Medium severity · published Aug 13, 2026
CVE-2026-73557
vLLM: Incomplete CVE-2025-62164 remediation can be bypassed by concurrent prompt parts
5.3
CVSS
0.2%
EPSS
No
On KEV
Description
vLLM is an inference and serving engine for large language models. From 0.20.2rc0 until 0.26.0, safe_load_prompt_embeds in vllm/renderers/embed_utils.py uses torch.sparse.check_sparse_tensor_invariants, whose process-global save, enable, and restore state can be raced by concurrent prompt_embeds parts submitted to POST /v1/chat/completions through AsyncMultiModalItemTracker.resolve_items, asyncio.gather, and the default executor, allowing an invalid sparse tensor to reach tensor.to_dense despite the CVE-2025-62164 guard when enable_prompt_embeds is enabled. This issue is fixed in version 0.26.0.
CVSS vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
Attack vector
Network
Complexity
Low
Privileges
None
User interaction
None
Confidentiality
None
Integrity
None
Availability
Low
Scope
Unchanged
Exploitation
Is it being used?
Exploit prediction (EPSS)0.2% · higher than 16% of CVEs
Exploited in the wildNo evidence yet
CISA KEVNot listed
Ransomware campaign useNot known
Affected products
Which versions
VL
vllm · vllm-project
>= 0.20.2rc0, < 0.26.0 · source: vendor, NVD
References
NVD
https://nvd.nist.gov/vuln/detail/CVE-2026-73557
CVE.org
https://www.cve.org/CVERecord?id=CVE-2026-73557
Patch
https://github.com/vllm-project/vllm/commit/793cf79c89d4049124e756915468ac30318f2e50
Issue Tracking
https://github.com/vllm-project/vllm/pull/48583
Release Notes
https://github.com/vllm-project/vllm/releases/tag/v0.26.0
Mitigation
https://github.com/vllm-project/vllm/security/advisories/GHSA-pr7f-p5mw-fc87
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