Ollama

Ollama is a widely adopted tool for running large language models locally, and despite a narrow product portfolio, its positioning as a commonly deployed inference engine has made it more prominent than typical in the vulnerability landscape. Vulnerabilities affecting the vendor skew strongly toward critical-severity outcomes, concentrating in path traversal, improper input validation, out-of-bounds reads, and divide-by-zero conditions that reflect both the complexity of model-loading and inference code and the security implications of accepting untrusted model files and user input. The single-product focus means that fixes must be applied uniformly across deployments, and the recurrence of path traversal and integrity-check weaknesses highlights the risk of local file exposure and model poisoning in environments where models are sourced from untrusted or third-party repositories. Defenders should monitor this vendor's releases closely given the concentration of critical findings and the breadth of deployment in development, research, and production environments; current exploitation activity and severity counts are shown alongside this summary.