CVE-2026-71486
CVE-2026-71486 · vLLM: Derender endpoints decode caller-supplied GenerateResponse token IDs without output bounds
This page contains facts from public vulnerability sources. Check the original advisory before updating.
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 limits are enforced, allowing an authenticated API client to consume excessive CPU and memory and produce oversized responses. This issue is fixed in version 0.26.0.
CVSS 4.3 · Medium · EPSS 0.4% · Public exploit · Fix available
- Published
- Updated
Stack: python
Affected products and versions
vllm
- Versions
- < 0.26.0: Affected
No fix published
vllm PyPI
- Versions
- 0.0.1
- 0.1.0
- 0.1.1
- 0.1.2
- 0.1.3
- 0.1.4
- 0.1.5
- 0.1.6
- 0.1.7
- 0.10.0
- 0.10.1
- 0.10.1.1
- 0.10.2
- 0.11.0
- 0.11.1
- 0.11.2
- 0.12.0
- 0.13.0
- 0.14.0
- 0.14.1
- 0.15.0
- 0.15.1
- 0.16.0
- 0.17.0
- 0.17.1
- 0.18.0
- 0.18.1
- 0.19.0
- 0.19.1
- 0.2.0
- 0.2.1
- 0.2.1.post1
- 0.2.2
- 0.2.3
- 0.2.4
- 0.2.5
- 0.2.6
- 0.2.7
- 0.20.0
- 0.20.1
- 0.20.2
- 0.21.0
- 0.22.0
- 0.22.1
- 0.23.0
- 0.24.0
- 0.25.0
- 0.25.1
- 0.3.0
- 0.3.1
- 0.3.2
- 0.3.3
- 0.4.0
- 0.4.0.post1
- 0.4.1
- 0.4.2
- 0.4.3
- 0.5.0
- 0.5.0.post1
- 0.5.1
- 0.5.2
- 0.5.3
- 0.5.3.post1
- 0.5.4
- 0.5.5
- 0.6.0
- 0.6.1
- 0.6.1.post1
- 0.6.1.post2
- 0.6.2
- 0.6.3
- 0.6.3.post1
- 0.6.4
- 0.6.4.post1
- 0.6.5
- 0.6.6
- 0.6.6.post1
- 0.7.0
- 0.7.1
- 0.7.2
- 0.7.3
- 0.8.0
- 0.8.1
- 0.8.2
- 0.8.3
- 0.8.4
- 0.8.5
- 0.8.5.post1
- 0.9.0
- 0.9.0.1
- 0.9.1
- 0.9.2
- Affected ranges
- ECOSYSTEM: < 0.26.0
Update to 0.26.0
Original sources
- https://github.com/vllm-project/vllm/security/advisories/GHSA-8737-qx52-hjff
- https://github.com/vllm-project/vllm/pull/47260
- https://github.com/vllm-project/vllm/commit/8e61b646e2d157f9b93451fa048f9c8530c8a67b
- https://github.com/vllm-project/vllm/releases/tag/v0.26.0
- https://nvd.nist.gov/vuln/detail/CVE-2026-71486