AI Red Teaming: safeguarding your AI model from hidden threats

AI red teaming is a systematic approach to identifying vulnerabilities and potential failures within AI models. This practice involves stress-testing AI systems by simulating real-world scenarios, such as adversarial attacks and other challenging conditions, to assess and improve their robustness, reliability, and security.

The concept of red teaming originally comes from military practice, where a dedicated group, known as the "red team," simulates attacks on an organization or particular unit to expose its problem areas that may be subject to intrusion. This methodology has been adapted to IT security, where it’s used for testing the safety of digital systems.

Red teaming is distinct from penetration testing (Pentesting), which typically involves targeted attacks to exploit suspected vulnerabilities. While pentesting is scoped to specific weaknesses within a system, red teaming is broader, encompassing various adversarial tactics. 

Comparable dimensions of penetration testing and red teaming. (Source: Red Teams — Pentesters, APTs, or Neither)

When applied to artificial intelligence, the same principles allow engineers to uncover and mitigate weaknesses in AI models before malicious actors can exploit them. Besides, red teaming AI systems means dealing with possible biases and ethical concerns, pushing the boundaries of what the AI can handle.

In this article, we’ll explore the significance of red teaming for AI projects, including large language models, examine red teaming tools and strategies, and demonstrate how Toloka can help make your AI models safer and more reliable.

What is red teaming for Gen AI?

Dive deeper into how red teaming can improve the security and performance of your AI models, check out our comprehensive guide on red teaming here.

Generative AI red teaming is a practice designed to assess and strengthen the resilience of generative AI systems. Unlike traditional cybersecurity red teaming, which focuses on identifying vulnerabilities, or red teaming in traditional machine learning, which involves testing models for specific weaknesses like data poisoning or model tampering,  involves evaluating how generative models handle unpredictable and potentially harmful inputs.

This approach goes beyond probing for security flaws—it involves stress-testing the AI’s decision-making processes to uncover unintended behaviors. The complexity of generative AI systems, with their ability to create original content, requires a nuanced approach, where potential misuse, ethical implications, and misinformation spread become key concerns.

Red teaming for generative AI involves challenging the model to produce outputs it is supposed to avoid and to reveal biases its developers may not have anticipated. The model can be realigned with reinforced security measures when these issues are identified.

A toxic prompt and jailbreaking attempt using the original prompt manipulation. Finally, it bypasses the safety mechanisms. Source: Efficient Detection of Toxic Prompts in Large Language Models)

Why red teaming is essential for AI models

AI models must be robust, secure, and fair, particularly those deployed in critical applications. Red teaming is essential because it allows organizations to proactively identify and address potential issues before they manifest in real-world scenarios. This approach helps prevent costly failures, ensures compliance with ethical standards, and generally enhances trust in AI systems.

In April 2024, New York City Mayor Eric Adams had to respond to criticism over the chatbot intended to help small businesses but was caught giving bizarre and illegal advice. (Source: Techdirt)

On 1 August 2024, the EU AI Act entered into force, with most of its provisions commencing in 2026. It became the first artificial intelligence law in the European Union, obliging businesses to mitigate risks associated with generative AI systems. The 400-page document mentions the concept of red teaming, requiring organizations to carry out and document adversarial testing.

In July 2024, the National AI Security Institute released a public draft of its guidance on “Managing Misuse Risk for Dual-Use Foundation Models” in the United States. The draft specifies that red teams should consist of external experts independent of the AI model developer.

The U.S. AI Safety Institute lists red teaming among the actions essential for measuring the risk of unwanted activities. Source: Managing Misuse Risk for Dual-Use Foundation Models

Beyond regulations that specifically mention red teaming, companies must also recognize their broader responsibility for the AI software they use. For example, the US Equal Employment Opportunity Commission classifies algorithmic decision-making as an employee selection procedure. This means employers are fully accountable for any discrimination arising from AI biases that are difficult to detect and prevent without red teaming. 

The benefits of implementing red teaming in AI Systems

Implementing red teaming offers several critical benefits for developing and deploying robust AI systems.

Enhanced security and early threat detection

By simulating adversarial attacks, red teams help identify and mitigate security vulnerabilities. This early detection of potential threats allows organizations to prevent significant damages by addressing risks before they can be exploited.

Improved model performance and adaptation to emerging threats

Stress testing reveals weaknesses that can be addressed to enhance the model's accuracy, reliability, and overall performance. Continuous red teaming also enables organizations to stay ahead of the rapidly evolving landscape of cybersecurity threats.

