LLM Security

With the popularity of large models (LLMs), open-source localized deployment frameworks, represented by Ollama, have dramatically lowered the threshold for developers to use and manage AI models. However, this convenience has also spawned new, large-scale security risks. A recent study jointly published by SentinelOne, Censys, and Pillar Security reveals the startling fact that there are more than 175,000 publicly exposed instances of Ollama on the Internet globally, creating a massive AI computing infrastructure security attack surface risk
This report aims to analyze the technical aspects of this incident. This report aims to provide an in-depth technical analysis of this incident, analyze its attack surface, realistic threats, systemic risks, and propose corresponding enterprise-level security hardening and governance strategies.

With the widespread penetration of Large Language Models (LLM) and Generative Artificial Intelligence (GenAI) in enterprise applications, the traditional software security paradigm based on deterministic logic is struggling to cope with new stochastic threats such as model inversion, data poisoning, and cue word injection.Google's Secure AI Framework (SAIF), to be launched in 2023, proposes a systematic defense architecture that aims to combine traditional Cybersecurity best practices with the specificities of Artificial Intelligence (AI) systems. The Secure AI Framework (SAIF), launched by Google in 2023, proposes a systematic defense architecture that aims to combine the best practices of traditional cybersecurity with the specificities of AI systems. In this paper, we will analyze the six core pillars, ecological synergy mechanism and evolution path of SAIF from the perspective of architectural design, providing theoretical and practical references for the construction of enterprise-level AI security system.