Secure AI Agents: Exhaustive testing with continuous LLM Red Teaming

Testing AI agents is challenging as continuously emerging vulnerabilities—from hallucinations to security exploits—expose organizations to significant reputational and financial risks when deployed systems fail in production environments. With thousands of AI incidents already reported, organizations deploying generative AI face increasing regulatory scrutiny and customer expectations for reliable, secure systems.

In this article, we describe how the Giskard LLM Evaluation Hub addresses testing of LLM-based systems through three key points: adversarial LLM agents that automate red teaming across security and quality dimensions, annotation tools that integrate domain-specific business expertise, and automated continuous red teaming that updates test cases as contexts evolve. This approach delivers exhaustive risk coverage, ensuring your AI systems remain protected against both current and emerging threats.

Implementing LLM Red Teaming to test AI agents

The LLM Evaluation Hub implements a structured workflow that balances automation with business expertise. Rather than relying solely on generic test cases or manual review, this approach enables targeted assessment of domain-specific risks.

Exhaustive risk detection for AI agents

Traditional testing approaches often miss critical edge cases or fail to adapt to evolving threats. By generating synthetic test cases that specifically target known vulnerability categories as well as domain-specific hallucinations, the LLM Evaluation Hub creates comprehensive coverage across all failure modes of LLM agents. This includes detecting hallucinations, identifying potential information disclosure risks, and preventing harmful content generation.

The system's ability to generate both legitimate and adversarial queries ensures a balanced testing approach that reflects real-world usage patterns. When combined with domain knowledge, these synthetic datasets provide unprecedented coverage of potential security and compliance risks.

Continuous AI Red Teaming with alerting

One of the most significant advantages of this approach is the shift from one-off to continuous evaluation. As new vulnerabilities emerge or business contexts evolve, the system automatically enriches test cases through monitoring of internal data, external sources, and security research.

The alerting system provides notifications when new vulnerabilities are detected, allowing teams to address issues before they affect users. This proactive monitoring approach is particularly valuable for maintaining conformity with evolving production and security standards.

Domain expert integration through annotation

While automation drives efficiency, business expertise remains critical for effective testing. The LLM Evaluation Hub provides annotation tools that enable domain experts to refine test cases without requiring technical expertise in AI systems. This feedback loop ensures that automated assessments align with business requirements and risk tolerance.

By transforming conversations into test cases, subject matter experts can contribute their specialized knowledge directly to the testing process. This collaborative approach bridges the gap between technical implementation and business objectives.

From LLM evaluation to continuous Red Teaming

The LLM Evaluation Hub enables a four-step workflow to implement effective AI agent testing with maximum coverage while minimizing manual effort:

1. Generation of synthetic data – Automatically create test cases with a focus on legitimate and adversarial queries that target potential security vulnerabilities.

2. Business annotation – Enable domain experts to review and refine test cases through annotation tools.

3. Test execution automation – Run evaluations in development, CI/CD pipelines, or production, and set up alerts for detected vulnerabilities.

4. Continuous red teaming – Ensure testing remains effective against evolving threats through automated enrichment of test cases based on internal and external changes.

In an upcoming tutorial we will provide a detailed guide on how to implement LLM-as-a-judge to test AI agents.

Conclusion

The LLM Evaluation Hub implements automated & continuous LLM red-teamers & judges as well as business annotation interfaces, providing organizations with an effective balance between automation and human expertise. This approach addresses the fundamental challenges of generative AI testing: infinite test cases, domain-specific requirements, and rapidly evolving threats.

As AI becomes increasingly embedded in critical business processes, exhaustive & continuous testing approaches like the LLM Evaluation Hub are essential for maintaining trust, ensuring security, and protecting brand reputation. 

Reach out to our team to discuss how the LLM Evaluation Hub can address your specific AI security challenges.