LLMGuardian is explicitly designed as a comprehensive LLM AI Model protection toolset that is aligned to addressing OWASP vulnerabilities in Large Language Models. The toolset is structured to protect production Generative AI (GenAI) LLM applications and specifically references the source for the established vulnerabilities: https://genai.owasp.org/llm-top-10/

The project's modular architecture incorporates several security components—Scanners, Defenders, Monitors, Vectors, Data, and Agency—that correspond directly to common LLM attack vectors and risk areas:

1. Security Components and Their Protection Mechanisms

The alignment is achieved by implementing specific modules to detect and mitigate threats that relate to common OWASP categories (such as Injection, Data Disclosure, and Excessive Agency):

Key Protection Mechanisms Aligned to Vulnerabilities

Scanners

Prompt injection detection, Data leakage scanning, Model security validation, Output validation checks

Defenders

Input sanitization, Output filtering, Rate limiting, Token validation

Monitors

Threat detection, Anomaly monitoring, Real-time usage tracking

Vectors

Addressing Supply chain vulnerabilities, Embedding weaknesses, Monitoring vector stores

Data

Protection from Data poisoning, Mitigation of Sensitive information disclosure, Data sanitizing

Agency

Protection against Excessive agency through Safe execution, Permission management, Scope limitation

2. Architecture and Deployment Alignment

The underlying design principles of LLMGuardian further ensure its effectiveness in addressing vulnerabilities:

• Security-First Design: Security considerations are built directly into the architecture.

• Testability: The toolset includes a dedicated test suite with security-specific test cases and vulnerability testing to validate its protection mechanisms.

• Deployment Features: The Huggingface Space implementation of LLMGuardian includes a FastAPI backend with endpoints specifically for Model scanning, Prompt injection detection, Input/output validation, and Rate limiting middleware.

Through this structure, LLMGuardian offers a defense-in-depth approach, covering detection (Scanners/Monitors), mitigation (Defenders/Agency/Data), and security validation (Testing/Scanners), all centered around addressing the established risks documented by OWASP for Large Language Models.

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