gcdRadar
archive/Patch now/AIR-2026-1114
closedclosed 17 Jun 2026Patch now2 Disclosed vulnerabilityAI

CVE-2026-12491: vLLM flawed image metadata handling leads to input distortion

vLLM

An input handling vulnerability exists in the vLLM inference library regarding EXIF and transparency metadata. Improper processing can distort input content and cause the model to misinterpret images.

disclosed CVENo confirmed exploitation, so this record is tracked as a disclosed vulnerability and not counted as an incident. It moves into the counts if CISA or the vendor confirms exploitation. Why

CVSS
4.8
EPSS
<0.01 · top 85% · 2026-11-13
fix status
Fix status unknown
first report
17 Jun 2026
last update
17 Jun 2026

Timeline · 1

  1. 17 Jun 2026CVE-2026-12491: A flaw was found in vLLM, an open-source library for large language model inference.

Sources · 1

Advisories and databases

  1. 2026-06-17

Titles and links belong to their publishers. The summary above is written by gcdRadar in its own words; data is CC BY-SA 4.0.

Classification

AI role
Attack on AI
categories
Infrastructure vulnerability
severity
2 · Disclosed vulnerability
fix status
Fix status unknown
targets
Framework / tooling
owasp llm top 10
LLM03:2025 Supply Chain
mitre atlas
no technique for these categories
dates
happened 17 Jun 2026 · first reported 17 Jun 2026
origin
NVD
Vulnerability record from NVD / the GitHub Advisory Database. This product uses the NVD API but is not endorsed or certified by the NVD.
record
confidence 0.95 · created 2026-10-18 10:04Z · updated 2026-10-18 10:04Z

Lane, AI role, categories and severity come from the classifier; the OWASP and ATLAS rows are derived from the categories. How these are assigned

Copy a brief

This record as a short Markdown brief, for a ticket, a chat or a coding agent.

the brief as text
# CVE-2026-12491: vLLM flawed image metadata handling leads to input distortion

https://gcdradar.vercel.app/incidents/cve-2026-12491-vllm-flawed-image-metadata-handling-leads-to · AIR-2026-1114 · closed 2026-06-17

- Lane: Patch now
- Severity: 2 (Disclosed vulnerability)
- Fix status: Fix status unknown
- First reported: 2026-06-17
- AI role: Attack on AI
- Products: vLLM
- CVE ids: CVE-2026-12491
- CVSS: 4.8

## Summary

An input handling vulnerability exists in the vLLM inference library regarding EXIF and transparency metadata. Improper processing can distort input content and cause the model to misinterpret images.

## What to do

No action recorded; read the sources below and the vendor's advisory.

## Latest updates

- 2026-06-17: CVE-2026-12491: A flaw was found in vLLM, an open-source library for large language model inference. (https://nvd.nist.gov/vuln/detail/CVE-2026-12491)

## Sources

- 2026-06-17 NVD: CVE-2026-12491: A flaw was found in vLLM, an open-source library for large language model inference. https://nvd.nist.gov/vuln/detail/CVE-2026-12491

Verify against the vendor's advisory before changing production. Data CC BY-SA 4.0, https://gcdradar.vercel.app.

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