This vulnerability allows a memory corruption issue in vllm versions 0.10.2 to before 0.11.1. The vulnerability exists in the Completions API endpoint, which processes user-supplied prompt embeddings. It loads serialized tensors using torch.load() without sufficient validation, exposing the application to potential risks.
The CVSS score of this vulnerability is 8.8, categorizing it as high severity. This high rating indicates that organizations face significant risks, including possible denial-of-service (DoS) and remote code execution (RCE) attacks. The urgency for defenders is critical, as the vulnerability can lead to serious security breaches.
Currently, there are no known exploits publicly available for this vulnerability, but the potential for exploitation remains high given its nature. Organizations should prioritize patching immediately to mitigate risk.
The vllm team has released a patch in version 0.11.1, which addresses this vulnerability. Organizations using affected versions must upgrade to the patched version to secure their systems.
Vulnerability Details
vLLM is an inference and serving engine for large language models (LLMs). The memory corruption vulnerability allows for a crash and potentially remote code execution. The issue arises from the Completions API endpoint, which handles user-supplied prompt embeddings through the loading of serialized tensors without adequate validation.
This vulnerability is characterized by several CWE identifiers, including CWE-20 (Improper Input Validation) and CWE-502 (Deserialization of Untrusted Data). The vulnerability was published on November 21, 2025, and has been assigned a CVSS v3.1 vector of AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H.
Technical Analysis
The vulnerability is due to a change in PyTorch 2.8.0, which disables sparse tensor integrity checks by default. This allows maliciously crafted tensors to bypass internal bounds checks and trigger an out-of-bounds memory write during the call to to_dense().
The attack vector for this vulnerability is network-based, which means attackers can exploit it remotely without physical access to the server running vLLM. The attack complexity is low and requires low privileges, making it easier for attackers to leverage this vulnerability.
The potential impacts of this vulnerability are significant, with high confidentiality, integrity, and availability impacts. Organizations must ensure that they take immediate steps to remediate this vulnerability.
Risk & Impact Analysis
Risk to organizations includes potential denial-of-service attacks and remote code execution, which could compromise the integrity of the hosting server and lead to unauthorized access to sensitive data. The blast radius could affect not only the vLLM instance but also any applications that rely on it.
Given the CVSS score of 8.8, organizations should address this vulnerability in their priority patch cycle. The ongoing risk of exploitation necessitates immediate action to ensure systems remain secure.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The affected versions include all versions from 0.10.2 to before 0.11.1. Organizations are encouraged to upgrade to version 0.11.1, which contains the necessary patch to mitigate this vulnerability.
Mitigation & Remediation
To mitigate this vulnerability, organizations should upgrade to vllm version 0.11.1 or later. If immediate upgrading is not feasible, organizations must evaluate potential workarounds and implement temporary measures to limit exposure.
Additionally, organizations should consider implementing security controls such as network segmentation and monitoring to help detect any attempts to exploit this vulnerability. For further guidance, organizations can refer to the penetration testing services to validate their security posture.
Detection Guidance
Organizations should monitor logs for suspicious activity related to the Completions API endpoint. Behavioral anomalies may indicate attempts to exploit the memory corruption vulnerability. Additionally, network signatures related to abnormal tensor processing should be analyzed.
AppSecure Threat Intelligence Insight
The emergence of this vulnerability highlights the importance of maintaining up-to-date libraries and frameworks. Organizations must remain vigilant in applying security patches promptly to reduce their exposure to similar vulnerabilities.
This incident serves as a reminder to conduct regular security assessments. Organizations are encouraged to implement a robust vulnerability management program to effectively identify and remediate security weaknesses.
As the landscape of attacks evolves, continuous security awareness and training for teams are essential. Emphasizing the need for penetration testing methodologies will empower teams to better understand and mitigate risks.
Lastly, organizations should leverage resources such as cloud penetration testing guides to further strengthen their security posture against evolving threats.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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