GuardLLM: Hardening Tool Calls for Secure LLM Applications

GuardLLM is a Python library that aims to harden LLM-based applications against various security threats. It provides a range of security controls, including input sanitization, content isolation, provenance tracking, and action gating. The library is designed to be model-agnostic, meaning it can be used with different LLMs, and it applies a defense-in-depth security model. GuardLLM addresses critical vulnerabilities such as prompt injection, data exfiltration, and cross-boundary abuse. It includes features like canary token detection, request binding, and outbound DLP to prevent unauthorized access and data leakage. The library also offers OAuth/OIDC integration patterns for mapping user scopes to tool policy decisions and provides structured audit logging hooks for monitoring and incident response. While GuardLLM passes all benchmark cases in its repository, it's important to note that perfect security is not achievable. The library should be used as one layer in a broader security architecture that includes robust authentication/authorization, network and runtime isolation, and secret management. The availability of tools like GuardLLM is crucial for building secure and reliable LLM applications, but developers must remain vigilant and continuously adapt their security measures to address emerging threats.