CVE-2026-111212 (GCVE-0-2026-111212)

Vulnerability from cvelistv5 – Published: 2026-10-16 00:45 – Updated: 2026-10-16 16:28
VLAI
Title
ImprintLab Medical-SAM-Adapter AutoBackend Model Loading autobackend.py session.get_modelmeta.custom_metadata_map eval injection
Summary
A security flaw has been discovered in ImprintLab Medical-SAM-Adapter 0.1.0-alpha/0.1.0. Affected by this issue is the function session.get_modelmeta.custom_metadata_map of the file models/MobileSAMv2/ultralytics/nn/autobackend.py of the component AutoBackend Model Loading. The manipulation of the argument imgsz/names/kpt_shape results in improper neutralization of directives in dynamically evaluated code. The attack requires a local approach. The exploit has been released to the public and may be used for attacks. The project was informed of the problem early through an issue report but has not responded yet.
SSVC
Exploitation: poc Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-16 16:26 UTC
CWE
  • CWE-95 - Improper Neutralization of Directives in Dynamically Evaluated Code
  • CWE-94 - Code Injection
Assigner
VulDB CNA under the mitre root
CNA scorecard B 82/100 over 3979 records in the last 180 days details
References
Impacted products
Vendor Product Version CPE status
ImprintLab Medical-SAM-Adapter Affected: 0.1.0
Affected: 0.1.0-alpha
    cpe:2.3:a:imprintlab:medical-sam-adapter:*:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

JSON
Events included in the timeline

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    Related by attack behaviour

    Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.