Securing LLM Applications: Leveraging Generative AI Agents and Active Monitoring

Securing LLM Applications: Part 2 – Agents and Active Monitoring

Authored by: Suresh Bansal, Technical Manager – Xoriant

As the use of Large Language Models (LLMs) continues to rise, ensuring the security of these applications is paramount. In Part 1 of this blog series, we delved into the various risks faced by LLM applications. Now, in Part 2, we will explore how the implementation of agents and active monitoring can help mitigate these risks. By proactively understanding threats and taking necessary measures, we can enhance the safety and reliability of LLM applications.

Understanding Generative AI Security Agents

A Generative AI Security Agent is a sophisticated tool that combines LLMs with key modules such as memory, planning, and access to tools. In this setup, the LLM acts as the central intelligence, overseeing operations while utilizing memory and tools to execute tasks.

One practical application of generative AI agents is in security testing for LLM applications. Below is an outline of how a security testing agent framework operates:

Security Testing Agent Framework

When a task is marked with a star, it indicates that it can be performed either manually or automated by an LLM.

• Identify Categories & Descriptions
• Name & Description of Application
• Create X Prompts for Each Category
• Run Prompts Against Application
• Evaluate Results
• Publish Report

Active Monitoring for LLM Applications

Security measures should not be limited to the development phase but should extend to continuous monitoring post-deployment. Active monitoring plays a crucial role in safeguarding applications in production. Here’s an overview of the active monitoring process:

Active Monitoring Process

• Request Evaluator LLM
• Response Evaluator LLM

Cost Considerations

Implementing Request & Response Evaluator LLMs may result in additional costs due to the nature of these modules. It is essential to consider cost-saving strategies such as using cheaper or open-source LLMs and processing a subset of requests.

Implementing Agents and Active Monitoring

Securing LLM applications against potential threats requires a continuous and proactive approach. By integrating security measures throughout the development lifecycle and implementing active monitoring in production, vulnerabilities can be identified and addressed promptly. Automation plays a vital role in real-time threat detection and mitigation.

For a real-world example, a financial client successfully leveraged LLMs for customer service, handling sensitive data like Personally Identifiable Information (PII) and financial records. By implementing specialized agents and a multi-layered security approach, the client maintained top-tier data security, compliance, and customer trust.

Further Readings

1. LLM Vulnerabilities
2. Red teaming LLM applications
3. Quality & Safety of LLM applications
4. Red teaming LLM models

About the Author

Suresh Bansal, a Technical Manager at Xoriant, specializes in Generative AI and technologies such as Vector DB, LLM, Hugging Face, Llama Index, Lang Chain, Azure, and AWS. With a background in pre-sales and sales, Suresh excels in creating compelling technical proposals and ensuring client success. He has collaborated with clients globally and holds advanced partnerships with AWS.