Jeopardy CTFs: Cybersecurity Education & Benchmarking

• Jeopardy CTFs are modular cybersecurity competitions featuring independently solvable challenges in domains like cryptography, web exploitation, and reverse engineering.
• They employ diverse scoring models—including static, time-decay, and rank-based systems—to fairly evaluate performance and benchmark AI agents.
• Their design integrates incremental hints and structured challenge chains, enhancing technical learning and practical applications in education and IDS evaluation.

Jeopardy-style Capture the Flag (CTF) competitions are a foundational format within cybersecurity training, education, and benchmarking. These events center on a suite of independent technical challenges, each solvable for points upon retrieval of a “flag”—a proof artifact emblematic of compromise, exploitation, or analysis. Rapidly adopted in competitive and instructional settings, Jeopardy CTFs have shaped talent pipelines, learning environments, and, more recently, the evaluation of AI cybersecurity agents. With the ascendancy of automation, these contests have also become focal points in the discourse on the measurement validity of cybersecurity skill and the evolution of benchmarking methodologies.

1. Formal Structure and Core Characteristics

Jeopardy CTFs present participants with a menu of standalone challenges spanning domains such as cryptography, web exploitation, reverse engineering, binary exploitation, forensics, and miscellaneous problems (Lyu et al., 24 Jan 2026, Sanz-Gómez et al., 28 Oct 2025). Each challenge is self-contained and solvable independently, in contrast to live Attack-Defense CTFs that simulate interactive attacks and live patching (Lyu et al., 24 Jan 2026).

Key structural parameters:

• Scoring: Each challenge is assigned a point value, typically reflecting difficulty (e.g., 10–500 points). Participants or teams accrue points to build a leaderboard score (Lyu et al., 24 Jan 2026, Vykopal et al., 2020). Multiple scoring algorithms are documented: static, time-decaying, and rank-based models.
• Categories: Classification of challenges into canonical security domains ensures breadth—examples from picoCTF and 24/7 CTF allocate 13–20% per category, see Table 1.

Category	picoCTF (%)	24/7 CTF (%)
Cryptography	18.5	14.0
Web Exploitation	17.0	21.0
Reverse Engineering	20.0	14.0
Binary Exploitation	13.5	20.0
Forensics	18.5	–
Networking	–	12.5
Miscellaneous	22.5	18.5

• Hints and Progression: Incremental or “tiered” hints reduce participant stagnation, with progressive hints often costed or gated (Vykopal et al., 2020).
• Delivery: Challenges are hosted via web shells, VMs, or containerized for reproducibility (Docker manifests in CAIBench (Sanz-Gómez et al., 28 Oct 2025)).

2. Design Principles and Pedagogical Integration

Jeopardy CTFs are engineered for modularity and broad coverage, promoting technical skill acquisition in cryptanalysis, exploit development, reverse engineering, web security, and forensics (Lyu et al., 24 Jan 2026). Key design recommendations include:

• Difficulty Balance: Mix of easy, moderate, and advanced challenges (e.g., 10–50, 100–200, 300+ points) to support novices and experts (Lyu et al., 24 Jan 2026).
• Hint Architecture: Progressive hints decrease frustration but are often costed (by points or decay) to preserve competitiveness; hints are annotated for content clarity (Vykopal et al., 2020).
• Scaffolding: Linear or tree-structured challenge chains and “zero-cost” orientation tasks support learning arcs (Vykopal et al., 2020).
• Accessibility: Minimal setup (web shells, pre-built VM or containers) reduces onboarding friction, with platforms such as picoCTF, OpenCTF, and CTFd documented for relative accessibility (Lyu et al., 24 Jan 2026).
• Learning Analytics: Platforms log granular data (view, download, hint request, submission), enabling real-time analytics, stuck-challenge detection, and curricular modification (Vykopal et al., 2020).

Jeopardy CTFs afford a uniquely self-paced environment, permitting unrestricted experimentation with security tooling and concepts without operational risk or legal exposure (Lyu et al., 24 Jan 2026).

3. Scoring Models and Anti-Plagiarism Mechanisms

Scoring models in Jeopardy CTFs are tailored to promote engagement and equitable skill recognition:

• Static Point Values: Transparent but do not incentivize early solves (Vykopal et al., 2020).
• Time-Decay Scoring: Points decline as a function of elapsed time, typically via linear or exponential decay. For example,

$S_j(t) = \max(P_{0,j} - \alpha_j t,\, S_{\min,j})$

or

$S_j(t) = P_{0,j} \exp(-\lambda_j t)$

Parameterization (e.g., holding full value for 2–4 hours) balances urgency and fairness.

• Rank-Based Multipliers: First solvers receive bonus credit or geometric decays by rank $n$:

$S_j(n) = P_j \gamma^{\,n-1},\quad 0<\gamma<1$

• Hybrid Models: Combine base value, time-decay, and speed bonuses (Vykopal et al., 2020).

