Project
About project
About project
About project
Modern LLM agents are increasingly granted access to corporate data, email, documents, and external services. In these environments, failures can have consequences far beyond incorrect responses—they may result in data leaks, unauthorized modifications, or unintended actions.
To deploy and continuously improve AI agents safely, organizations need a systematic methodology for assessing and monitoring an agent's security after every update, whether it involves the underlying model, the system prompt, or the connected tools.
SOLUTION
SOLUTION
SOLUTION
We developed a comprehensive red teaming methodology for an enterprise LLM agent and built a dataset of verified adversarial scenarios for automated security testing.
The benchmark was based on anonymized real-world interaction logs collected from users of the agent and reviewed by domain experts. From these interactions, we constructed adversarial test cases that simulate realistic attack vectors, including prompt injection, internal data leakage, data manipulation, and fraud-oriented scenarios.
For every test case, we defined explicit success criteria for the attack. Instead of evaluating only the model's final response, we analyzed the entire execution trace, including tool invocations, request parameters, and data access patterns. This enabled us to evaluate the agent's behavior at the action level rather than relying solely on textual outputs.
RESULTS
RESULTS
RESULTS
The client received an internal security benchmark tailored to its technology stack, business processes, and AI agent workflows.
The dataset contains both successful attack scenarios and examples of correct, policy-compliant behavior, making it suitable for regression testing of new agent versions. After integration with Promptfoo, security evaluation became part of the continuous integration pipeline: every change to the system prompt, knowledge base, or connected tools is automatically tested for newly introduced vulnerabilities before deployment.
PROCESS AND TECHNOLOGIES
PROCESS AND TECHNOLOGIES
PROCESS AND TECHNOLOGIES
The benchmark was created using a combination of automated scenario generation and expert validation.
AI trainers transformed real user requests into progressively more sophisticated adversarial conversations, constructing multi-turn dialogues in which the agent could fail only after several interactions. Every scenario was executed in an isolated environment with a fixed set of tools and data to ensure reproducible evaluation.
During testing, we analyzed multiple classes of security failures, including incorrect MCP tool invocations, unauthorized disclosure of confidential information, modification of information based on unreliable sources, and other violations of established security policies.
To automate regression testing, we designed a test case format compatible with Promptfoo and an LLM-as-a-Judge evaluation framework. This enabled integration of security validation into the client's existing CI/CD pipeline.
