CVE-2025-0313 in ollama
Summary
by MITRE • 03/20/2025
A vulnerability in ollama/ollama versions <=0.3.14 allows a malicious user to create a GGUF model that can cause a denial of service (DoS) attack. The vulnerability is due to improper validation of array index bounds in the GGUF model handling code, which can be exploited via a remote network.
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Analysis
by VulDB Data Team • 03/21/2025
The vulnerability identified as CVE-2025-0313 represents a critical denial of service weakness within the ollama/ollama software ecosystem affecting versions 0.3.14 and earlier. This flaw resides in the GGUF model parsing functionality where the software fails to properly validate array index bounds during model processing operations. The issue manifests when a malicious actor crafts a specially crafted GGUF model file that contains malformed array references which bypass normal validation checks. The vulnerability is particularly concerning because it can be exploited remotely over network connections, allowing attackers to trigger the denial of service condition without requiring local system access or elevated privileges.
The technical root cause of this vulnerability aligns with CWE-129, which addresses improper validation of array index bounds, and CWE-134, which covers format string vulnerabilities that can lead to arbitrary code execution or system instability. When the ollama software attempts to parse the malicious GGUF model, the insufficient bounds checking causes the application to attempt memory access operations at invalid array indices, leading to segmentation faults or memory corruption that ultimately results in application crash or system hang. This type of vulnerability falls under the ATT&CK technique T1499.004, specifically targeting application availability through resource exhaustion or process termination.
The operational impact of this vulnerability extends beyond simple service disruption as it affects the reliability and availability of AI inference services that depend on ollama for model execution. Organizations utilizing ollama for large language model deployments face potential business disruption when attackers exploit this weakness to terminate critical inference processes. The remote exploitability means that threat actors can target systems without physical access, potentially causing widespread service degradation across multiple endpoints. The vulnerability particularly impacts environments where ollama serves as a backend for AI applications, API services, or containerized inference platforms that may not have robust input validation mechanisms in place.
Mitigation strategies for CVE-2025-0313 should prioritize immediate patching of affected ollama installations to version 0.3.15 or later, which includes the necessary bounds checking improvements. Organizations should implement network segmentation and access controls to limit exposure of ollama services to untrusted networks, while also establishing robust input validation procedures for all model files before processing. Additionally, implementing monitoring solutions that detect unusual process termination patterns or memory access violations can help identify exploitation attempts. The fix addresses the underlying memory safety issue by introducing proper array boundary validation and error handling mechanisms that prevent invalid memory access operations, aligning with industry best practices for preventing buffer overflow conditions and maintaining system stability in machine learning inference environments.