Offensive AI Security Engineer – Red Team

As an Offensive AI Security Engineer at Lucid, your main responsibilities will include designing and executing adversarial attacks on AI-driven security systems, conducting penetration testing using LLMs, and identifying vulnerabilities in automotive AI models. You'll also develop automation tools and explore the latest in AI attack techniques, collaborating with other teams to enhance our overall security posture.

To excel as an Offensive AI Security Engineer at Lucid, candidates should have hands-on experience in AI/ML exploitation and offensive security, with strong programming skills in Python, C, and C++. A bachelor’s degree in Cybersecurity, AI/ML, or related fields is required, while a master's degree is preferred. Familiarity with AI frameworks and reverse engineering tools will further bolster your application.

In this role, you will work on innovative projects that revolve around AI security. This includes developing adversarial attack strategies, automating vulnerability discovery, improving fuzzing techniques, and performing security assessments on AI models. The work is varied and dynamic, providing you an opportunity to contribute to cutting-edge automotive technology.

The culture at Lucid fosters collaboration and innovation. As part of the Offensive AI Security team, you will collaborate with a diverse group of highly skilled professionals who are dedicated to pushing the boundaries of technology. Lucid values creativity and encourages team members to share ideas and explore new approaches to security challenges.

Lucid Motors offers extensive growth opportunities for an Offensive AI Security Engineer. As you contribute to the development of AI security solutions, you will also have access to further training and educational resources. You'll be at the forefront of AI security trends, which positions you well for advancement within the rapidly evolving automotive and cybersecurity fields.

Adversarial machine learning is a branch of machine learning that focuses on understanding the vulnerabilities inherent in ML models and how to exploit them. In the role of Offensive AI Security Engineer at Lucid, understanding this concept is crucial as you'll be tasked with designing attacks to identify weaknesses in AI security mechanisms in our vehicles.

LLM-based penetration testing involves utilizing large language models to evaluate security measures of AI systems. In your response, discuss how you have leveraged LLMs in past projects to automate testing processes, improve the accuracy of threat identification, and reduce overall testing time.

Begin by analyzing the architecture of the AI system, identifying potential vulnerabilities, and studying existing adversarial techniques. From there, outline a strategy to implement your attack, ensuring that you understand the ethical implications and adheres to legal standards throughout the testing process.

Key programming languages for this role include Python, C, and C++. Python is vital for scripting and automating tasks, C provides an understanding of low-level operations in embedded systems, and C++ is important for performance-intensive applications, especially in AI modeling.

Use this opportunity to highlight a specific instance where your investigation led to the discovery of a security flaw. Describe the steps you took to uncover it, the techniques utilized, and the resolution process, emphasizing the impact your contribution had on overall system security.

Staying up-to-date with AI security trends often requires a multi-faceted approach, including subscribing to industry journals, attending conferences, participating in webinars, and collaborating with peers in the field. Highlight specific resources or communities you are part of to demonstrate your commitment to continual learning.

Discuss the tools you are proficient in such as Ghidra, IDA Pro, or Binary Ninja. Explain how these tools help in analyzing binaries for vulnerabilities and how you'd utilize them to enhance security assessments in the context of the Offensive AI Security Engineer role at Lucid.

AI enhances automation by using machine learning algorithms to analyze and identify patterns in existing vulnerabilities, allowing faster and more accurate discovery of new ones. You can elaborate on how integration of AI tools can streamline the process and allow for more efficient exploitation and testing.

Emphasize the importance of open communication and knowledge sharing. Describe how team collaboration is vital in crafting effective security strategies and approaches, as different perspectives often lead to more robust security solutions.

Talk about your hands-on experience with AI fuzzing tools like AFL++ or Honggfuzz, detailing how you have utilized these tools to identify potential security vulnerabilities within AI models or systems and improve the overall security framework.