If you are using local #LLM models, remember that the software used to run models such as #Ollama has a young codebase and it may be easier to find critical vulnerabilities in it, as mentioned in the following article. You should include such risks in your threat analysis process. https://lnkd.in/eXwZpnNN #Cybersecurity #AI
💥 EXCLUSIVE: Wiz Research uncovers CVE-2024-37032, aka #Probllama — a vulnerability in Ollama that left thousands of AI models exposed 😲 This flaw could allow attackers to gain remote code execution and alter prompt answers to generate misleading information. Security teams should update their Ollama instances to the latest version to mitigate this vulnerability. Kudos to our research team, Sagi Tzadik and Shir Tamari for uncovering this and to Ollama for the collaboration with the fast fix 🚀 https://lnkd.in/eXwZpnNN
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
5moIt's crucial to acknowledge the potential vulnerabilities in emerging AI technologies like local LLM models, such as Ollama, especially given their relatively young codebases. Historical data often shows that early-stage software can be more susceptible to security flaws, necessitating rigorous threat analysis and mitigation strategies. Considering the rapid advancement of AI and its integration into critical systems, how do you propose balancing innovation with cybersecurity resilience in the development and deployment phases of AI technologies? Specifically, in environments where real-time data processing and decision-making rely heavily on AI, how can we ensure robust defenses against evolving cyber threats without compromising on technological progress?