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The Expanding Role of Red Teaming in Defending AI Systems

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The Expanding Role of Red Teaming in Defending AI Systems

Artificial Intelligence (AI) systems are transforming industries, from healthcare to finance, but their dynamic, adaptive, and often opaque nature introduces unique security challenges. Unlike traditional software, AI systems are susceptible to novel threats like adversarial inputs, data poisoning, and model manipulation, necessitating specialized defense strategies. AI red teaming, the practice of simulating adversarial attacks to uncover vulnerabilities, has emerged as a cornerstone of AI security. By adopting an attacker’s perspective, red teams proactively identify weaknesses, ensuring robust, trustworthy systems. This article explores the expanding role of AI red teaming, supported by real-world examples from 2025, and outlines its critical importance in defending AI systems.

The Evolution of Red Teaming for AI

Red teaming, rooted in military and cybersecurity traditions, involves simulating adversarial tactics to test system defenses. Traditional red teaming targets static systems, leveraging decades of experience in predictable frameworks. AI systems, however, are dynamic and non-deterministic, making vulnerabilities harder to detect. Threats like prompt injections, adversarial examples, and model theft require specialized approaches. AI red teaming adapts these principles, combining cybersecurity expertise with AI-specific knowledge to address these challenges.

The importance of AI red teaming has grown with the rapid adoption of generative AI (GenAI) and large language models (LLMs). A 2025 Gartner forecast predicts IT spending on GenAI will rise from $5 billion in 2024 to $39 billion by 2028, expanding attack surfaces and necessitating robust testing. Red teaming ensures systems are resilient against evolving threats, fostering trust in AI deployments.

Key Components of AI Red Teaming

AI red teaming encompasses several critical processes:

  1. Threat Modeling: Identifying potential adversaries, from hobbyists to state-sponsored actors, and mapping data flows to pinpoint vulnerabilities.
  2. Adversarial Testing: Crafting inputs, such as perturbed images or malicious prompts, to manipulate model behavior.
  3. Bias and Fairness Audits: Testing for discriminatory outputs across demographic scenarios to ensure ethical performance.
  4. Model Security Checks: Probing for data leaks, model extraction, or backdoors.
  5. Incident Response Simulations: Evaluating blue team (defensive) responses to attacks.

These components require interdisciplinary teams, including AI experts, cybersecurity professionals, and data scientists, to address the multifaceted nature of AI vulnerabilities.

Real-World Examples of AI Red Teaming in 2025

  1. OpenAI’s External Red Teaming Initiative

OpenAI has pioneered AI red teaming, emphasizing human-in-the-middle designs to combine human expertise with automated testing. In January 2025, OpenAI published two papers detailing its approach. The first, “OpenAI’s Approach to External Red Teaming,” describes how specialized external teams, including cybersecurity and subject matter experts, uncovered vulnerabilities in ChatGPT’s security perimeters. These teams identified biases and voice mimicry risks that automated testing missed, leading to enhanced safety protocols. The second paper introduced an automated framework using multi-step reinforcement learning to generate diverse attack scenarios, improving model resilience. OpenAI’s efforts have set a benchmark, with 73% of organizations recognizing red teaming’s importance, though only 28% maintain dedicated teams.

  1. Microsoft’s AI Red Team for Bing Chat

Before launching Bing Chat in 2023, Microsoft conducted extensive AI red teaming, a practice that continued into 2025. Dozens of security and responsible AI experts spent hundreds of hours probing GPT-4-based models for risks like prompt injection and ungrounded content generation. In 2025, Microsoft’s red team identified a novel vulnerability in Bing Chat’s plugin ecosystem, where malicious plugins could manipulate outputs. This led to stricter plugin vetting and real-time monitoring, preventing potential exploits. Microsoft’s collaboration with MITRE on the Adversarial Machine Learning Threat Matrix further enhanced its red teaming framework, ensuring robust defenses.

  1. Google’s AI Red Team and Secure AI Framework (SAIF)

Google’s AI Red Team, detailed in a 2023 report updated in 2025, focuses on realistic attack simulations. In 2025, the team identified a prompt attack vulnerability in Google’s AI-powered search, where crafted inputs could generate misleading results. By leveraging insights from Mandiant and Google DeepMind, the team mitigated this risk through enhanced input filtering. Google’s Secure AI Framework (SAIF) integrates red teaming to address risks like data poisoning and model exfiltration, ensuring safer AI deployments. This approach helped Google maintain trust in its AI-driven products, including Gemini.

