AI-LLM-Security-Audit: A Practical Guide to Large Language Model Security Auditing

This open-source project provides a 10-dimensional LLM security audit framework covering key areas such as prompt injection, jailbreak attacks, RAG security, and supply chain risks, offering a practical checklist for security assessment of enterprise-level LLM applications. It addresses the current lack of a systematic framework for LLM security auditing, helping organizations shift from passive remediation to proactive assessment.

With the rapid adoption of Large Language Models (LLMs) in enterprise applications, security issues have become increasingly prominent, including prompt injection attacks, model supply chain contamination, training data leakage, and multimodal content risks. Compared to mature web application security auditing, LLM security auditing is still in its infancy. Many organizations lack a systematic security assessment framework when deploying LLM applications and mostly adopt a passive remediation approach, which is unacceptable in high-risk scenarios such as finance, healthcare, and government services. The industry urgently needs a comprehensive, practical, and actionable LLM security audit guide.

The ai-llm-security-audit project, open-sourced by the 0xelitesystem team, provides an LLM security audit framework covering 10 key dimensions. Each dimension includes detailed check items, attack scenario examples, and mitigation recommendations. The 10 dimensions are: Direct Prompt Injection, Indirect Prompt Injection, Jailbreak Attacks, RAG System Security, Output Processing Security, Model Supply Chain Security, Training Data Security, Agent Tool Security, Multimodal Security, and Assessment & Monitoring.

The project has significant practical value: Security teams can assess the security posture of LLM applications using the systematic checklist; Development teams can refer to best practices to integrate security considerations during the design phase; Auditors gain a standardized assessment framework; Researchers obtain a panoramic view of the LLM security field. Typical attack scenarios validate the framework's effectiveness, such as direct prompt injection (attackers embed system instructions to leak sensitive information), indirect prompt injection (contaminate RAG data sources to trigger attacks), and jailbreak attacks (induce models to generate non-compliant content through hypothetical scenarios).

The ai-llm-security-audit project provides a comprehensive and practical framework for LLM security auditing, covering the full lifecycle security considerations from input to output, training to deployment, and unimodal to multimodal. For organizations currently deploying or planning to deploy LLM applications, it is a valuable security assessment resource. Against the backdrop of increasingly important AI security, systematic security auditing should become a standard process for LLM application launch.

The project has limitations: It needs continuous updates to address rapidly evolving security threats and lacks supporting automated testing tools. Future development directions include: Developing an automated testing toolset; Establishing a community-driven threat intelligence sharing mechanism; Creating customized audit guides for specific industries such as finance and healthcare.