Comprehensive Analysis of Semantic AI Security Challenges and Attacks in Autonomous Driving Systems

Kernekoncepter

Autonomous driving systems rely heavily on AI components to make safety-critical decisions, but these AI algorithms are known to be vulnerable to adversarial attacks. Addressing the semantic gaps between the system-level attack inputs and AI component-level vulnerabilities, as well as the gaps between AI component-level attack impacts and system-level effects, is crucial for ensuring the security of autonomous driving.

Resumé

The paper provides a comprehensive systematization of knowledge (SoK) on the emerging research space of semantic AI security in autonomous driving (AD) systems. It collects and analyzes 53 papers published in the past 5 years, focusing on works that address the semantic AI security challenges, as opposed to generic AI security.
The paper taxonomizes the existing works based on several critical research aspects, including the targeted AI components, attack goals, attack vectors, attacker's knowledge, defense deployability, defense robustness, and evaluation methodologies. It summarizes the current status and trends for each aspect, and identifies 6 substantial scientific gaps through quantitative comparisons with security works in closely-related domains.
The key gaps include the general lack of system-level evaluations, the predominant focus on integrity attacks over other security properties, the limited exploration of cyber-layer attack vectors, the lack of defense solutions targeting attack prevention, the insufficient consideration of deployability factors, and the limited adoption of adaptive attack evaluations. The paper provides insights and potential future directions to address these gaps, not only at the design level, but also at the research goal, methodology, and community levels.
To address the most critical methodology-level gap on system-level evaluations, the paper takes the initiative to develop an open-source, uniform, and extensible system-driven evaluation platform, named PASS, for the semantic AD AI security research community. It showcases the capabilities and benefits of such a platform using representative AD AI attacks.

Statistik

"For example, as concretely estimated by Jia et al. [17], for camera object detection-only AI attacks, a component-level success rate of up to 98% can still be not enough to affect object tracking results."
"Various such counterexamples have already been discovered in AD system context, e.g., when the object detection model error is at a far distance for automatic emergency braking systems [14, 16], or such errors can be effectively tolerated by downstream AI modules such as object tracking [17]."

Citater

"It is widely recognized that in AD system, AI component-level errors do not necessarily lead to system-level effect (e.g., vehicle collisions). However, system-level evaluation is generally lacking in existing works."
"For CPS systems such system-level evaluation is generally more difficult due to the involvement of physical components. However, we find that for existing security research on related CPS domains such as drone and ASR/SI, actually almost all attack works perform system-level evaluations (100% for drone, 94% for ASR/SI based on the SoKs [24,52])."
"This means that even with high attack success rates shown at the AI component level, it is actually possible that such an attack cannot cause any meaningful effect to the AD vehicle driving behavior."

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SoK: On the Semantic AI Security in Autonomous Driving

by Junjie Shen,... kl. arxiv.org 04-29-2024

https://arxiv.org/pdf/2203.05314.pdf

Dybere Forespørgsler

How can the research community collectively develop and maintain a common system-level evaluation infrastructure to enable more meaningful and comparable security research in autonomous driving?

To collectively develop and maintain a common system-level evaluation infrastructure for security research in autonomous driving, the research community can take several steps:

Community Collaboration: Researchers from academia, industry, and government agencies should collaborate to establish common standards and protocols for system-level evaluation. This collaboration can help in sharing resources, expertise, and best practices.

Open-Source Platform: Developing an open-source platform that allows researchers to contribute, access, and utilize evaluation tools and datasets can facilitate collaboration and standardization. This platform should be designed to be extensible, allowing for the integration of new evaluation methods and scenarios.

Standardized Evaluation Scenarios: Defining a set of standardized evaluation scenarios that cover a wide range of potential security threats and attack vectors in autonomous driving systems. These scenarios should be realistic, diverse, and representative of real-world driving conditions.

Benchmarking and Metrics: Establishing common benchmarks and evaluation metrics to assess the effectiveness of security solutions. These benchmarks should be designed to measure the impact of attacks on system-level driving behavior and safety.

