CVE-2026-48111 (GCVE-0-2026-48111)
Vulnerability from cvelistv5 – Published: 2026-06-05 16:09 – Updated: 2026-06-08 17:19
VLAI
EPSS
VEX
Title
GHSL-2026-121 7-Zip UEFI DEPEX OOB Read
Summary
7-Zip is a file archiver with a high compression ratio. Versions 9.21 through 26.00 contain an off-by-one out-of-bounds read vulnerability in the ParseDepedencyExpression function of the UEFI firmware image parser(CPP/7zip/Archive/UefiHandler.cpp). The function validates an attacker-controlled opcode byte using > instead of >= against the element count of the 10-entry kExpressionCommands static array, allowing an opcode value of 10 to read one pointer slot (8 bytes on x64) past the end of the array in .rodata. The out-of-bounds value is then dereferenced as a const char * and passed through strlen and memcpy into the archive's Characts property, which may cause either a denial of service (access violation when the adjacent bytes do not form a valid readable pointer) or a minor information disclosure of an adjacent .rdata string literal into archive metadata. The vulnerability is reached automatically during IInArchive::Open() via the call path OpenFv/OpenCapsule → ParseVolume → ParseSections when processing a SECTION_DXE_DEPEX (0x13) or SECTION_PEI_DEPEX (0x1B) section whose first body byte is 0x0A, and the UEFI handler is enabled by default in stock 7z.dll with signature-based detection for both UEFIc and UEFIf formats. The outcome (crash vs. silent leak) is deterministic per build but linker-layout dependent, with no write primitive and no disclosure of heap data, secrets, or ASLR base addresses. Version 26.01 fixes the issue.
Severity
4.3 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-08 17:19 UTC
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://securitylab.github.com/advisories/GHSL-20… | x_refsource_CONFIRM |
Impacted products
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- 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.
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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
model, served locally by ML-Gateway.
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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
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