CVE-2025-66960 is a vulnerability identified in the ollama software version 0.12.10, specifically in the function readGGUFV1String located in the source file fs/ggml/gguf.go. This function is responsible for reading string lengths from GGUF metadata, which is untrusted input. The vulnerability arises because the function does not properly validate or sanitize the length value read from this metadata, allowing an attacker to supply malformed or malicious GGUF metadata with manipulated string length fields. When the software attempts to process this crafted input, it can lead to a denial of service (DoS) condition, causing the application to crash or hang, thereby impacting availability. The flaw can be triggered remotely without requiring authentication or user interaction, increasing its risk profile. Although no exploits have been reported in the wild yet, the vulnerability is publicly disclosed and assigned a CVE identifier. The lack of a CVSS score means that severity must be assessed based on impact and exploitability factors. The vulnerability primarily affects availability, with no direct impact on confidentiality or integrity. The affected software, ollama, is used in AI and machine learning workflows, which may process GGUF metadata files as part of their operations. The absence of patches or mitigation details means organizations must be vigilant and prepare to apply fixes once released.

For European organizations, the primary impact of CVE-2025-66960 is the potential disruption of AI or machine learning services that rely on ollama software to process GGUF metadata. A successful exploitation could cause denial of service, leading to downtime, loss of productivity, and potential operational delays. This is particularly critical for sectors dependent on continuous AI model training or inference, such as finance, healthcare, automotive, and research institutions. While the vulnerability does not compromise data confidentiality or integrity, service unavailability can indirectly affect business continuity and trust. Organizations processing untrusted or external GGUF metadata are at higher risk. The absence of known exploits provides a window for proactive mitigation, but the remote and unauthenticated nature of the attack vector increases urgency. European entities with AI infrastructure integrating ollama should assess their exposure and prepare incident response plans to handle potential DoS events.

To mitigate CVE-2025-66960, organizations should first identify all instances of ollama version 0.12.10 or earlier in their environment. Until a patch is released, restrict the ingestion of GGUF metadata to trusted sources only, implementing strict validation and sanitization at the application or network perimeter. Employ input validation controls to check string lengths and metadata structure before processing. Monitor application logs and system behavior for signs of crashes or hangs related to GGUF metadata processing. Consider deploying runtime application self-protection (RASP) or web application firewalls (WAF) that can detect and block malformed inputs. Engage with the ollama vendor or open-source community to obtain patches or updates addressing this issue as soon as they become available. Additionally, implement robust backup and recovery procedures to minimize downtime impact. Educate developers and security teams about this vulnerability to ensure rapid response and remediation.