This article discusses vulnerabilities in large language model (LLM) frameworks, highlighting specific case studies of security issues like remote code execution and SQL injection. It offers lessons learned for both users and developers, emphasizing the importance of validation and cautious implementation practices.

This article outlines key security vulnerabilities identified by NVIDIA's AI Red Team in large language model (LLM) applications. It highlights risks such as remote code execution from LLM-generated code, insecure access in retrieval-augmented generation, and data exfiltration through active content rendering. The blog offers practical mitigation strategies for these issues.

The article discusses the potential security risks associated with using large language models (LLMs) in coding practices. It highlights how these models can inadvertently introduce vulnerabilities and the implications for developers and organizations. The need for robust security measures when integrating LLMs into coding workflows is emphasized.

The article provides an in-depth explanation of the Model Context Protocol (MCP), highlighting its role in enhancing the capabilities of large language models (LLMs) through improved context provision. It also conducts a detailed threat model analysis, identifying key security vulnerabilities and potential attack vectors associated with MCP's functionalities, such as sampling and composability.

PromptMe is an educational project that highlights security vulnerabilities in large language model (LLM) applications, featuring 10 hands-on challenges based on the OWASP LLM Top 10. Aimed at AI security professionals, it provides a platform to explore risks and mitigation strategies, using Python and the Ollama framework. Users can set up the application to learn about vulnerabilities through CTF-style challenges, with solutions available for beginners.