CVE-2026-54235

vLLM: temperature=NaN and temperature=Infinity bypass validation and propagate to GPU kernels

Published Jun 22, 2026 CVSS 4.0 · 6.9 Medium EPSS < 1% KEV · Not listed

Description

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, ll temperature validation gates use comparison operators (<, >), which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagate to GPU sampling kernels, where they produce undefined behavior or CUDA errors that can crash the inference worker. This vulnerability is fixed in 0.23.1rc0.

Analysis

Technical impact

The flaw in vLLM’s temperature validation logic arises from the use of comparison operators that treat Python’s IEEE‑754 NaN and positive Infinity values as false, allowing them to pass every guard. When those invalid temperature values reach the GPU sampling kernels, they trigger undefined behavior or cause CUDA errors that crash the inference worker. This vulnerability, identified as CWE‑1287, does not compromise confidentiality or integrity but disrupts the availability of the inference service.

Affected Systems

The vLLM inference engine, released by the vllm‑project, is affected in all versions before 0.23.1rc0. Upgrading to 0.23.1rc0 or more recent releases removes the validation bug.

Risk and Exploitability

With a CVSS score of 6.9 the vulnerability is considered medium severity, and no EPSS score is provided. It is not listed in the CISA KEV catalog. Based on the description, the likely attack vector is remote; an attacker can submit a request to the public inference API with a temperature value of NaN or positive Infinity, and if the backend processes it, the worker will crash, leading to a denial‑of‑service. No code execution or data exfiltration is possible.

Affected Products

Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).

Vendor Product Default status Versions

vllm-project

vllm

affected

Version	Status	Constraints
`< 0.23.1rc0`	affected	—

Configuration 1 [-]

cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*

No data.

Vendor Product Versions

Vllm-project

Vllm

Version	Status	Scheme	Platform
`[0,0.23.1rc0)`	affected	semver	—

Remediation

No solution or workaround provided in the CVE record.

OpenCVE Recommended Actions

Upgrade to vLLM 0.23.1rc0 or newer to apply the validated fix.
If an immediate upgrade is infeasible, add a server‑side filter that explicitly rejects NaN or infinity values, for example by checking isfinite() before invoking the inference engine.
Establish monitoring that automatically restarts crashed inference workers to reduce downtime.

Generated by OpenCVE AI on June 23, 2026 at 00:20 UTC.

Tracking

Advisories

Source	ID	Title
Github GHSA	GHSA-7h4p-rffg-7823	vLLM: temperature=NaN and temperature=Infinity bypass validation and propagate to GPU kernels

Metrics

Attack Vector Network

Attack Complexity Low

Privileges Required None

Attack Requirements None

User Interaction None

Vulnerable System Confidentiality Impact None

Vulnerable System Integrity Impact None

Vulnerable System Availability Impact Low

Subsequent System Confidentiality Impact None

Subsequent System Integrity Impact None

Subsequent System Availability Impact None

Attack Vector Network

Attack Complexity Low

Privileges Required None

Scope Unchanged

Confidentiality Impact None

Integrity Impact Low

Availability Impact Low

User Interaction None

No CVSS v3.0

No CVSS v2

This CVE is not in the KEV list.

Exploit prediction score: < 1% (0.450% probability within 30 days)

Exploitation none

Automatable yes

Technical Impact partial

References

Link	Providers
https://github.com/vllm-project/vllm/commit/d598d239737cfa37bcfcb98886ec3f3557fc7198
https://github.com/vllm-project/vllm/pull/45116
https://github.com/vllm-project/vllm/security/advisories/GHSA-7h4p-rffg-7823
https://nvd.nist.gov/vuln/detail/CVE-2026-54235
https://www.cve.org/CVERecord?id=CVE-2026-54235

History

Jun 26, 2026 12:15 PM Added references. Scoring metrics updated View changes

Type	Values Removed	Values Added
References		https://nvd.nist.gov/vuln/detail/CVE-2026-54235 https://www.cve.org/CVERecord?id=CVE-2026-54235
Metrics	threat_severity `None`	cvssV3_1 `{'score': 6.5, 'vector': 'CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:L'}` threat_severity `Moderate`

Jun 23, 2026 1:30 PM Scoring metrics updated View changes

Type	Values Removed	Values Added
Metrics		ssvc `{'options': {'Automatable': 'yes', 'Exploitation': 'none', 'Technical Impact': 'partial'}, 'version': '2.0.3'}`

Jun 23, 2026 1:30 AM First appearance in the feed. Affected vendors updated View changes

Type	Values Removed	Values Added
First Time appeared		Vllm-project Vllm-project vllm
Vendors & Products		Vllm-project Vllm-project vllm

Jun 22, 2026 10:45 PM Description updated. Title updated. Weaknesses updated. Added references. Scoring metrics updated View changes

Type	Values Removed	Values Added
Description		vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, ll temperature validation gates use comparison operators (<, >), which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagate to GPU sampling kernels, where they produce undefined behavior or CUDA errors that can crash the inference worker. This vulnerability is fixed in 0.23.1rc0.
Title		vLLM: temperature=NaN and temperature=Infinity bypass validation and propagate to GPU kernels
Weaknesses		CWE-1287
References		https://github.com/vllm-project/vllm/commit/d598d239737cfa37bcfcb98886ec3f3557fc7198 https://github.com/vllm-project/vllm/pull/45116 https://github.com/vllm-project/vllm/security/advisories/GHSA-7h4p-rffg-7823
Metrics		cvssV4_0 `{'score': 6.9, 'vector': 'CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N'}`