📌What is AI Red Team?
In our previous post, we looked at why red teaming is important for generative AI models and how it can be done.
Today, we’ll dive into:
- how companies can actually organize and run red teaming efforts,
- what tools and frameworks are available, and
- what industry-specific factors need to be considered.
Building an Internal Red Team
In large organizations or AI-first companies, it’s common to establish a dedicated AI red team to conduct continuous adversarial testing of models. These teams typically include security experts, ML engineers, and policy analysts, and are responsible for tasks such as:
Designing and executing attack scenarios before model deployment
Reviewing system prompts and filtering logic
Collaborating with domain experts to run realistic, context-driven tests
Reporting vulnerabilities and recommending mitigation strategies
Collaborating with External Experts and the Community
Not every organization has the resources to build an in-house team. In those cases, working with external security consultants, academic researchers, and industry communities becomes essential. Crowdsourced red teaming challenges are also gaining traction, with many companies opening their AI systems to external testing in controlled environments.
Embedding Red Teaming into the Development Lifecycle
Red teaming shouldn’t be treated as a one-off event. It needs to be a repeatable and integrated part of the AI development lifecycle. Here’s how it can be structured:
Planning phase: Define attack scenarios and safety objectives early on
Development phase: Run regular red team simulations alongside model training
Pre-deployment phase: Conduct intensive red team campaigns and third-party audits
Post-deployment: Monitor user feedback, test for newly emerging vulnerabilities, and maintain a rollback plan
This approach ensures that safety and robustness are considered at every stage of model development and deployment.
Automation Tools
Microsoft PyRIT: An open-source LLM red teaming toolkit for writing and running automated attack scripts.
RedTeaming LLM Agents: AI-driven agents that generate adversarial prompts and validate model responses. Used by organizations like Anthropic and Meta.
Security Benchmarks
OWASP LLM Top 10: A guideline covering the ten most common LLM-specific threats, such as prompt injection and data leakage.
CyberSecEval: Led by Meta, this benchmark measures metrics like resistance to prompt injection.
Alignment and Safety Testing
OpenAI Evals: An open-source framework for automated scenario-based evaluation.
AI Verify (Singapore): A government-led platform assessing policy alignment, ethics, and factuality.
Scale AI Evaluation Platform: Compares models under identical conditions and visualizes attack success rates.
Finance
- Goal: Protect sensitive information and ensure compliance with regulations (e.g., Fed, SEC)
- Test Cases:
Insider information leakage
Fraudulent recommendation scenarios
Biased lending advice
Healthcare
- Goal: Patient safety and data privacy (e.g., HIPAA compliance)
- Test Cases:
Generation of incorrect medical guidance
Safe handling of requests related to self-harm or risky behavior
Exposure of sensitive data from training sets
Education
- Goal: Preserve learning integrity, prevent cheating, and ensure age-appropriate use
- Test Cases:
Requests for assignment completion
Generation of historically biased content
Handling of inappropriate content for minors
Red teaming isn’t just about security. It’s about building a foundation of trust in AI systems. In that sense, it’s an investment in long-term success.
By proactively “attacking” their own AI models, companies can uncover potential risks early and strengthen user confidence before issues ever reach the real world.
Check out how we evaluate AI Safety
