CVE-2026-26282 (GCVE-0-2026-26282)
Vulnerability from cvelistv5 – Published: 2026-02-19 20:41 – Updated: 2026-02-20 20:02
VLAI
EPSS
VEX
Title
NanaZip has DotNet Single file OOB Heap Read
Summary
NanaZip is an open source file archive Starting in version 5.0.1252.0 and prior to version 6.0.1630.0, NanaZip has an out-of-bounds heap read in `.NET Single File` bundle header parser due to missing bounds check. Opening a crafted file with NanaZip causes a crash or leaks heap data to the user. Version 6.0.1630.0 patches the issue.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-02-20 20:02 UTC
CWE
- CWE-126 - Buffer Over-read
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/M2Team/NanaZip/security/adviso… | x_refsource_CONFIRM |
| https://github.com/user-attachments/files/2527414… | x_refsource_MISC |
Impacted products
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
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They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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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.