What is the CVSS score of CVE-2025-25183?

CVE-2025-25183 has a CVSS 3.1 base score of 2.6 (Low). CVSS vector: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:L/A:N. EPSS exploitation probability: 0.3%.

Which versions of Python are affected by CVE-2025-25183?

CVE-2025-25183 is a low-severity vulnerability in vLLM (CVSS 2.6). The limited severity suggests constrained impact, typically requiring specific conditions or local access for exploitation.. A patch is available.

How to fix or mitigate CVE-2025-25183?

During next maintenance window: Apply vendor patches when convenient. Monitor vendor channels for updates.

Python CVE-2025-25183

LOW

Improper Validation of Integrity Check Value (CWE-354)

2025-02-07 security-advisories@github.com

Python Information Disclosure Vllm

2.6

CVSS 3.1 · GitHub Advisory

Severity by source

GitHub Advisory PRIMARY

2.6 LOW

AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:L/A:N

Primary rating from GitHub Advisory · only source for this CVE.

CVSS VectorGitHub Advisory

CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:L/A:N

Attack Vector

Network

Attack Complexity

High

Privileges Required

Low

User Interaction

Required

Scope

Unchanged

Confidentiality

None

Integrity

Low

Availability

None

Lifecycle Timeline

Patch released

Mar 31, 2026 - 21:13 nvd

Patch available

Analysis Generated

Mar 28, 2026 - 18:25 vuln.today

CVE Published

Feb 07, 2025 - 20:15 nvd

LOW 2.6

Blast Radius

ecosystem impact

† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth

3 pypi packages depend on vllm (3 direct, 0 indirect)

Ecosystem-wide dependent count for version 0.7.2.

DescriptionGitHub Advisory

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try exploit hash collisions. The impact of a collision would be using cache that was generated using different content. Given knowledge of prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use. This issue has been addressed in version 0.7.2 and all users are advised to upgrade. There are no known workarounds for this vulnerability.

AnalysisAI

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated low severity (CVSS 2.6), this vulnerability is remotely exploitable. No vendor patch available.

Technical ContextAI

This vulnerability is classified under CWE-354. vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try exploit hash collisions. The impact of a collision would be using cache that was generated using different content. Given knowledge of prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use. This issue has been addressed in version 0.7.2 and all users are advised to upgrade. There are no known workarounds for this vulnerability. Affected products include: Vllm. Version information: version 0.7.2.

RemediationAI

No vendor patch is available at time of analysis. Monitor vendor advisories for updates. Apply vendor patches when available. Implement network segmentation and monitoring as interim mitigations.