CVE-2026-54235: vLLM: temperature=NaN and temperature=Infinity bypass validation and propagate to GPU kernels
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.
Published Jun 17, 2026 · Updated Jul 17, 2026
Affected packages
vllm
- Ecosystem
- PyPI
- Affected
- >= 0.8.5, <= 0.23.0
- Fixed in
- 0.24.0
- Check
pip show vllm
Advisory details
From the GitHub Advisory Database (CC BY 4.0).
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:
if 0 < self.temperature < _MAX_TEMP: # NaN → False; +Inf → Falsesampling_params.py:462:
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
Credited to brodmart, jperezdealgaba.
References
- https://github.com/vllm-project/vllm/security/advisories/GHSA-7h4p-rffg-7823
- https://github.com/vllm-project/vllm/pull/45116
- https://github.com/vllm-project/vllm/commit/d598d239737cfa37bcfcb98886ec3f3557fc7198
- https://nvd.nist.gov/vuln/detail/CVE-2026-54235
- https://github.com/advisories/GHSA-7h4p-rffg-7823
- https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2026-3405.yaml
- https://github.com/vllm-project/vllm
- https://pypi.org/project/vllm
How this page is made: the data panel comes from the GitHub Advisory Database, CISA’s KEV catalog, the NVD and FIRST EPSS, and updates twice a day. No analysis has been written for this entry yet. Methodology
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