CVE-2026-44636

May 15, 2026, 5:56 p.m.

7.4
High

Description

libsixel is a SIXEL encoder/decoder implementation derived from kmiya's sixel. From to 1.8.7-r1, signed integer overflow in sixel_encode_highcolor's allocation size calculation can lead to a heap buffer overflow. The public sixel_encode entry point validates only that width and height are greater than zero, with no upper bound. width and height are multiplied as plain int when computing the allocation size for paletted_pixels and normalized_pixels. Any caller that asks libsixel to encode a pixel buffer with width times height greater than INT_MAX (about 2.15 billion) will hit a wrapped allocation size; under the right wrap, the malloc succeeds with a buffer much smaller than the encoder expects, and the encoder writes past the end of the heap allocation. This vulnerability is fixed in 1.8.7-r2.

Product(s) Impacted

Vendor Product Versions
Saitoha
  • Libsixel
  • *

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-122
Heap-based Buffer Overflow
A heap overflow condition is a buffer overflow, where the buffer that can be overwritten is allocated in the heap portion of memory, generally meaning that the buffer was allocated using a routine such as malloc().

*CPE(s)

Affected systems and software identified for this CVE.

Type Vendor Product Version Update Edition Language Software Edition Target Software Target Hardware Other Information
a saitoha libsixel / / / / / / / /

CVSS Score

7.4 / 10

CVSS Data - 3.1

  • Attack Vector: LOCAL
  • Attack Complexity: HIGH
  • Privileges Required: NONE
  • Scope: UNCHANGED
  • Confidentiality Impact: HIGH
  • Integrity Impact: HIGH
  • Availability Impact: HIGH
  • CVSS:3.1/AV:L/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H

    View Vector String

Timeline

Published: May 14, 2026, 8:17 p.m.
Last Modified: May 15, 2026, 5:56 p.m.

Status : Analyzed

CVE has been recently published to the CVE List and has been received by the NVD.

More info

*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.