28.3.1 CTF (Capture The Flag) organization

Phase 1: Strategic Planning & Scoping

The success of a CTF is determined long before the first flag is captured. The planning phase establishes the foundation for the entire event. Your primary goal is to define the competition’s identity, scope, and rules.

Defining Objectives and Target Audience

First, clarify the purpose of your CTF. Is it to train internal red teams, engage the public security community, or recruit talent? The answer shapes every subsequent decision. Identify your target audience: are they seasoned ML security researchers, traditional penetration testers new to AI, or students? The difficulty and nature of the challenges must align with their skill level.

• Educational: Focus on foundational concepts like prompt injection or data poisoning.
• Research-Oriented: Introduce novel or unsolved problems in AI security.
• Recruitment: Design challenges that test skills relevant to a specific job role.

Establishing Rules of Engagement (RoE)

Clear and concise rules are non-negotiable. They prevent ambiguity, ensure fair play, and protect your infrastructure. For AI CTFs, the RoE must address model-specific issues:

• Scope: Clearly define what is in-scope (e.g., interacting with a specific API endpoint) and out-of-scope (e.g., performing DoS attacks on the model, attacking the hosting platform).
• Resource Limits: Specify any rate limits on API calls to prevent abuse and manage costs.
• Flag Format: Standardize the flag format (e.g., flag{...}) and explain how flags are obtained (e.g., tricking the model into revealing it, causing a specific error state).
• Collaboration: State whether participants can work in teams and outline the rules for collaboration.
• Tooling: Mention any restrictions on automated tools or scanners.

Phase 2: Challenge Development & Infrastructure

This is the technical core of CTF organization. You will build the challenges and the platform that participants interact with. For AI CTFs, this means creating vulnerable models and deploying them securely.

Challenge Design

AI challenges differ significantly from traditional cybersecurity tasks. They often involve manipulating model logic rather than exploiting memory corruption or web vulnerabilities. Common AI CTF challenge categories include:

• Prompt Injection / Jailbreaking: Bypassing safety filters or instructions in an LLM.
• Model Evasion: Crafting inputs that are misclassified by a computer vision or malware detection model.
• Data Extraction: Recovering sensitive training data from a model.
• Model Inversion: Reconstructing input features from a model’s output.
• Backdoor Activation: Finding and using a hidden trigger in a trojaned model.

# Pseudocode for a simple, vulnerable LLM API endpoint
from flask import Flask, request, jsonify
import some_llm_library

app = Flask(__name__)
# The system prompt that the user is not supposed to override.
SYSTEM_PROMPT = "You are a helpful assistant. The secret flag is flag{s3cr3t_p4ssw0rd}."

@app.route('/ask', methods=['POST'])
def ask_model():
    user_input = request.json.get('prompt')

    # Vulnerability: Direct concatenation without proper sanitization.
    # A user can inject instructions to ignore the original system prompt.
    full_prompt = f"{SYSTEM_PROMPT}nUser: {user_input}nAssistant:"

    # Call the model with the combined prompt.
    response = some_llm_library.generate(full_prompt)
    
    return jsonify({'response': response})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=1337)

Infrastructure and Platform

You need a reliable platform to host challenges, manage users, and display a scoreboard. You can choose a popular open-source framework or build a custom solution.

• Platform: CTFd is a widely used, self-hostable framework. It handles user registration, challenge hosting, flag submission, and scoring.
• Hosting: Challenges, especially those involving large models, require significant compute resources. Cloud platforms (AWS, GCP, Azure) offer scalability, but costs must be carefully managed. Containerization (e.g., Docker) is essential for isolating challenges and ensuring consistent environments.
• Monitoring: Implement logging and monitoring for all challenge infrastructure. This helps you detect abuse, troubleshoot issues, and understand which challenges are receiving the most traffic.

Phase 3: Live Event Execution

During the competition, your team’s role shifts from development to operations. The goal is to ensure a smooth and fair experience for all participants.

Communication and Support

Establish a dedicated communication channel, such as a Discord or Slack server. This is essential for making announcements, providing hints, and answering participant questions. Have staff online and available throughout the event to address technical issues promptly. A challenge being down or misconfigured can ruin the experience.

Monitoring and Incident Response

Keep a close eye on your infrastructure. Monitor CPU/GPU usage, network traffic, and application logs. Be prepared to respond to incidents, which could range from a participant accidentally (or intentionally) crashing a service to a full-blown platform outage. Having a rollback plan for challenges is a good practice.

Phase 4: Post-Event Analysis & Community Engagement

The event isn’t over when the timer hits zero. The post-mortem phase is critical for learning from the experience and providing value back to the community.

Gathering Feedback and Announcing Winners

Once the competition ends, validate the scoreboard and officially announce the winners. Distribute a feedback survey to participants to gather insights on challenge quality, platform stability, and overall experience. This feedback is invaluable for improving future events.

Publishing Write-ups and Solutions

Encourage participants to publish write-ups of their solutions. This knowledge sharing is a core benefit of CTFs. Your team should also release official solutions and the source code for the challenges. This transparency helps participants learn and provides a valuable resource for others looking to understand AI vulnerabilities.