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AI red teaming, also known as adversarial testing, has become an industry-standard security measure in recent years as safety and security threats to AI systems have grown. Red teaming involves simulating potential threats to an AI model in controlled environments to prepare the system for real-world challenges. This proactive approach helps organizations identify and mitigate vulnerabilities, ensuring their AI applications remain secure and reliable.
Red teaming is not a one-size-fits-all solution. Different regulations, providers, and frameworks cater to the unique security needs of specialized AI applications. Choosing and understanding the right options for your AI model will help you ensure it remains secure. Below, we explore the current regulations, model provider recommendations, and key frameworks that should guide your AI red teaming practices.
Current Regulations
Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence
In October 2023, the Biden administration issued an Executive Order aimed at ensuring the safe, secure, and trustworthy development and use of AI. This order mandates AI red teaming as a critical component:
- Section 4.1(a)(ii): Establishes guidelines for developers of AI, particularly dual-use foundation models, to conduct AI red-teaming tests to ensure the deployment of safe, secure, and trustworthy systems.
- Section 4.2(a)(i)(C): Requires developers to provide ongoing information, reports, or records of dual-use foundation models' performance in relevant AI red-team testing to the Federal Government.
- Section 10.1(b)(viii)(A): Mandates external testing for AI, including AI red-teaming for generative AI, to safeguard against discriminatory, misleading, inflammatory, unsafe, or deceptive outputs.
EU AI Act
The EU AI Act establishes comprehensive regulations for AI systems within the European Union, emphasizing:
- Risk Management: Mandatory risk assessments and mitigation strategies to identify and manage potential threats.
- Robustness and Accuracy: Ensuring AI systems are resilient and accurate, minimizing errors and vulnerabilities.
- Transparency: Clear documentation and reporting of AI system capabilities and limitations to maintain accountability and trust.
ISO/IEC 23894
ISO/IEC 23894 focuses on the management of risk in AI systems, providing international standards for ensuring the safety, security, and reliability of AI applications. This standard emphasizes the importance of continuous testing and evaluation throughout the AI system lifecycle, aligning with red teaming methodologies
Model provider recommendations for Adversarial testing
OpenAI’s ChatGPT
In their safety best practices, OpenAI recommends that you “red-team” your application to ensure protection against adversarial input, testing the product over a wide range of inputs and user behaviors, both a representative set and those reflective of someone trying to break the model.
Google Gemini
Google Gemini recommends that despite the time, effort, and expertise it may take to evaluate your application, the more you red-team it, the greater your chance of spotting problems, especially those occurring rarely or only after repeated runs of the application.
They suggest selecting test data that is most likely to elicit problematic output from the model, probing the model's behavior for all types of possible harm, even unusual examples and edge-cases. You can refer to Google's Responsible AI practices for more details on what to consider when building an adversarial testing dataset.
Amazon Bedrock
Amazon Bedrock advocates that you regularly test your applications for prompt injection and other security vulnerabilities using techniques like penetration testing, static code analysis, and dynamic application security testing (DAST). They suggest that you regularly monitor and perform these tests on your model as you update it to ensure consistent safety, security and performance.
AI security frameworks
AI security frameworks play a crucial role in the process of red teaming. These frameworks provide methodologies designed to identify and mitigate vulnerabilities in AI systems. These frameworks are crucial for ensuring the security, reliability, and robustness of AI applications. By simulating potential attacks and examining how AI systems respond to malicious inputs, red teaming helps organizations proactively address weaknesses before they can be exploited in real-world scenarios. Implementing these frameworks provides a structured approach to safeguarding AI systems against threats like data poisoning, model evasion, and model inversion, ultimately enhancing the overall trustworthiness and effectiveness of AI deployments.
NIST AI Risk Management Framework (RMF)
The NIST AI Risk Management Framework (RMF) emphasizes continuous testing and evaluation throughout the AI system's lifecycle. This framework advocates for practices such as differential testing, adversarial testing, and stress testing, which align closely with red teaming methodologies. NIST encourages organizations to proactively identify and mitigate risks related to security, bias, and robustness, ensuring AI systems are resilient against potential adversarial attacks.
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems)
MITRE ATLAS is a comprehensive framework specifically designed for AI security, providing a knowledge base of adversarial AI tactics and techniques. Similar to the MITRE ATT&CK framework for cybersecurity, ATLAS helps organizations understand potential attack vectors against AI systems, including data poisoning, model evasion, and model inversion. This framework guides red teaming efforts by offering a structured approach to testing AI vulnerabilities and includes real-world case studies and examples, making it a valuable resource for AI teams and security professionals.
OWASP LLM Top 10
The OWASP Top 10 framework provides a list of the most critical security risks to large language models, highlighting common vulnerabilities that can be exploited by attackers, such as prompt injection, data poisoning, and model extraction. By following the OWASP top 10 for LLMS 10 guidelines, organizations can systematically identify and address these vulnerabilities, ensuring their AI systems are robust and secure.
By incorporating this framework into AI red teaming practices, organizations can ensure a more comprehensive and structured approach to identifying and mitigating risks in AI systems. Leveraging these established frameworks helps create robust defenses against adversarial threats, which will ultimately lead to more secure, reliable AI deployments.
Ensure Your AI Applications Perform as Intended
Pillar Security offers a robust solution to fortify AI applications in today's complex landscape. Our comprehensive, multi-stage Red Teaming solution combines deep AI knowledge with cybersecurity expertise to identify vulnerabilities throughout the AI lifecycle.
Pillar's engine simulates tailored attack scenarios, uncovering hidden weaknesses and enhancing defenses. This approach hardens AI systems against adversarial examples and LLM-focused attacks, while evaluating model robustness through pre-deployment testing. Pillar implements techniques to improve model resilience to malicious inputs, building confidence in your AI's security against evolving threats.
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