Continuous Improvement: Encouraging continuous improvement and refinement of the evaluation infrastructure based on feedback from researchers, industry practitioners, and regulatory bodies. This iterative process can help in enhancing the reliability and effectiveness of the evaluation platform.

By following these steps and fostering a culture of collaboration and innovation, the research community can collectively develop and maintain a robust system-level evaluation infrastructure for security research in autonomous driving.

What are the potential limitations and drawbacks of focusing predominantly on integrity attacks in autonomous driving, and how can the research community expand the scope to other security properties like confidentiality and availability?

Focusing predominantly on integrity attacks in autonomous driving systems has several limitations and drawbacks:

Limited Scope: By focusing only on integrity attacks, the research community may overlook other critical security properties such as confidentiality and availability. Neglecting these properties can leave vulnerabilities unaddressed, leading to potential privacy breaches and system downtime.

Incomplete Protection: Emphasizing integrity alone may provide a false sense of security, as attackers could exploit confidentiality or availability vulnerabilities to bypass existing defenses. A holistic approach that considers all security properties is essential for comprehensive protection.

Real-World Impact: Ignoring confidentiality and availability can have significant real-world consequences, such as data breaches, unauthorized access to sensitive information, and service disruptions. These impacts can pose serious risks to the safety and security of autonomous driving systems.

To expand the scope to other security properties like confidentiality and availability, the research community can:

Diversify Research Focus: Allocate resources and attention to studying and addressing confidentiality and availability threats in autonomous driving systems. This can involve conducting targeted research, developing new defense mechanisms, and evaluating system vulnerabilities from multiple perspectives.

Interdisciplinary Collaboration: Foster collaboration between security researchers, AI experts, automotive engineers, and policymakers to gain a comprehensive understanding of security challenges in autonomous driving. This interdisciplinary approach can help in identifying and mitigating risks across all security properties.

Regulatory Compliance: Align research efforts with regulatory requirements and industry standards that mandate protection of confidentiality, availability, and integrity in autonomous driving systems. Adhering to these guidelines can ensure a well-rounded security posture.

By broadening the focus to include confidentiality and availability, the research community can enhance the resilience and robustness of autonomous driving systems against a wider range of security threats.

What are the unique challenges and opportunities in exploring cyber-layer attack vectors for autonomous driving systems, and how can the research community better leverage the insights from related CPS domains?

Exploring cyber-layer attack vectors for autonomous driving systems presents both challenges and opportunities:

Challenges:

Complexity: Cyber-layer attacks in autonomous driving involve intricate interactions between software, hardware, and communication systems, making them challenging to detect and mitigate.
Adversarial Sophistication: Attackers may employ advanced techniques to exploit vulnerabilities in the cyber layer, requiring sophisticated defense mechanisms to counteract these threats.
Regulatory Compliance: Ensuring compliance with regulatory frameworks and safety standards while addressing cyber-layer vulnerabilities can be a complex and time-consuming process.

Opportunities:

Enhanced Security Posture: By identifying and addressing cyber-layer attack vectors, the research community can strengthen the overall security posture of autonomous driving systems, making them more resilient to cyber threats.
Innovation: Exploring cyber-layer attacks presents an opportunity for innovation in security technologies, threat intelligence, and incident response strategies tailored to the unique challenges of autonomous driving.
Cross-Domain Insights: Leveraging insights from related Cyber-Physical Systems (CPS) domains such as drones and ASR/SI can provide valuable lessons and best practices that can be applied to autonomous driving security research.

To better leverage insights from related CPS domains, the research community can:

Knowledge Sharing: Foster collaboration and knowledge sharing between researchers working in autonomous driving and other CPS domains to exchange insights, methodologies, and best practices.
Interdisciplinary Research: Encourage interdisciplinary research that combines expertise from cybersecurity, AI, automotive engineering, and other relevant fields to address cyber-layer vulnerabilities comprehensively.
Continuous Learning: Stay informed about the latest developments in CPS security research, emerging cyber threats, and regulatory requirements to adapt security strategies and technologies accordingly.
By addressing the unique challenges and seizing the opportunities presented by cyber-layer attack vectors, the research community can enhance the security and resilience of autonomous driving systems in the face of evolving cyber threats.