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CVE-2026-20884 (GCVE-0-2026-20884)

Vulnerability from cvelistv5 – Published: 2026-04-07 13:49 – Updated: 2026-04-13 13:04
VLAI
Summary
An integer overflow vulnerability exists in the deflate_dng_load_raw functionality of LibRaw Commit 8dc68e2. A specially crafted malicious file can lead to a heap buffer overflow. An attacker can provide a malicious file to trigger this vulnerability.
Severity
8.1 (High)
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-08 03:55 UTC
CWE
  • CWE-190 - Integer Overflow or Wraparound
Assigner
References
Impacted products
Vendor Product Version CPE status
LibRaw LibRaw Affected: Commit 8dc68e2
guessed Create a notification for this product.
Credits
Discovered by Francesco Benvenuto of Cisco Talos.
Show details on NVD website
To clipboard 

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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

  • 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.


Detection rules are retrieved from Rulezet.


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.