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CVE-2026-25987 is a heap buffer over-read vulnerability in the MAP image decoder of ImageMagick, a widely used open-source image editing library. When processing crafted MAP files, the decoder reads beyond the intended heap buffer boundary, potentially causing application crashes or unintended memory disclosure. The vulnerability affects ImageMagick versions prior to 7.1.2-15 (7.x branch) and prior to 6.9.13-40 (6.x branch), as well as Magick.NET NuGet packages prior to version 14.10.3. It was disclosed on February 23–24, 2026, with patches released simultaneously. The GitHub Security Advisory assigns a CVSS v3.1 score of 5.3 (Moderate), while Feedly's aggregated data estimates a higher score of 9.1 (Critical) based on network-reachable, unauthenticated exploitation potential (GitHub Advisory, Red Hat).
The root cause is an out-of-bounds read (CWE-125) in ImageMagick's MAP image decoder, where insufficient bounds checking allows the decoder to read one or more bytes beyond the allocated heap buffer when parsing a maliciously crafted MAP file. AddressSanitizer output confirms a heap-buffer-overflow READ of size 1 at an out-of-bounds address during image decoding. The vulnerability is exploitable remotely with no authentication or user interaction required — an attacker simply needs to supply a crafted MAP file to an application or service that processes images via ImageMagick. The fix was introduced in commit bbae021 in the ImageMagick repository (GitHub Advisory, Github Advisory).
Successful exploitation can result in two primary consequences: unintended disclosure of sensitive data from adjacent heap memory regions (confidentiality impact), and denial of service through application crashes during image decoding (availability impact). There is no integrity impact, as the vulnerability does not allow data modification. Applications and services that accept user-supplied MAP image files — such as web applications using ImageMagick for image processing — are most at risk, and memory disclosure could potentially expose sensitive runtime data such as credentials or cryptographic material stored in process memory (GitHub Advisory, Red Hat Bugzilla).
As of the time of disclosure, there is no public proof-of-concept exploit and no evidence of active in-the-wild exploitation (Github Advisory). The vulnerability is not listed in the CISA Known Exploited Vulnerabilities (KEV) catalog. The EPSS score is approximately 0.028% (0.000280), placing it in a low percentile for near-term exploitation likelihood. The vulnerability was reported by researcher ylwango613 and disclosed responsibly through GitHub's security advisory process (GitHub Advisory).
heap-buffer-overflow or READ of size 1 during MAP file decoding.convert, magick, or application-embedded calls)..map files in upload directories or temporary image processing folders.The primary remediation is to upgrade ImageMagick to version 7.1.2-15 or later (7.x branch) or 6.9.13-40 or later (6.x branch). For .NET applications using Magick.NET NuGet packages, upgrade to version 14.10.3 or later. As interim workarounds: restrict or disable processing of MAP image files from untrusted sources; implement input validation to reject malformed or unexpected MAP files before they reach ImageMagick; and deploy image processing services behind network segmentation to limit exposure. Linux distribution packages (Ubuntu USN-8069-1, Debian DSA-6158/6159, openSUSE, Amazon Linux 2 ALAS2-2026-3211) have also released updated packages (GitHub Advisory, Red Hat, Ubuntu Advisory).
The vulnerability received routine coverage from Linux distribution security teams, with Ubuntu, Debian, openSUSE, SUSE, and Amazon Linux all issuing security advisories and updated packages within weeks of disclosure. Red Hat tracked the issue via Bugzilla and assigned medium severity. No notable researcher commentary or significant social media discussion has been identified beyond standard vulnerability database aggregation and scanner detection updates from Tenable (Nessus) and Qualys.
Source: This report was generated using AI
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