What Is AI Red Teaming? Benefits and Examples

Key Highlights

• Generative AI complicates pre-existing security risks and presents new vulnerabilities.
• Human beings are central to securing organizational systems through red teaming
• Active, multilayered defense is essential for securing AI models

What Is a Red Team?

A red team is a group of security experts who simulate a cyber attack against an organization to identify security vulnerabilities. By exposing weaknesses through phishing, social engineering, and penetration testing, a red team can help an organization improve security. In some cases, the organization may form a blue team to defend against the simulated cyber attack in a collaborative effort. 

What Is Red Teaming in AI?

Red teaming in AI is the simulation of a cyber attack against an artificial intelligence system to proactively test its security and spot vulnerabilities before malicious actors can exploit them. By probing and breaking AI systems with red teaming efforts, organizations can improve their safety and security.

Prompt Hacking Large Language Models

Ensuring AI systems do not produce hateful or dangerous content has become the main focus of AI ethics. Some AI companies have put together red teams to test a large language model’s ability to maintain guardrails and avoid potential harms. These teams employ prompt hacking techniques, which involve the use of input prompts designed to trick AI into generating text, audio, video, or images that violate its AI safety protocol.

Benefits of AI Red Teaming

Advanced AI red teaming offers several benefits over traditional software testing. It delivers better defenses against leading-edge attacks, reduced biases, and deep insights that would otherwise go untapped. Take a look at some of the main benefits of red teaming AI systems. 

Improved Resistance to Bleeding-Edge Attacks

While inserting adversarial examples into AI models’ training data is common practice, it often protects against old, well-known threats. Generative AI red teaming goes deeper by testing AI systems against emerging security risks.

Reduced Bias

An AI red team can identify and eliminate biases in training data and processing. This modern approach prevents unwanted outputs, such as hate speech, and promotes trust in organizational implementations.

Better Safety and Increased Compliance

Red teaming AI systems results in more safety and thorough compliance with applicable laws. AI red teams verify that AI systems adhere to the ethical standards laid out by the General Data Protection Regulation (GDPR) and the EU Artificial Intelligence (AI) Act, minimizing the risk of accruing regulatory fines. 

Heightened Performance

Adversarial AI testing with a red team can do more than find flaws — it can boost performance. By simulating rare or extreme events that tax a system’s resources, red teamers can identify performance bottlenecks. Once personnel alleviate these bottlenecks, performance will improve even in the harshest conditions. 

Deep Insights

Developers of generative AI systems often possess some understanding of common security concerns, like brute force or backdoor attacks. However, many of them lack the subject matter expertise to anticipate the latest cybersecurity threats. By drawing upon specialized knowledge and adopting the perspective of a threat actor, a red teamer can provide insights that company insiders overlook.

Real Red Teaming Exercise Examples

Most midsize and large organizations employ red teams to test traditional software and AI systems. Corporations and government agencies constantly check for security risks through standard cybersecurity and AI testing protocols. Examine some real-world examples of AI red teaming at Microsoft, JPMorgan Chase, and the United States Department of Defense.

Microsoft AI Red Team

In 2023, Microsoft formed an AI red team to test over 100 generative AI applications, including ChatGPT-4. The team assessed risks like model manipulation, prompt hacks, disclosure of sensitive data, and the generation of copyrighted material and biased information. This real-world example of AI red teaming helped the company achieve its goal of securing AI systems under the Microsoft brand.

JPMorgan Chase Red Teaming

JPMorgan Chase maintains a red team to simulate cyberattacks and identify risks within their security technology. In 2019, the red team penetrated the company’s security systems by tricking employees into disclosing login usernames and passwords. In response, JPMorgan Chase implemented multi-factor logins and bolstered its employee training program.

United States Department of Defense Red Team Testing

The United States Department of Defense (DoD) often deploys red teams to test for security vulnerabilities within its military networks. In 2018, the DoD conducted a large-scale attack that exploited poorly configured databases and weak passwords. The eye-opening result prompted the government to revise military infrastructure protocols.

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Comparing Red Teaming, Vulnerability Assessments, and Pen Testing

Cybersecurity communities have several ways to test AI systems against cyber attacks. They can check AI security through red teaming, vulnerability assessments, pen testing, and other forms of internal and external testing. Let’s examine the differences between three of the most popular approaches to testing artificial intelligence.

Red Teaming

Red teaming is a structured testing effort with multi-vector attack simulations to evaluate system resilience against advanced persistent threats (APTs) and sophisticated threat actors. 

Its comprehensive security evaluation framework is optimized when executed in conjunction with concurrent blue team defensive operations, facilitating real-time assessment of security controls, incident response protocols, and threat detection mechanisms. The attack surface encompasses both technical and human-centric attack vectors, including social engineering exploitation, physical security compromise attempts, and network/system penetration methodologies.

The primary success metrics include mean time to detection (MTTD), depth of penetration achieved within the target environment, and operational persistence duration. These metrics quantify the effectiveness of security operations center (SOC) capabilities, defensive countermeasures, and incident response procedures. 

The engagement parameters of red teaming typically facilitate unrestricted tactical flexibility, emulating the techniques of real-world threats to achieve predefined tactical and strategic objectives while measuring defensive control efficacy and security team response capabilities.

Vulnerability Assessments

Vulnerability assessments are a comprehensive security analysis technique that implements systematic enumeration and classification of security deficiencies within an enterprise infrastructure or discrete system architecture. The assessment procedure generates a hierarchically prioritized vulnerability matrix, with associated remediation vectors and mitigation strategies quantified by criticality metrics.

A distinguishing characteristic of this security evaluation is its non-exploitative nature. It explicitly excludes proof-of-concept exploitation attempts against identified attack surfaces, focusing instead on vulnerability discovery, impact analysis, and risk quantification through passive reconnaissance and configuration analysis.

Penetration Testing

Penetration testing, or pen testing, is a precise, offensive security methodology for vulnerability exploitation validation. In contrast to vulnerability enumeration and surface analysis, pen testing encompasses active exploitation attempts against identified attack vectors. The engagement parameters and attack surface are explicitly delineated through client-specified rules of engagement (ROE), frequently informed by preliminary vulnerability assessment findings. 

In the context of machine learning systems, organizations may prioritize testing for model evasion attacks and prompt injection vulnerabilities. However, more sophisticated adversarial attacks such as model extraction, training data poisoning, or membership inference attacks typically fall outside standard pen testing scope due to their specialized nature and potential for system degradation.

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Artificial Intelligence Red Teaming by AiFA Labs

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