Plagiarism and collusion resistance are achieved via:

• Auto-generated per-player challenge variants (random salts, metamorphic binaries) (Vykopal et al., 2020).
• Submission timing heuristics to flag improbable solve orderings or synchronized submissions.
• Detailed event logging enables post-hoc analysis of flag sharing or solution copying (Vykopal et al., 2020).

4. Jeopardy CTFs in AI and Agent Benchmarking

Jeopardy CTFs serve both as agentic AI ordinal benchmarks and as challenge corpora within integrated multi-domain cybersecurity evaluation frameworks.

• CAIBench employs 117 Dockerized Jeopardy challenges spanning multiple domains and difficulty tiers; success metrics include pass₁₀₀@1 rates (percentage of problems solved in up to 100 tool-agent attempts) (Sanz-Gómez et al., 28 Oct 2025).
• Leading LLM-based agents (alias1, claude-4.5, gpt-5, qwen3-32B) achieve 45–75% success on “Base” challenges, with significant drop-off on harder or robotics-themed RCTF2 categories (down to 22%) (Sanz-Gómez et al., 28 Oct 2025).
• Even state-of-the-art models reveal persistent ceilings, with success on core tasks plateauing (~75% for easiest tier), and failures concentrated on multi-stage reasoning, novel protocol (ROS, OPC) handling, and coordination across toolchains.
• This suggests that Jeopardy CTFs, while informative for low-to-moderate complexity skills, do not discriminate effectively in the upper echelons of LLM or agent capabilities, particularly given the lack of difficulty weighting (Sanz-Gómez et al., 28 Oct 2025).

5. Security, Research, and Educational Applications

Jeopardy CTFs have diversified roles:

• Cybersecurity Education: University courses now incorporate Jeopardy CTFs as graded or ungraded assignments, leveraging their modularity and scalable analytics (Vykopal et al., 2020, Lyu et al., 24 Jan 2026). Analytics enable feedback at the per-student and per-challenge level for formative assessment.
• IDS Evaluation: Kern et al. (Kern et al., 20 Jan 2025) embed IDS-specific Jeopardy challenges in live events, leveraging a controlled deployment architecture to surface false negatives, with scoring tightly coupled to stealth (minimization of triggered IDS alerts). Stealth-weighted scoring employs a logarithmic decay relative to alert volume:

$$P(A)=\max\left\{P_{\min},\,P_{\max} - (s \ln A)(P_{\max}-P_{\min})\right\}$$

• Agent Evaluation: Jeopardy CTFs provide reproducible, independent benchmarks for measuring discrete skill execution among AI agents (Mayoral-Vilches et al., 2 Dec 2025, Sanz-Gómez et al., 28 Oct 2025).

However, the separation from live system context limits measurement of workflow integration, persistence, and adaptive defense strategy (Lyu et al., 24 Jan 2026, Mayoral-Vilches et al., 2 Dec 2025).

6. Limitations and Evolution in the Era of AI Domination

Recent empirical evidence demonstrates that advanced AI agents (CAI, alias1) now systematically outpace human participants in major Jeopardy CTF circuits, with >90% solve rates and substantially superior velocity and cost-efficiency (Mayoral-Vilches et al., 2 Dec 2025). In competitive benchmarks:

• CAI consistently achieved #1 ranking or top percentile performance across major global events (Dragos OT, Neurogrid, Cyber Apocalypse).
• Infrastructure scaling and token-budget, not conceptual security insight, now primarily determine winners (Mayoral-Vilches et al., 2 Dec 2025).

This has critical implications:

• Talent Identification: Jeopardy CTFs no longer differentiate top human talent; selection pressure localizes on automation capability.
• Research Benchmarking: The "solved" nature of classic Jeopardy CTFs necessitates more adversarial, interactive formats (specifically, Attack & Defense) to restore measurement of skills such as adaptive reasoning, deception, patch development, and resilience under pressure (Mayoral-Vilches et al., 2 Dec 2025).

Initial pilot events in dynamic Attack & Defense settings demonstrate AI win rates fall to 50% or below, supporting the assertion that adaptive defense/attack operations remain resistant to full automation (Mayoral-Vilches et al., 2 Dec 2025).

7. Design Best Practices and Future Directions

Best-practice recommendations, distilled from educational and research deployments (Vykopal et al., 2020, Lyu et al., 24 Jan 2026), include:

• Structured challenge chains and incremental scaffolding to enable guided learning and reduce plagiarism.
• Balanced, hybrid scoring to sustain engagement across early, late, and diverse-skill participants.
• Comprehensive event logging and analytics to inform challenge refinement and instructional improvement.
• Explicit policy communication delineating acceptable collaboration and cheating boundaries.
• Integration into blended curricula, supplementing Jeopardy challenges with lab-based or live-action formats (Attack & Defense, wargames) to bridge domain breadth with operational context and realism (Lyu et al., 24 Jan 2026).

A plausible implication is that in both research and education, the continuing value of Jeopardy CTFs lies in their modularity and accessibility, while emerging measurement objectives—aptitude at strategic adaptation, resilience, and integrated defense—require more interactive and adversarial competition modalities.