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AutoDriving CTF
In-person contest
24-hour Online Friday to Sat 10:00 - 18:00
The AutoDriving CTF contest focuses on the emerging security challenges in autonomous driving systems. Various levels of self-driving functionalities, such as AI-powered perception, sensor fusion and route planning, are entering the product portfolio of automobile companies. From the security perspective, these AI-powered components not only contain common security problems such as memory safety bugs, but also introduce new threats such as physical adversarial attacks and sensor manipulations. Two popular examples of physical adversarial attacks are camouflage stickers that interfere with vehicle detection systems, and road graffitis that disturb lane keeping systems. The AI-powered navigation and control relies on the fusion of multiple sensor inputs, and many of the sensor inputs can be manipulated by malicious attackers. These manipulations combined with logical bugs in autonomous driving systems pose severe threats to road safety.
We design autonomous driving CTF (AutoDriving CTF) contests around the security challenges specific to these self-driving functions and components.
The goals of the AutoDriving CTF are the followings:
- Demonstrate security implications of autonomous driving system design decisions through hands-on challenges, increase the awareness of potential risks in security professionals, and encourage them to propose defense solutions and tools to detect such risks.
- Provide CTF challenges that allow players to learn attack and defense practices related to autonomous driving in a well-controlled, repeatable, and visible environment.
- Build a set of vulnerable autonomous driving components that can be used for security research and defense evaluation.
The contest is based on a Jeopardy style of CTF game with a set of independent challenges. A typical contest challenge includes a backend that runs autonomous driving components in simulated or real environments, and a frontend that interacts with the players. This year's contest will follow the style of last year and includes the following types of challenges:
- “attack”: such as constructing adversarial patches and spoofing fake sensor inputs,
- “forensics”: such as investigating a security incident related to autonomous driving,
- “detection”: such as detecting spoofed sensor inputs and fake obstacles,
- “crashme on road!”: such as creating dangerous traffic scenarios to expose logical errors in autonomous driving systems.
- “smart planner”: such as creating intelligent path planners for dangerous tasks that are difficult for human drivers
Most of these challenges will be developed using game-engine based autonomous driving simulators, such as CARLA and SVL. The following link contains some challenge videos, summaries from AutoDriving CTF at DEF CON 29 and DEF CON 30
https://drive.google.com/drive/folde...25?usp=sharing
https://www.youtube.com/channel/UCPP...wk-464KIzr8xKw
# What's new in 2024
This year, we will unlock new traffic conflict scenarios that are observed from real-world driving logs such as Jaywalk and double parked vehicles. New difficulty levels will be added to challenges in such scenarios by integrating real downstream AI modules such as object tracking from open-source autonomous driving software like Apollo, Autoware and OpenPilot.
In order to enable the audience to experience the challenges more directly, we plan to set up a vehicle wheel controller on site and provide a driving game this year. Audiences can drive themselves to compete with the self-driving vehicle in some of the challenges. Driving game demo:
https://drive.google.com/drive/folde...YB?usp=sharing
# For players
- What do players need to do to participate AutoDriving CTF?
Most of the challenges do not require domain knowledge of autonomous driving software or adversarial machine learning, although knowledge of those helps. For example, the players can generate images the way they like (e.g., drawing, photoshopping) to fool the AI-components or write a short python script to control the vehicle. Some challenges, such as incident forensics likely would require players to learn domain knowledge such as sensor information format and how fusion works.
- What do we expect players to learn through the CTF event?
Players can (1) gain a deep understanding of real-world autonomous driving systems' design, implementation, and their corresponding security properties and characteristics; and (2) learn the attack and defense practices related to autonomous driving in a well-controlled, repeatable, visible, and engaging environment.
Twitter: @autodrivingctf
..
AutoDriving CTF
In-person contest
24-hour Online Friday to Sat 10:00 - 18:00
The AutoDriving CTF contest focuses on the emerging security challenges in autonomous driving systems. Various levels of self-driving functionalities, such as AI-powered perception, sensor fusion and route planning, are entering the product portfolio of automobile companies. From the security perspective, these AI-powered components not only contain common security problems such as memory safety bugs, but also introduce new threats such as physical adversarial attacks and sensor manipulations. Two popular examples of physical adversarial attacks are camouflage stickers that interfere with vehicle detection systems, and road graffitis that disturb lane keeping systems. The AI-powered navigation and control relies on the fusion of multiple sensor inputs, and many of the sensor inputs can be manipulated by malicious attackers. These manipulations combined with logical bugs in autonomous driving systems pose severe threats to road safety.
We design autonomous driving CTF (AutoDriving CTF) contests around the security challenges specific to these self-driving functions and components.
The goals of the AutoDriving CTF are the followings:
- Demonstrate security implications of autonomous driving system design decisions through hands-on challenges, increase the awareness of potential risks in security professionals, and encourage them to propose defense solutions and tools to detect such risks.
- Provide CTF challenges that allow players to learn attack and defense practices related to autonomous driving in a well-controlled, repeatable, and visible environment.
- Build a set of vulnerable autonomous driving components that can be used for security research and defense evaluation.
The contest is based on a Jeopardy style of CTF game with a set of independent challenges. A typical contest challenge includes a backend that runs autonomous driving components in simulated or real environments, and a frontend that interacts with the players. This year's contest will follow the style of last year and includes the following types of challenges:
- “attack”: such as constructing adversarial patches and spoofing fake sensor inputs,
- “forensics”: such as investigating a security incident related to autonomous driving,
- “detection”: such as detecting spoofed sensor inputs and fake obstacles,
- “crashme on road!”: such as creating dangerous traffic scenarios to expose logical errors in autonomous driving systems.
- “smart planner”: such as creating intelligent path planners for dangerous tasks that are difficult for human drivers
Most of these challenges will be developed using game-engine based autonomous driving simulators, such as CARLA and SVL. The following link contains some challenge videos, summaries from AutoDriving CTF at DEF CON 29 and DEF CON 30
https://drive.google.com/drive/folde...25?usp=sharing
https://www.youtube.com/channel/UCPP...wk-464KIzr8xKw
# What's new in 2024
This year, we will unlock new traffic conflict scenarios that are observed from real-world driving logs such as Jaywalk and double parked vehicles. New difficulty levels will be added to challenges in such scenarios by integrating real downstream AI modules such as object tracking from open-source autonomous driving software like Apollo, Autoware and OpenPilot.
In order to enable the audience to experience the challenges more directly, we plan to set up a vehicle wheel controller on site and provide a driving game this year. Audiences can drive themselves to compete with the self-driving vehicle in some of the challenges. Driving game demo:
https://drive.google.com/drive/folde...YB?usp=sharing
# For players
- What do players need to do to participate AutoDriving CTF?
Most of the challenges do not require domain knowledge of autonomous driving software or adversarial machine learning, although knowledge of those helps. For example, the players can generate images the way they like (e.g., drawing, photoshopping) to fool the AI-components or write a short python script to control the vehicle. Some challenges, such as incident forensics likely would require players to learn domain knowledge such as sensor information format and how fusion works.
- What do we expect players to learn through the CTF event?
Players can (1) gain a deep understanding of real-world autonomous driving systems' design, implementation, and their corresponding security properties and characteristics; and (2) learn the attack and defense practices related to autonomous driving in a well-controlled, repeatable, visible, and engaging environment.
Twitter: @autodrivingctf