The vulnerability identified as CVE-2025-24357 affects the vllm library, which is designed for large language model (LLM) inference and serving. It is crucial to recognize the potential severity of this vulnerability, which has been assigned a CVSS score of 7.5, indicating a high severity level. This score reflects the significant risks associated with the vulnerability, particularly considering its potential for arbitrary code execution.
Exploitation of this vulnerability allows attackers to execute arbitrary code on the target system during the unpickling process of model checkpoints. The vulnerability arises from the implementation of the hf_model_weights_iterator function in the vllm/model_executor/weight_utils.py file, where the torch.load function is used with a default parameter setting that does not adequately mitigate risks associated with untrusted data.
The urgency of addressing this vulnerability is underscored by its exploitation potential, which is classified as high. Organizations utilizing the vllm library should take immediate action to update to version 0.7.0, where this issue has been resolved, to prevent any potential exploitation.
Organizations should prioritize patching immediately to mitigate the risk of this vulnerability. The nature of the vulnerability emphasizes the importance of securing dependencies and regularly assessing the security posture of utilized libraries.
Vulnerability Details
CVE-2025-24357 details a significant security risk associated with the vllm library. The official description states that the hf_model_weights_iterator function, which is part of the vllm/model_executor/weight_utils.py implementation, fails to properly handle untrusted pickle data, leading to arbitrary code execution when malicious data is processed.
The vulnerability has a CVSS score of 7.5, indicating a high level of severity. This score is derived from the following CVSS vector: CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H, which highlights the network attack vector, high attack complexity, and the requirement for user interaction.
The affected product is the vllm library, with all versions prior to 0.7.0 being vulnerable. The vulnerability has been classified under CWE-502, which pertains to deserialization of untrusted data.
The vulnerability was published on January 27, 2025, and is classified as analyzed. Organizations using the vllm library should take note of the patch available in version 0.7.0 to ensure their systems are protected against this vulnerability.
Technical Analysis
The root cause of CVE-2025-24357 lies in the improper handling of untrusted data during the model checkpoint loading process. The vllm library, while designed for LLM operations, utilizes the torch.load function, which, by default, allows arbitrary code execution if malicious pickle data is provided.
The attack vector is classified as network-based, meaning that an attacker can exploit this vulnerability remotely without physical access to the target system. The attack complexity is deemed high, requiring the attacker to craft malicious pickle data that would be accepted by the target system's model loading mechanisms.
The vulnerability does not require any privileges to exploit, as it can be executed by any user with the ability to trigger the model loading process. User interaction is required, as the victim must execute the loading process, potentially leading to a system compromise.
The impact of this vulnerability is significant, as it could lead to high confidentiality, integrity, and availability impacts. Attackers exploiting this vulnerability may gain unauthorized access to sensitive data, alter data integrity, or disrupt service availability.
Risk & Impact Analysis
Organizations that utilize the vllm library are at risk of significant operational impact if CVE-2025-24357 is exploited. The ability for attackers to execute arbitrary code poses a severe threat, potentially leading to unauthorized access to sensitive information, system alterations, and service disruptions.
The blast radius for this vulnerability is extensive, as it is applicable to any system employing the vulnerable versions of the vllm library. This includes services relying on LLM models, which may be integral to various applications and business processes.
Given the high CVSS score of 7.5, organizations should prioritize patching this vulnerability immediately. The risk of exploitation is compounded by the fact that user interaction is required, which may inadvertently expose systems to malicious data.
To protect against potential exploitation, organizations should implement stringent security measures, including regular updates of dependencies and rigorous security assessments of libraries used within their applications.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The vulnerable versions of the vllm library include all versions prior to 0.7.0. Organizations using these versions should take immediate action to upgrade to the latest version to mitigate the risk associated with CVE-2025-24357.
Mitigation & Remediation
To remediate CVE-2025-24357, organizations should update the vllm library to version 0.7.0 or later, where this vulnerability has been addressed. In addition to patching, organizations should consider implementing security best practices such as code reviews and dependency management to reduce the risk of similar vulnerabilities in the future.
Further measures include rigorous testing of all libraries used within applications and employing continuous security testing to monitor for vulnerabilities actively. For a comprehensive approach to security, organizations may explore services such as continuous penetration testing to identify weaknesses in their security posture.
Detection Guidance
Organizations should monitor their systems for any indicators of exploitation, including unusual activity related to model loading processes. Log analysis should focus on identifying any unexpected invocations of the torch.load function with untrusted data.
Behavioral anomalies may also signal potential exploitation attempts. Security teams should ensure that proper logging mechanisms are in place to capture relevant data for forensic analysis in case of an incident.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2025-24357 highlights an ongoing trend in software vulnerabilities related to deserialization processes. As libraries like vllm gain traction, understanding the implications of untrusted data handling becomes increasingly important.
This case serves as a reminder for security teams to prioritize thorough security assessments of third-party libraries and dependencies. Organizations should implement strategies to continually assess and improve their security posture, ensuring that they remain vigilant against similar threats.
For further reading on how to enhance security practices, organizations can refer to the vulnerability management program design and the importance of proactive security measures.
Additionally, organizations should stay informed about the latest trends in application security through resources like the penetration testing methodology guide to better understand the challenges and solutions in securing applications.
Finally, organizations should consider exploring the benefits of security testing best practices to ensure they have a comprehensive approach to cybersecurity.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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