Cyber Resilience

CVE-2026-12491

Published
17 June 2026
Modified
07 July 2026
CVSS Score v3.1 4.8
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:L
EPSS Score 0.0024 15th percentile
Risk Priority 38 floored blend · peak EPSS

Summary

CVE-2026-12491 is a medium-severity Misinterpretation of Input (CWE-115) vulnerability. Its CVSS base score is 4.8 (Medium).

Operationally, exploitation aligns with the MITRE ATT&CK technique Masquerading (T1036); ranked at the 15th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.

This vulnerability is AI-related — categorised as LLM Application Platforms.

The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) — see the control section below for these in your framework.

OWASP Top 10 for Web (2025)

A05:2025 Injection

EU & UK References

Vulnerability Data

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,…

more

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.

CWE(s)
CWE-115

AI Security AnalysisAI

AI Category
LLM Application Platforms
Risk Domain
N/A
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Matched keywords: large language model

Related Threats

MITRE ATT&CK Enterprise Techniques

T1036 Masquerading Stealth
Adversaries may attempt to manipulate features of their artifacts to make them appear legitimate or benign to users and/or security tools.
attack.mitre.org →
T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
attack.mitre.org →
T1204 User Execution Execution
An adversary may rely upon specific actions by a user in order to gain execution.
attack.mitre.org →
T1221 Template Injection Stealth
Adversaries may create or modify references in user document templates to conceal malicious code or force authentication attempts.
attack.mitre.org →
T1659 Content Injection Initial Access
Adversaries may gain access and continuously communicate with victims by injecting malicious content into systems through online network traffic.
attack.mitre.org →
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2024-11169Shared CWE-115
CVE-2023-0880Shared CWE-115
CVE-2025-22870Shared CWE-115
CVE-2023-32228Shared CWE-115
CVE-2025-5747Shared CWE-115
CVE-2025-54584Shared CWE-115
CVE-2026-42004Shared CWE-115
CVE-2025-5826Shared CWE-115
CVE-2023-32260Shared CWE-115
CVE-2025-32908Shared CWE-115

Affected Assets

Mitigating Controls

Mitigating Controls (NIST 800-53 r5) AI

SI-10 Information Input Validation good match

Input validation directly stops the system from accepting and acting on misinterpreted data values.

Mitigating Controls (NIST CSF 2.0) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.

PR.PS-06 mostly match
prevents

Secure SDLC practices directly require correct input parsing/validation to avoid misinterpretation flaws.

Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.

A.8.29 Security testing in development and acceptance mostly match
finds

Security testing can detect input misinterpretation flaws but does not itself implement the preventive controls.

A.8.25 Secure development life cycle partial match
prevents

Secure development lifecycle mandates input validation and canonicalization that directly prevent misinterpretation of inputs.

A.8.26 Application security requirements partial match
prevents

Application security requirements explicitly call for robust input handling and sanitization to avoid misinterpretation.

A.8.27 Secure system architecture and engineering principles partial match
prevents

Secure architecture principles include defensive input processing and error handling that reduce misinterpretation risks.

A.8.28 Secure coding partial match
prevents

Secure coding standards require strict input validation and canonical forms to eliminate misinterpretation vulnerabilities.

References