarial teams simulate realistic cyber operations through controlled attack and defense scenarios.
• Empirical studies measure AI effectiveness using metrics such as initial access and patch success, revealing that defensive advantages diminish under operational constraints.
• Advanced frameworks employing parallel agent deployment and taxonomy mapping provide actionable insights for refining automated cybersecurity strategies.

Attack/Defense Capture-the-Flag (CTF) battlegrounds are controlled environments where adversarial teams simultaneously attempt to penetrate (attack) and protect (defend) a set of networked systems or software services. These competitive scenarios serve as testbeds for cyber operations research, realistic threat modeling, education, and the empirical evaluation of both human and AI-driven offensive and defensive capabilities. The attack/defense CTF paradigm directly reflects the multi-agent complexities, resource constraints, and behavioral factors present in real-world cybersecurity, making it an indispensable methodology for both academic inquiry and technological development.

1. Empirical Evaluation of Offense and Defense Effectiveness

Recent controlled studies employing autonomous AI agents in attack/defense CTF environments reveal nuanced performance dynamics. When measuring basic offensive success—initial access (binary exploit or shell compromise)—autonomous offensive agents achieved a mean 28.3% success rate across 23 battleground deployments. Defensive agents measured in terms of unconstrained patching achieved a considerably higher 54.3% success rate, a statistically significant difference (Fisher’s exact test, $p=0.0193$; Cohen’s $h \approx -0.537$; 95% CIs [17.3%, 42.5%] for offense, [40.2%, 67.8%] for defense) (Balassone et al., 20 Oct 2025).

However, this apparent defensive advantage is highly sensitive to operational constraints. When defense is required to maintain service availability (operational defense, 23.9% success) or to prevent all intrusions while keeping the service online (complete defense, 15.2% success), the defensive success rate drops; the statistical difference between defense and offense success vanishes ($p > 0.05$). These findings directly demonstrate that defensive effectiveness is critically dependent on the operational definition of “success”—a factor often overlooked in conceptual analyses.

Metric	Offense (Initial Access)	Defense (Unconstrained Patch)	Defense (Operational)	Defense (Complete)
Success Rate (%)	28.3	54.3	23.9	15.2
Stat. Significance	Baseline	$p=0.0193$ vs. offense	$p>0.05$	$p>0.05$

2. Parallel Agent Execution and Experiment Design

Advanced frameworks now orchestrate simultaneous deployment of offensive and defensive AI agents in attack/defense CTFs. For instance, the CAI (Cybersecurity AI) framework structures teams such that a Red Team (OFF) and Blue Team (DEF) agent—each running in isolated containers—are executed in parallel on identical vulnerable hosts, under fixed operational windows (15 minutes per match) and with carefully staged credential access (offense: IP only; defense: full SSH) (Balassone et al., 20 Oct 2025). This methodology enables precise, high-throughput measurement of agentic cybersecurity effectiveness and supports statistical rigor through paired deployments and constrained environments.

3. Statistical and Taxonomic Analysis of Vulnerability Exploitation

Agent performance is further dissected by mapping outcomes to established security taxonomies: MITRE ATT&CK, CWE, and CAPEC. For example, success rates vary markedly by CWE class—with attack techniques targeting CWE-78 (OS Command Injection) attaining a 50% offensive success rate, whereas attacks against CWE-89 (SQL Injection) yielded 0% success but were consistently detected and patched by defensive agents (Balassone et al., 20 Oct 2025). Sample sizes remain limited, so these observations serve as exploratory signals; future studies with larger datasets and paired statistical methodologies are required for definitive conclusions.

The framework for statistical analysis includes non-parametric hypothesis testing (e.g., Fisher’s exact test for proportion comparisons), effect-size calculations (Cohen’s $h$), and confidence intervals (Wilson’s method). Time-to-event analyses and more expressive multi-stage metrics are identified as needed future enhancements.

4. Impact of Operational Constraints and Framework Design

A key empirical result is that unconstrained defense (e.g., indiscriminate patching) does not reflect realistic deployment demands. Once defenses must maintain system availability or ensure zero intrusions, their advantage over offense dissipates. This aligns CTF battleground evaluation more closely with real-world blue team requirements.

The success/failure dichotomy is expanded to include multi-dimensional metrics: vulnerability detection, service uptime (“availability points”), and own/capture points. Defensive agent performance is nontrivially degraded when forced to balance hyper-proactive remediation with operational continuity—highlighting the necessity for open-source, flexible Cybersecurity AI frameworks to ensure parity in the evolving automated threat landscape (Balassone et al., 20 Oct 2025).

5. Implications for Practice and Future Research

Findings from attack/defense CTF battlegrounds have direct implications for both AI cybersecurity development and operational doctrine:

• Defensive AI agents, when unconstrained, appear to outperform their offensive counterparts, but this advantage disappears under realistic operational requirements.
• Taxonomy-centric evaluation suggests certain vulnerabilities (e.g., command injection) are particularly susceptible to both automated exploitation and defense, whereas database-focused weaknesses may be more robustly protected by defensive automation.
• Statistical methodologies grounded in matched agent deployments and hierarchical taxonomy mapping (e.g., to MITRE ATT&CK) yield actionable insights for prioritizing defense and attack development.

Recommended future research includes:

• Increasing per-taxonomy sample sizes and paired analyses for higher statistical power.
• Adapting the experimental approach for heterogeneous operating systems (e.g., Windows) and agentic architectures.
• Integrating richer, time-to-event and cause-specific metrics to capture the nuanced dynamics of multi-stage attacks and defense.
• Benchmarking new LLM frameworks, multi-modal agentic systems, and adaptive, operationally-constrained defense strategies.

6. Significance for the Evolution of Automated Cybersecurity

The controlled, empirical evidence from CTF battleground studies directly challenges the longstanding assumption of an inherent attacker advantage in AI-enabled cybersecurity. Instead, it reveals an equilibrium contingent on defensive operational realities and success metrics.

The need for rapid adoption and continuous evolution of open-source, modular Cybersecurity AI frameworks is heightened by the pace of offensive automation. Defensive research and operational practice must integrate rigorous, empirically validated approaches to remain at parity with increasingly capable and adaptable offensive agentic systems.

In conclusion, attack/defense CTF battlegrounds serve as an essential, ecologically valid testbed for measuring and advancing both the science and practice of automated cyber operations. Statistical rigor, taxonomy-informed evaluation, and attention to operational constraints are imperative for advancing from theoretical claims to robust, deployable cyber defense capabilities in the AI era (Balassone et al., 20 Oct 2025).