Critical Flaws in Ollama AI Framework Could Enable DoS, Model Theft, and Poisoning
Six security holes in the Ollama artificial intelligence (AI) framework have been found by cybersecurity experts. These vulnerabilities might be used by a malevolent actor to carry out a variety of tasks, such as model poisoning, denial-of-service attacks, and model theft.
According to a paper released last week by Oligo Security researcher Avi Lumelsky, the vulnerabilities used together might enable an attacker to perform a variety of harmful operations with a single HTTP request, such as model poisoning, model theft, denial-of-service (DoS) attacks, and more.
Large language models (LLMs) can be installed and run locally on Windows, Linux, and macOS systems using the open-source Ollama application read more about Critical Flaws in Ollama AI Framework Could Enable DoS Model Theft an...
