CVE-2025-25183 is a low-severity vulnerability affecting vllm, a high-throughput and memory-efficient inference and serving engine for large language models (LLMs). This vulnerability allows maliciously constructed statements to lead to hash collisions, resulting in cache reuse. Such cache reuse can interfere with subsequent responses, causing unintended behavior. Given the predictable nature of Python's built-in hash() function, particularly as of Python 3.12, attackers could exploit this behavior to craft prompts that collide, thereby manipulating the cache.
The impact of a collision could result in the use of cached data generated from different content. This opens the door for attackers who are aware of the prompts in use to populate the cache with colliding prompts, leading to potentially malicious consequences. Organizations using vllm should be aware that there are no known workarounds for this vulnerability, making it essential to upgrade to version 0.7.2 to mitigate this risk.
The vulnerability has been classified with a CVSS score of 2.6, which indicates a low severity. However, the nature of the attack vector being network-based, combined with the high attack complexity and required user interaction, means that organizations should still take appropriate measures to address this vulnerability promptly.
Organizations should prioritize patching immediately to reduce the risk of exploitation and ensure the reliability of their systems.
Vulnerability Details
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. This vulnerability allows for hash collisions resulting in cache reuse which can interfere with responses and lead to unintended behavior. It is essential that users upgrade to version 0.7.2 as there are no known workarounds.
The CVSS score for this vulnerability is 2.6, indicating a low severity level. The attack vector is network-based, and it requires low privileges and user interaction. The integrity impact is classified as low, while confidentiality and availability impacts are noted as none.
Technical Analysis
The root cause of this vulnerability lies in the changes made to Python's built-in hash() function as of version 3.12. The behavior of hash(None) now results in a predictable constant value, making it feasible for malicious actors to exploit hash collisions. This results in cache reuse, as the same hash can be generated for multiple different inputs.
The attack vector is network-based, indicating that an attacker can exploit this vulnerability remotely. The attack complexity is high, as it requires knowledge of the prompts that are in use, and user interaction is required to trigger the vulnerability. The confidentiality impact is none, while the integrity impact is low, as cached data could potentially be manipulated. The availability impact is also none.
Risk & Impact Analysis
Organizations utilizing vllm should be aware of the potential risks associated with this vulnerability, particularly in environments where LLMs are deployed for critical applications. The ability for an attacker to manipulate cached responses can lead to significant unintended consequences, especially if an attacker has knowledge of the prompts in use.
The urgency of addressing this vulnerability is moderate, given its low CVSS score and the complexity required for exploitation. However, organizations should still prioritize patching to avoid potential exploitation and ensure the integrity of their applications.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
All versions of vllm prior to version 0.7.2 are affected by this vulnerability. Users are strongly encouraged to upgrade to this version to mitigate the risk associated with hash collisions.
Mitigation & Remediation
To remediate this vulnerability, organizations should upgrade to version 0.7.2 of vllm. If immediate upgrading is not possible, organizations should implement monitoring and logging practices to detect any unusual behavior in applications using vllm.
For further security measures, organizations can consider penetration testing to identify potential weaknesses in their systems.
Detection Guidance
Organizations should monitor logs for any anomalous behavior that may indicate exploitation attempts. Behavioral anomalies or unexpected cache hits should be investigated promptly. Additionally, network signatures that indicate unusual cache utilization patterns may be useful for early detection.
AppSecure Threat Intelligence Insight
The long-term significance of this vulnerability lies in its representation of the risks associated with predictable hashing behaviors in programming languages. With the increasing complexity of applications relying on AI and machine learning technologies, understanding the implications of such vulnerabilities is critical.
This vulnerability serves as a reminder for security teams to regularly review and update their software dependencies, especially those that incorporate third-party libraries. Regular security assessments, including penetration testing methodologies, can help in identifying and mitigating such risks before they can be exploited.
As organizations increasingly adopt AI technologies, they must also be aware of the security implications these technologies entail. Understanding vulnerabilities like CVE-2025-25183 can guide organizations in implementing more robust security practices.
To further enhance your understanding of security best practices, consider exploring additional resources on vulnerability management programs and how they can be effectively designed to safeguard against such vulnerabilities.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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