GenAI Red Teaming evaluates defensive capabilities by simulating real-world threats. In the context of generative AI security, Red Teaming involves a systematic verification of systems against potential adversarial behaviors, emulating specific Tactics, Techniques, and Procedures (TTPs) that malicious actors could use to exploit AI systems.

For an overview of the methodologies and fundamental principles, consult the complete guide to GenAI Red Teaming.

Red Teaming Strategy for Large Language Models

An effective Red Teaming strategy for Large Language Models requires risk-driven contextual decisions, aligned with organizational goals—including those of responsible AI—and the specific nature of the application. Inspired by the PASTA (Process for Attack Simulation and Threat Analysis) framework, this strategy emphasizes risk-oriented thinking, contextual adaptability, and cross-functional collaboration.

Risk-based Scoping

The first step consists of defining the testing perimeter based on criticality and potential business impact:

• Prioritize applications and endpoints to be tested based on criticality and potential business impact.
• Consider the type of LLM implementation and the outcomes the application has access to, whether as an agent, classifier, summarizer, translator, or text generator.
• Focus on applications that handle sensitive data or drive significant business decisions.
• Conduct an impact analysis regarding the organization’s Responsible AI (RAI) and use the NIST AI RMF to map, measure, and manage; the Red Team is an integral part of these exercises.

Cross-functional Collaboration

Collaboration between different functions is essential to ensure consistency and organizational support:

• Obtain consensus from diverse stakeholders, such as Model Risk Management (MRM), Legal, Risk, and Information Security, on the processes, process maps, and metrics that will guide continuous oversight.
• Collectively define performance thresholds for chosen metrics, agree on escalation protocols, and coordinate responses to identified risks.
• This collaboration ensures consistency, transparency, and support for responsible, secure, and compliant AI deployments.

Tailored Assessment Approaches

There is no one-size-fits-all approach for every context:

• Select and adapt the methodology best suited to the complexity and integration level of the application.
• Not all LLM integrations are suitable for black-box testing; for systems deeply integrated into processes, a gray-box or assumed-breach assessment is preferable.

Clarity of Red Teaming Objectives

Defining the expected outcomes of the Red Team engagement in advance is fundamental to measuring success:

• Objectives may include testing for domain compromise, critical data exfiltration, or the induction of unintended behaviors in crucial business workflows.
• Documenting objectives allows for aligning expectations between technical teams and business stakeholders.

Threat Modeling and Vulnerabilities Assessment

Threat modeling provides the foundation for identifying and prioritizing risks:

• Development of a threat model based on business and regulatory requirements.
• Ask fundamental questions to guide the analysis:
  1. What are we building with AI?
  2. What can go wrong in terms of AI security?
  3. What can undermine the trustworthiness of the AI?
  4. How will we address these issues?
• Integrate known threats and architectural risks, such as those identified by third-party frameworks including Berryville IML.

Model Reconnaissance and Application Decomposition

The reconnaissance phase allows for understanding the internal structure of the model:

• Analyze the LLM structure via APIs or interactive playgrounds.
• Verify architecture, hyperparameters, number of transformer layers, hidden layers, and feedforward network dimensions.
• Understanding the internal workings allows for a more precise exploitation strategy.

Attack Modelling and Exploitation of Attack Paths

Use the gathered information to build realistic attack scenarios:

• Use the information gathered during the reconnaissance and vulnerability assessment phases to devise realistic attack scenarios.
• Simulate adversarial behaviors for all defined objectives, ensuring the method reflects authentic threats to the organization.

Risk Analysis and Reporting

The final phase transforms technical results into concrete actions:

• At the end of the tests, analyze all detected risks and vulnerabilities.
• Present the results clearly, along with recommendations for mitigation actions and escalation paths.
• This allows stakeholders to make informed decisions to strengthen security and trustworthiness in LLM-based applications.

Useful Insights

To delve deeper into the operational and methodological aspects of security testing for generative AI, consult these resources:

• GenAI Red Teaming – overview of methodologies and fundamental principles.
• GenAI Red Teaming Techniques – operational techniques for testing generative AI systems.
• Metrics for GenAI Red Teaming – KPIs and metrics to measure test effectiveness.
• Threat Modeling for AI and LLMs – methodologies for identifying and prioritizing threats.
• Risks and Threats in GenAI Red Teaming – overview of specific risks for generative AI systems.
• Blueprint for GenAI Red Teaming – implementation framework for structured testing programs.