CVE-2025-66959 Overview
A denial of service vulnerability exists in Ollama v.0.12.10 that allows remote attackers to crash the application through the GGUF decoder component. The vulnerability stems from improper input validation (CWE-20) when processing specially crafted GGUF model files, leading to an unhandled panic condition that terminates the service.
Critical Impact
Remote attackers can cause service disruption by sending malformed GGUF data to Ollama instances, potentially impacting AI/ML workflows and production deployments relying on the service.
Affected Products
- Ollama v.0.12.10
- Potentially earlier versions with the same GGUF decoder implementation
Discovery Timeline
- 2026-01-21 - CVE CVE-2025-66959 published to NVD
- 2026-01-22 - Last updated in NVD database
Technical Details for CVE-2025-66959
Vulnerability Analysis
This vulnerability affects the GGUF (GPT-Generated Unified Format) decoder component within Ollama, a popular open-source platform for running large language models locally. The GGUF format is used to store and load machine learning model weights and metadata.
The core issue lies in the decoder's failure to properly validate length fields before performing memory copy operations. When the decoder processes a GGUF file with maliciously crafted length values, it triggers a panic condition due to unchecked boundary access. This represents a classic improper input validation flaw where untrusted input is used directly in memory operations without adequate sanity checking.
The network-accessible nature of this vulnerability means that any Ollama instance exposed to untrusted networks—whether through direct API access or via applications that proxy GGUF file uploads—could be targeted for denial of service attacks.
Root Cause
The root cause is improper input validation (CWE-20) in the GGUF decoder's copy operation. The decoder fails to verify that length parameters specified in the GGUF file header are within acceptable bounds before using them in memory allocation or copy operations. When an attacker supplies a GGUF file with an excessively large or negative length value, the decoder attempts to perform an invalid memory operation, causing the Go runtime to panic and terminate the process.
Attack Vector
The attack can be executed remotely over the network without requiring authentication or user interaction. An attacker can exploit this vulnerability by:
- Crafting a malicious GGUF file with invalid length parameters in the header
- Submitting the malformed file to an Ollama instance through its API endpoints
- The GGUF decoder processes the file and encounters the invalid length
- The unchecked length value triggers a panic during the copy operation
- The Ollama service crashes, resulting in denial of service
For detailed technical analysis of the panic condition and exploitation mechanics, refer to the Zero Blog CVE-2025-66959 Analysis.
Detection Methods for CVE-2025-66959
Indicators of Compromise
- Unexpected Ollama service crashes or restarts in system logs
- Panic stack traces in Ollama logs referencing the GGUF decoder or copy operations
- Unusual network traffic patterns with malformed GGUF file uploads
- Repeated service unavailability correlated with model loading attempts
Detection Strategies
- Monitor Ollama process health and implement alerting on unexpected terminations
- Analyze application logs for Go panic messages related to boundary errors or invalid memory access
- Implement network-level inspection for GGUF files with anomalous header values
- Deploy SentinelOne Singularity to detect and alert on process crash patterns indicative of exploitation attempts
Monitoring Recommendations
- Enable verbose logging in Ollama to capture detailed error information during GGUF parsing
- Set up automated restart mechanisms with crash counting to detect repeated exploitation attempts
- Monitor for rapid successive crashes which may indicate an active denial of service attack
- Implement API rate limiting to reduce the impact of sustained exploitation attempts
How to Mitigate CVE-2025-66959
Immediate Actions Required
- Restrict network access to Ollama instances to trusted sources only
- Implement input validation at the network perimeter to filter potentially malicious GGUF files
- Deploy Ollama behind a reverse proxy with request size limits and content inspection
- Monitor the GitHub Issue #9820 for patch availability and updates
Patch Information
As of the last update, users should monitor the official Ollama GitHub repository for security patches addressing this vulnerability. Check the GitHub Issue #9820 for the latest information on fixes and updated releases. Upgrade to the patched version as soon as it becomes available.
Workarounds
- Limit Ollama API access to localhost or trusted internal networks only
- Implement a pre-validation layer that inspects GGUF files for header integrity before passing them to Ollama
- Use containerization with automatic restart policies to minimize service downtime during crashes
- Consider deploying Ollama behind an application firewall that can inspect and sanitize model file uploads
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
