Practical Threat Models in AI Security: Bridging Research and Practice

Core Concepts

Bridging the gap between academic threat models and practical AI security by studying real-world threat models.

Abstract

The content discusses the disparity between academic threat models in AI security and practical usage. It focuses on the need to study more practical threat models by analyzing the six most studied attacks in AI security research. The article highlights mismatches between research and practice, emphasizing the importance of understanding how AI is used in practice. It provides insights into the common dataset sizes, challenges in AI security research, and factors influencing access to AI systems.
Structure:

Introduction

Identifies the gap between academic research and practical AI security.

Background

Defines AI and different paradigms like ML, RL, and DM.

Methodology

Describes the questionnaire design, pretests, and recruiting process.

Results

Analyzes training-time attacks (poisoning, backdoors), test-time attacks (evasion, model stealing), and privacy attacks (membership inference, attribute inference).

AI Security Beyond Specific Attacks

Discusses common dataset sizes, challenges in AI security research, and factors influencing access to AI systems.

Stats

Recent works have identified a gap between research and practice in artificial intelligence security.
A survey with 271 industrial practitioners was conducted to match threat models to AI usage in practice.
71.6% of participants reported that training data was not accessible.
48.1% of participants stated to use third-party models and then fine-tune them.
44.5% of participants reported allowing public access to model outputs.
35.3% of participants reported allowing queries to the model.
4.8% of participants granted 3rd party access to their weights.

Quotes

"Our paper is thus a call for action to study more practical threat models in artificial intelligence security."
"The underlying problem, an absence of knowledge on how artificial intelligence (AI) is used in practice, is however still unaddressed."
"Most academic attacks require access to more data, limiting their usefulness."
"Our work is a call for transferability studies, when neither model nor data are known."

Key Insights Distilled From

Towards more Practical Threat Models in Artificial Intelligence Security

by Kathrin Gros... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2311.09994.pdf

Deeper Inquiries

How can the disparity between academic threat models and practical AI security be effectively addressed?

The disparity between academic threat models and practical AI security can be effectively addressed through a few key strategies:

Collaboration between academia and industry: Encouraging collaboration between academia and industry can help bridge the gap between theoretical threat models and real-world security challenges. By working together, researchers can gain insights into practical security risks and develop more relevant threat models.

Empirical validation: Researchers should conduct empirical studies to validate threat models in real-world settings. This can involve working closely with industry practitioners to understand their security concerns and challenges, and then testing academic threat models against these practical scenarios.

Incorporating real-world data: Academic researchers should consider incorporating real-world data and scenarios into their threat modeling exercises. By using actual datasets and case studies from industry, researchers can create more realistic and applicable threat models.

Continuous feedback loop: Establishing a continuous feedback loop between academia and industry can help ensure that threat models are updated and refined based on practical experiences and emerging security threats. This iterative process can lead to more effective and relevant AI security measures.

How can the implications of the findings impact the development of AI security systems in practice?

The implications of the findings on the development of AI security systems in practice are significant and can influence the following aspects:

Improved threat detection and mitigation: By understanding the practical threat models and security risks identified in the study, organizations can enhance their threat detection capabilities and implement more effective mitigation strategies to protect their AI systems.

Enhanced data security measures: Insights from the study can help organizations strengthen their data security measures, especially in terms of access control and data protection. This can lead to better safeguarding of sensitive information used in AI models.

Tailored security solutions: The findings can guide the development of tailored security solutions that address specific vulnerabilities identified in practical AI usage. This customization can lead to more targeted and robust security measures.

Compliance with regulations: Understanding the practical threat models can assist organizations in ensuring compliance with data protection regulations and security standards. By aligning security practices with regulatory requirements, organizations can mitigate legal risks associated with AI security.

How can the study of practical threat models in AI security impact the future of AI research and development?

The study of practical threat models in AI security can have a profound impact on the future of AI research and development in the following ways:

Enhanced security-focused AI research: The insights gained from studying practical threat models can drive a shift towards more security-focused AI research. This can lead to the development of AI systems that are inherently more secure and resilient to cyber threats.

Innovation in security technologies: Understanding practical security risks can spur innovation in security technologies for AI systems. Researchers may explore new approaches and solutions to address emerging threats and vulnerabilities in AI applications.

Integration of security by design: By considering practical threat models early in the development process, AI researchers can integrate security by design principles into their projects. This proactive approach can help prevent security breaches and minimize risks in AI systems.

Cross-disciplinary collaboration: The study of practical threat models can foster cross-disciplinary collaboration between AI researchers, cybersecurity experts, and industry practitioners. This collaboration can lead to the development of comprehensive security frameworks that encompass both technical and operational aspects of AI security.