Increased transparency and building stakeholder confidence

Rigorous generative AI red teaming builds trust among stakeholders by demonstrating a commitment to the AI model's security and fairness. Regularly validated AI systems, especially those assessed by credible external teams, help foster confidence in the system's safety and reliability. 

Regulatory compliance

As regulations surrounding AI systems become more stringent, AI red teaming ensures that models comply with legal and ethical standards. This approach addresses existing issues but also anticipates and prevents future problems.

These benefits highlight the value of red teaming as an essential component of AI model development. By allowing engineers to simulate various unwanted scenarios in a controlled environment, red teaming ensures the AI system can appropriately respond to potential threats.

AI Red teaming practices

Red teams are focused on simulating adversarial scenarios and testing the limits of AI models. Here’s a brief overview of some key red-teaming techniques ethical hackers use to enhance the security and reliability of AI systems.

Jailbreak prompting

Jailbreak prompting exposes weaknesses in LLMs by pushing the models to deviate from their safety constraints. This method reveals how models can be manipulated to produce harmful or biased outputs, highlighting potential conflicts between their capabilities and safety protocols. 

While cautious, this creative approach can overly restrict the model, leading to frequent evasiveness. Thus, there's a trade-off between making the model helpful by following instructions and keeping it harmless by minimizing the risk of harm.

Instructing the model to respond in code instead of natural language can also reveal its learned biases. (Source: Hugging Face)

Human-guided and automated red teaming

Human intuition and creativity help identify vulnerabilities in an AI system. A red team and its ethical hackers use their expertise to craft inputs that challenge the model's responses and test how well the AI adheres to ethical standards under pressure.

Also there are software tools used to mimic a wide range of real-world cyberattacks provide a scalable and efficient approach to generative AI red teaming with automated frameworks, which can conduct an unlimited number of attacks on the target system.

For example, multi-round automatic red-teaming (MART) involves an adversarial LLM and a target LLM working in cycles, where the adversarial LLM generates challenging prompts that the target LLM must learn to handle safely. Another technique, deep adversarial automated red-teaming (DART), dynamically adjusts attack strategies across iterations, further enhancing the model's safety.

Test cases can be automatically generated by a language model (LM) and replied by the target LM, with failing test cases being found by a classifier. (Source: Red Teaming Language Models with Language Models)

Automated tools often rely on pre-existing classifiers to detect undesirable outputs, limiting the adaptability of red teaming to specific models. However, certain approaches are specifically designed to eliminate this problem.

A framework suggested by MIT scientists in 2023. Their approach presumes starting with sampling from the target model. (Source: Explore, Establish, Exploit: Red-Teaming Language Models from Scratch)

Tools for an AI red teaming

A variety of tools have been developed to assist in the process, including specialized datasets, automated frameworks, and evaluation platforms that enhance the scope of red team activities. 

For instance, AttaQ is an AI red teaming dataset, which includes 1,402 adversarial questions to evaluate LLMs. This dataset serves as a benchmark for measuring potential risks. By utilizing this dataset, researchers can systematically test AI systems, identify ethical concerns, and work towards reducing harmful outcomes.

In the AttaQ dataset, all questions are divided into seven classes: deception, discrimination, harmful information, substance abuse, sexual content, personally identifiable information, and violence. Source: HuggingFace)

The open-source evaluation framework HarmBench identifies several desirable properties previously overlooked in red teaming evaluations, providing a systematic approach to benchmarking.

Illustration of the standardized evaluation pipeline, given an attack method and a model. A diverse set of behaviors is transformed into test cases. Source: HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal)

Prompting4Debugging is a specific tool to identify problematic prompts for diffusion models like Stable Diffusion. P4D demonstrated that many prompts, initially deemed "safe," can still bypass existing safeguards.

The Prompting4Debugging framework employs prompt engineering techniques to red-team the text-to-image diffusion model (Source: Prompting4Debugging)

Together, all these tools enable a thorough examination of generative AI models. By integrating relevant resources into red teaming processes, organizations can better safeguard their AI systems, contributing to developing safer and more trustworthy AI technologies.

How to make your model safe with Toloka

AI red teaming by Toloka can ensure your AI model is robust against potential threats and aligned with industry best practices. Our team of AI researchers, linguists, and subject matter specialists creates prompts specifically designed to trigger unwanted responses from LLMs. We generate a comprehensive report that details the prompts, responses, and classifications to help identify potential safety risks. Additionally, we assist in addressing these issues by delivering high-quality datasets to fine-tune your model, enhancing its safety and ethical standards. Book a demo with us to learn more.

Curious to learn more? Discover how red teaming enhances both the security and functionality of your AI models in our comprehensive guide—click here to dive in.