A vulnerability in Ollama versions <=0.3.14 allows a malicious user to create a customized gguf model file that can be uploaded to the public Ollama server. When the server processes this malicious model, it crashes, leading to a Denial of Service (DoS) attack. The root cause of the issue is an out-of-bounds read in the gguf.go file. With a CVSS score of 7.5, this vulnerability is classified as high severity, indicating a significant risk to organizations relying on this technology.
Risk to organizations includes potential downtime and service disruption. The vulnerability can be exploited without requiring user interaction, and importantly, it does not necessitate any privileges, making it accessible to any attacker. Organizations should prioritize patching immediately to prevent potential exploitation.
As of now, there are no known public exploits available for this vulnerability, but the exploitability is deemed high. This emphasizes the importance of timely remediation, as the lack of an active exploit does not mitigate the risk associated with the vulnerability.
Organizations using Ollama should assess their environments and ensure they are running a version that is patched against this vulnerability. As it is not high-profile, proactive measures are essential to avoid potential service failures.
Vulnerability Details
The vulnerability allows a malicious user to create a customized gguf model file for upload to the public Ollama server. Upon processing this malicious model, the server crashes, resulting in a Denial of Service (DoS) attack. This is classified under CWE-125, which pertains to out-of-bounds read vulnerabilities. The severity of this vulnerability is highlighted by its CVSS score of 7.5, indicating a high level of risk associated with potential exploitation.
Technical Analysis
The root cause of this vulnerability stems from an out-of-bounds read in the gguf.go file, which allows attackers to manipulate the server's behavior. This vulnerability is exploitable over a network, requiring low attack complexity and no privileges or user interaction. The impact is primarily on availability, as the server crashes upon processing the malicious model.
Risk & Impact Analysis
Organizations using Ollama are at risk of service disruptions due to this vulnerability. The potential blast radius is significant, as the denial of service can affect all users of the public server. Given the high exploitability score, organizations should assess their risk posture and prioritize remediation efforts. The urgency for addressing this vulnerability is high due to the potential availability impact and the nature of the attack vector.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
All versions prior to vendor patch (<=0.3.14) are affected. Organizations should ensure they upgrade to the patched version to mitigate risks associated with this vulnerability.
Mitigation & Remediation
To mitigate this vulnerability, organizations should apply the latest patches provided by Ollama. It is crucial to monitor the official website for updates and ensure that all instances of Ollama are upgraded to the fixed version. Additionally, organizations may consider implementing network controls to limit unauthorized access to the service. For more comprehensive security, organizations should engage in penetration testing to identify potential vulnerabilities in their configurations.
Detection Guidance
Organizations should monitor logs for any indicators of unusual activity related to model uploads. Behavioral anomalies, such as unexpected crashes or performance degradation of the Ollama service, should be investigated promptly. Detecting patterns of access that deviate from normal usage can help identify potential exploitation attempts.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2024-12055 lies in its potential to trigger broader Denial of Service attacks if not addressed. This vulnerability reflects a growing trend in software vulnerabilities related to model processing and AI applications, highlighting the need for rigorous validation of inputs. Security teams should be vigilant and proactive in identifying similar weaknesses in their systems. It is essential to implement strategic defensive measures to mitigate risks. For further insights, organizations can refer to the following resources: AI security best practices, penetration testing methodology, and vulnerability management programs to effectively manage similar risks.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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