CVE-2025-6199
Published: 17 June 2025
Summary
CVE-2025-6199 is a low-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Redhat Enterprise Linux. Its CVSS base score is 3.3 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Local System (T1005); ranked at the 27.5th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-18505
Vulnerability details
A flaw was found in the GIF parser of GdkPixbuf’s LZW decoder. When an invalid symbol is encountered during decompression, the decoder sets the reported output size to the full buffer length rather than the actual number of written bytes.…
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This logic error results in uninitialized sections of the buffer being included in the output, potentially leaking arbitrary memory contents in the processed image.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability causes uninitialized heap memory to be disclosed in the output image when processing a crafted GIF, enabling adversaries to collect arbitrary data from the local system's process memory.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.