TL;DR:
- Red teaming is crucial for managing generative AI risks.
- Challenges include defining red teaming, selecting the right team (internal or external), and setting objectives.
- Degradation objectives should align with past incidents, covering illicit activities, bias mitigation, toxicity, and privacy concerns.
- Manual attack strategies involve code injection, content exhaustion, hypothetical scenarios, pros and cons, and role-playing.
- Effective documentation and legal considerations are essential for successful red teaming.
- Addressing vulnerabilities requires clear plans for responsibility and patching.
- Businesses can minimize liabilities by understanding these challenges and implementing effective red teaming.
Main AI News:
Governments worldwide have started converging on a solution for managing generative AI risks, and it’s called “red teaming.” In October, the Biden administration introduced an executive order on AI, emphasizing the need for high-risk generative AI models to undergo structured red teaming. But what does red teaming generative AI entail, and how can businesses navigate this emerging landscape?
Red teaming is a positive development in managing generative AI risks, but it comes with its own set of challenges. Defining a red team, standardizing testing procedures, and ensuring the dissemination of findings post-testing are all critical hurdles. Each generative AI model has unique vulnerabilities and attack surfaces, making consistent and transparent red teaming vital for both model developers and companies utilizing them.
In this article, we will address these challenges based on our experience at Luminos.Law, a firm comprising lawyers and data scientists dedicated to managing AI risks. We’ve red teamed numerous generative AI systems, gaining insights into what works and what doesn’t. Here’s a comprehensive guide for businesses looking to red team generative AI effectively.
Defining Red Teaming for Generative AI
Despite growing enthusiasm, there is no universally agreed-upon definition of red teaming for generative AI. Major tech companies have started embracing it, but its practical application remains unclear. The term “red teaming” originated during the Cold War and was adapted into cybersecurity for traditional software systems.
However, red teaming generative AI is distinct. Unlike other AI systems that make decisions, generative AI creates content, often in the form of text, images, or audio. The harms it can produce, from offensive content to blatant falsehoods, are more akin to human actions than traditional software issues. Red teaming generative AI involves crafting malicious prompts or inputs to assess the system’s ability to produce harmful content, requiring unique approaches.
Selecting Your Red Team
The composition of a red team is another complex decision. Should it be internal or external? Companies like Google advocate for internal red teams, comprising employees with diverse expertise in simulating attacks. Others, like OpenAI, opt for external red teaming, even creating networks to engage external experts. Determining the right approach remains a challenge.
To address this, we suggest a flexible strategy based on risk assessment. Given the scale of AI adoption, fully red teaming each model is impractical. Instead, assess risk levels for different models, considering factors like harm likelihood, severity, and rectifiability. Lower-risk models require less thorough testing, while high-risk ones benefit from external red teams’ expertise, reducing liability.
Defining Degradation Objectives
Selecting the right degradation objectives is crucial for effective red teaming. These objectives guide testing by identifying the most significant liabilities each system poses. Clear objectives, aligned with past incidents from similar generative AI systems, ensure focused testing and actionable takeaways.
Common degradation objectives include:
- Enabling illicit activities: Generative AI systems can facilitate harmful activities, exposing companies to liability. Testing should cover activities like weapon-making instructions, fraudulent accounting, and hacking campaigns.
- Bias mitigation: AI systems can perpetuate bias, leading to discrimination issues. Addressing biases in model output and performance is vital to avoid legal troubles.
- Toxicity: Generative AI can produce offensive or inappropriate content. Ensuring it adheres to community standards is essential, especially as AI models learn from unfiltered internet data.
- Privacy concerns: Generative AI may inadvertently leak personal information, potentially violating privacy policies or enabling adversarial hacks. Protecting user data is paramount.
Preparing for Attacks
With objectives in place, it’s time to plan attacks. Red teams employ manual and automated methods, focusing on mapping objectives to potential successful attacks and attack vectors. Attack vectors may involve direct interactions with the model or more complex methods, such as indirect prompt injection.
Some manual attack strategies include:
- Code injection: Using code or code-like prompts to generate harmful outputs, a high-success method.
- Content exhaustion: Overwhelming the model with vast amounts of information.
- Hypothetical scenarios: Creating output based on hypothetical instructions to bypass content controls.
- Pros and cons: Soliciting harmful responses by discussing controversial topics.
- Role-playing: Directing the model to assume negative roles and provoke harmful content.
Effective testing requires mapping each strategy to degradation objectives, attack vectors, and thorough note-taking to analyze successful attacks later.
Documentation and Legal Considerations
Managing extensive documentation is crucial in successful red teaming, especially when testing hundreds or even thousands of strategies. Custom templates and clear documentation processes streamline this aspect.
Additionally, considering legal privilege is essential, as sensitive information discussed during testing could be discoverable in regulatory investigations or lawsuits. Engaging legal counsel to determine information sharing guidelines is prudent.
Addressing Vulnerabilities
Lastly, organizations should have clear plans for addressing vulnerabilities detected during red teaming. Establishing responsibility within product or data science teams, the timing of vulnerability patching, and communication processes must be defined before red teaming commences.
Conclusion:
Effective red teaming of generative AI models is essential for businesses to mitigate risks and enhance the trustworthiness of their AI systems. By prioritizing objectives, employing thorough documentation, and addressing vulnerabilities, organizations can navigate the evolving AI landscape with confidence, ensuring compliance with regulations and minimizing potential legal liabilities.
