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The CTF target will be an AI robot inside a simulated environment. Instead of requiring continuous manual player interaction, the competition may allow participants to submit their own AI models or agents, which will then be used to interact with, exploit, or solve challenges against the robotic target environment.
The challenge scope may include both AI model security and robotics system security. In addition to allowing participants to compete using AI models, the target systems and challenges themselves may involve AI models such as VLA, VLM, or LLM components. Potential challenge topics may include adversarial patches, prompt injection, prompt jailbreaks, model poisoning, and other attacks against AI-enabled robotic decision-making systems.
The CTF may also include challenges related to the robot’s underlying software stack, communication layers, and robotics protocols, such as ROS 2, DDS, and related robotic middleware or system interfaces.
The goal is to explore the intersection of robotics security, AI model behavior, autonomous exploitation, and CTF-style challenge design. Participants may be asked to think beyond traditional web, pwn, reversing, or cloud challenges and consider how autonomous systems perceive, decide, communicate, and act inside a robotic environment.
This event is expected to be experimental and community-driven. Details such as rules, scoring, participation format, model submission requirements, challenge categories, target environments, and challenge availability may change as the event develops.
Please refer to the official Robotic Hacking Community website for the latest and most accurate event information.
Tentative Challenge Areas
AI robotic security
Autonomous agent / AI model participation
VLA, VLM, and LLM security
Adversarial patch attacks
Prompt injection
Prompt jailbreak
Model poisoning
AI-enabled robotic decision-making
ROS 2 / DDS security
Robotic middleware and communication protocols
Simulated robotic target environments
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