  1. Lakera’s Gandalf Platform

Lakera’s Gandalf, an interactive red teaming platform, has redefined AI security testing in 2025. With over 25 years of cumulative gameplay from millions of global players, Gandalf’s threat intelligence database maps evolving attack landscapes. In a 2025 case study, Lakera collaborated with Dropbox to secure LLM-powered applications. Gandalf identified a jailbreak vulnerability in Dropbox’s AI features, where crafted prompts could bypass safety filters. Lakera’s red teaming led to the implementation of Lakera Guard, enhancing data protection and user trust. This example underscores the value of community-driven red teaming in identifying real-world vulnerabilities.

  1. HackAPrompt 2.0 Competition

The HackAPrompt 2.0 competition, launched in 2025, is the largest AI red teaming event to date, analyzing generative AI weaknesses. Building on HackAPrompt 1.0’s analysis of 600,000 adversarial prompts, the 2025 event revealed how simple prompt manipulations could trick models into producing harmful outputs, such as “I have been PWNED.” Organizers used these findings to improve model guardrails, demonstrating the power of crowd-sourced red teaming. The competition highlighted the fragility of LLMs under minimal pressure, prompting developers to prioritize adversarial testing.

Challenges in AI Red Teaming

Despite its benefits, AI red teaming faces several challenges:

  • Complexity of AI Systems: The non-deterministic nature of LLMs makes it difficult to anticipate all attack vectors.
  • Resource Intensity: Building dedicated red teams requires significant investment, with only 28% of organizations maintaining them.
  • Evolving Threats: AI-driven cyberattacks, like those using DeepPhish or PassGAN, evolve rapidly, requiring continuous adaptation.
  • Balancing Security and Usability: Overly strict defenses can hinder functionality, while lenient ones expose risks.

To address these, organizations are adopting automated red teaming tools, like SydeLabs (acquired by Protect AI in 2024), to scale vulnerability detection. Collaboration between red and blue teams also enhances defensive capabilities.

Best Practices for Effective AI Red Teaming

  1. Interdisciplinary Teams: Combine AI experts, cybersecurity professionals, and data scientists for comprehensive testing.
  2. Realistic Simulations: Use threat intelligence, like MITRE ATT&CK, to emulate real-world adversaries.
  3. Continuous Testing: Integrate red teaming into development cycles to address evolving threats.
  4. Automated Tools: Leverage platforms like Gandalf or Counterfit for scalable testing.
  5. Regulatory Compliance: Ensure red teaming aligns with standards like the EU AI Act to meet safety and ethical requirements.

The Future of AI Red Teaming

As AI systems become more autonomous, red teaming will evolve to address agentic AI, which pursues goals independently. Brenda Leong of ZwillGen predicts that red teaming for agentic AI will require longer testing cycles, multidisciplinary teams, and scenario-based threat models to simulate extended use and behavioral drift. For example, a multi-agent system managing supply chain logistics could inadvertently violate regulations if not rigorously tested, highlighting the need for adaptive red teaming.

The global cybersecurity market, including red teaming, is projected to grow from $149.5 billion in 2023 to $423.67 billion by 2032, driven by AI adoption. Initiatives like OpenAI’s automated frameworks and Lakera’s Gandalf platform will continue to shape the field, making red teaming more accessible and effective.

AI red teaming is indispensable for securing dynamic, high-stakes AI systems. Real-world examples from OpenAI, Microsoft, Google, Lakera, and HackAPrompt 2.0 demonstrate its impact in uncovering vulnerabilities and enhancing resilience. By simulating adversarial attacks, red teaming builds trust in AI deployments, ensuring they are safe, ethical, and reliable. As AI evolves, organizations must prioritize red teaming, integrating automated tools, interdisciplinary expertise, and continuous testing to stay ahead of threats. In 2025 and beyond, AI red teaming will remain a critical defense strategy, safeguarding the future of AI innovation.