CVE-2026-88368 (GCVE-0-2026-88368)

Vulnerability from cvelistv5 – Published: 2026-09-24 00:00 – Updated: 2026-09-24 17:55
VLAI
Summary
NanoSVG commit 239e102ec contains an incorrect numeric conversion vulnerability in the rasterizer's nsvg__addActive() function. A specially crafted SVG document containing sufficiently large geometry coordinates can cause fixed-point-scaled edge coordinates to exceed the range representable by int. The rasterizer subsequently converts these values to int without range validation, resulting in undefined behavior and possible process termination, leading to denial of service.
Severity
7.5 (High)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
SSVC
Exploitation: poc Automatable: yes Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-24 17:54 UTC
CWE
  • n/a
  • CWE-681 - Incorrect Conversion between Numeric Types
Assigner
References
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

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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.



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.