CVE-2026-54235

mediumPriorität · verfolgen · 26EPSS 0.004 · 39. PerzentilDEEUUS
grimoire priorityP3 · Track
Exploitable?
—
no known exploitation
Likely / severe?
0.45% (39th) EPSS
CVSS 6.5 · medium
Is it mine?
6 products
Prüfe deine Angriffsfläche
Who says so?
5 sources
cnnvd · enisa_euvd · osv · cert_bund

EPSS · bewertet 2026-10-27

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.

01

cvss

Technischer Schweregrad, 0–10. Eine Zeile je Quelle.

6.9
010

Basiswert 0–10 · höher = schwerwiegender · Vektor überfahren zum Auflösen⚠ Quellen uneinig · low–medium · 2.7–6.9

v3.16.5mediumCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:Lprimary · NVD / NIST USAusnutzbarkeit 3.9 · Auswirkung 2.5
v3.16.5mediumCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:Lsecondary · OSV
v4.06.9mediumCVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:Nsecondary · ENISA EU
v4.06.9mediumCVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:Xsecondary · security-advisories@github.com
v4.02.7lowCVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:U/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:Xsecondary · CVSS-BT (t0sche)
02

SSVC

Eine Handlungsentscheidung — aus Ausnutzung und Auswirkung.

Exploitation: none · Automatable: yes · Technical Impact: partialcoordinatorcisa_ssvcUS
03

schwächen

Die CWE-Schwächeklassen, zu denen dies gehört.

04

betroffene Produkte (6)

Betroffene Produkte und Versionsbereiche.

05

Advisories (3)

Hersteller- und Ökosystem-Meldungen dazu.

  • WID-SEC-W-2026-1897vllm: Mehrere SchwachstellenDeutsch2026-06-11csaf · cert_bundDE

    Ein Angreifer kann mehrere Schwachstellen in vllm ausnutzen, um Sicherheitsmaßnahmen zu umgehen, einen Denial-of-Service-Zustand zu verursachen, Daten zu manipulieren oder vertrauliche Informationen offenzulegen.

  • GHSA-7h4p-rffg-7823vLLM: temperature=NaN and temperature=Infinity bypass validation and propagate to GPU kernels2026-06-17ghsa · osvUS

    ## Summary All 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. Note: `-Infinity` is correctly caught. ## Root Cause `sampling_params.py:384`: ```python if 0 < self.temperature < _MAX_TEMP: # NaN → False; +Inf → False ``` `sampling_params.py:462`: ```python if self.temperature < 0.0: # NaN → False; +Inf → False raise VLLMValidationError(...) ``` No `math.isnan()` or `math.isinf()` check exists anywhere in `sampling_params.py`. Python semantics (verified): `float('nan') < 0.0` → `False`, `float('inf') < 0.0` → `False`. ## Impact Crash of inference worker on GPU kernel execution with NaN/Inf softmax input, degrading service for all concurrent users. ## Remediation Add `math.isfinite(self.temperature)` check in `_verify_args()`. Reject non-finite float values with a 400 error. ## Fix A fix for this vulnerability was merged here: https://github.com/vllm-project/vllm/pull/45116

  • PYSEC-2026-3405vLLM: temperature=NaN and temperature=Infinity bypass validation and propagate to GPU kernels2026-07-13vendor · osvUS

    ## Summary All 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. Note: `-Infinity` is correctly caught. ## Root Cause `sampling_params.py:384`: ```python if 0 < self.temperature < _MAX_TEMP: # NaN → False; +Inf → False ``` `sampling_params.py:462`: ```python if self.temperature < 0.0: # NaN → False; +Inf → False raise VLLMValidationError(...) ``` No `math.isnan()` or `math.isinf()` check exists anywhere in `sampling_params.py`. Python semantics (verified): `float('nan') < 0.0` → `False`, `float('inf') < 0.0` → `False`. ## Impact Crash of inference worker on GPU kernel execution with NaN/Inf softmax input, degrading service for all concurrent users. ## Remediation Add `math.isfinite(self.temperature)` check in `_verify_args()`. Reject non-finite float values with a 400 error. ## Fix A fix for this vulnerability was merged here: https://github.com/vllm-project/vllm/pull/45116

06

auch bekannt als (6)

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07

quellen

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veröffentlicht 2026-06-22geändert 2026-10-25
08

Referenzen (9)

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