CVE-2026-14191 (GCVE-0-2026-14191)

Vulnerability from cvelistv5 – Published: 2026-07-01 02:41 – Updated: 2026-07-09 04:47
VLAI
Title
WinRAR / UnRAR RAR5 recovery-volume (.rev) out-of-bounds heap write in RecVolumes5::ReadHeader
Summary
An out-of-bounds heap write exists in the RAR5 recovery-volume (.rev) parser in WinRAR and UnRAR (RecVolumes5::ReadHeader in recvol5.cpp). The RecItems vector is sized only when the first .rev file in a set is processed; subsequent .rev files supply an independent RecNum value that is validated against that file's own TotalCount field but never against the actual size of RecItems. A crafted set of two or more .rev files can therefore write an attacker-controlled 32-bit value (the header's RevCRC field) to RecItems[RecNum] at an attacker-controlled offset up to 65534 * sizeof(RecVolItem) bytes past the allocation, corrupting adjacent heap objects. Triggering requires the victim to run a recovery/test operation on an attacker-supplied .rev set (for example 'unrar t x.part1.rev', WinRAR 'Repair archive', or auto-recovery when extracting a volume set with a missing .rar part). This is the RAR5-path sibling of CVE-2023-40477 (which was fixed in the RAR3 path only in WinRAR 6.23). Fixed in WinRAR / RAR 7.23.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-01 13:07 UTC
CWE
  • CWE-787 - Out-of-bounds Write
  • CWE-129 - Improper Validation of Array Index
Impacted products
Vendor Product Version CPE status
RARLAB WinRAR Affected: 0 , < 7.23 (custom)
guessed Create a notification for this product.
RARLAB RAR Affected: 0 , < 7.23 (custom)
guessed Create a notification for this product.
RARLAB UnRAR Affected: 0 , ≤ 7.21 (custom)
guessed Create a notification for this product.
RARLAB UnRAR.dll Affected: 0 , < 7.23 (custom)
guessed Create a notification for this product.
Show details on NVD website

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}



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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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