PatchTheCVECVE-2026-71486: vLLM is an inference and serving engine for large language models
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Routine
MEDIUM 4.3UPGRADE
CVE-2026-71486 is a medium-severity flaw (CVSS 4.3) affecting vllm. Below: why it ranks where it does, which versions are affected, what to do, and the exact commands.
vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the /v1/completions/derender and /v1/chat/completions/derender endpoints accept caller-supplied GenerateResponse objects whose generate_responses, choices, token_ids, prompt_logprobs, logprobs.content, top_logprobs, and routed_experts structures are processed by OnlineDerenderer and tokenizer.decode before max_model_len, max_tokens, max_num_seqs, or response-size
Why this priority
- Public exploit reference
A fixed release or distro patch exists.
Publisher (CNA): GitHub, Inc. · Vendor: vllm
Am I affected?
Timeline
- Published CVE record published
- Updated Record last modified
vllm vllm UPGRADE
Affected: < 0.26.0
Fixed in: 0.26.0
Safe to downgrade to: no unaffected older release
1. Check installed version
apt-cache policy vllm || rpm -q vllm || vllm --version
2. UPGRADE to 0.26.0 (Debian / Ubuntu)
sudo apt update && sudo apt install --only-upgrade vllm
2. UPGRADE (RHEL / Amazon Linux / Fedora)
sudo dnf upgrade vllm
Note
Distros often backport fixes without changing the version number. Confirm with the distro advisory or changelog.
Verify at the official source NVD · CVE Program · Report a problem with this page
Scores and fixes here are computed automatically from those and other public sources. Confirm with the vendor advisory before changing production systems.
Scores and fixes here are computed automatically from those and other public sources. Confirm with the vendor advisory before changing production systems.
Related CVEs
- CVE-2026-56340 HIGH 8.8 UPGRADE vLLM versions >= 0.10.2 and < 0.13.0 are missing sparse tensor validation in multimodal embeddings processing
- CVE-2026-73560 MEDIUM 6.5 UPGRADE vLLM is an inference and serving engine for large language models