CVE-2026-77219 (GCVE-0-2026-77219)
Vulnerability from cvelistv5 – Published: 2026-08-21 20:20 – Updated: 2026-10-01 15:21 X_Open Source
VLAI
EPSS
VEX
Title
GNU Emacs < 31.0.91 Heap Over-Read via PBM/PPM/PGM Image Loader
Summary
GNU Emacs before 31.0.91 contains an integer overflow in the PBM/PPM/PGM image loader that allows an attacker to leak heap memory contents by supplying a crafted image with large dimensions and an elevated max color index. The image loader multiplies image dimensions and channel count using signed integer arithmetic; for sufficiently large values, the result wraps to a negative number, bypassing the bounds check and causing the pixel reader to access heap memory past the end of the allocated buffer. The over-read contents are interpreted as pixel color values and rendered on screen.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-21 21:32 UTC
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://debbugs.gnu.org/cgi/bugreport.cgi?bug=81344 | issue-tracking |
| https://github.com/emacs-mirror/emacs/releases/ta… | release-notes |
| https://github.com/emacs-mirror/emacs/commit/b07e… | patch |
| https://www.vulncheck.com/advisories/gnu-emacs-he… | third-party-advisory |
Impacted products
Date Public
2026-07-01 00:00
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
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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.