CVE-2026-12491: vLLM · Misinterpretation of Input
Affects vLLMSource CVE Database V5
Not known to be exploited · CVSS 4.8 Medium : upgrade to 0.24.0 when convenient.
Data as of 30 Oct 2026, 20:25 UTC
- EPSS
- 0.2% chance of exploitation in 30 days – higher than 15% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
- Patch
- Available – fixed in 0.24.0
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:LExploitation evidence
Disclosed: evidence found.
CVE published 17 Jun 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponized: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 30 Oct 2026.
What to do
Upgrade vLLM and vllm to 0.24.0.
Affected software
| Product | Affected | Fixed in |
|---|---|---|
| vLLMvllm | Affected
| Fixed in
|
GitHubvllmpkg:github/vllmOther threats | Affected
| Fixed in
|
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
Description
A flaw was found in vLLM, an open-source library for large language model inference. This vulnerability arises from improper handling of image metadata, specifically EXIF orientation and PNG transparency (tRNS) data, during image processing. When images are converted to RGB, transparency information may be implicitly discarded or remapped, leading to unexpected rendering of transparent pixels and distortion of input content. This can result in the model misinterpreting image content, potentially affecting the integrity of processed data.