CVE-2026-46607
Published: 25 June 2026
Summary
CVE-2026-46607 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability. Its CVSS base score is 7.8 (High).
Operationally, ranked at the 21.9th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
OWASP Top 10 for Web (2025)
EU & UK References
No EU or UK CSIRT advisories indexed for this CVE.
Vulnerability details
Glances is an open-source system cross-platform monitoring tool. Prior to 4.5.5, glances/outdated.py uses pickle.load() to read a version-check cache file stored at a predictable, world-accessible path (~/.cache/glances/glances-version.db or $XDG_CACHE_HOME/glances/glances-version.db). No integrity check, signature verification, or format validation is performed before…
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deserialization. An attacker with write access to that path — through any of several realistic local or container-level scenarios — can plant a malicious pickle file and achieve arbitrary code execution as the OS user running Glances the next time it starts with version checking enabled (the default). This vulnerability is fixed in 4.5.5.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
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.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Validates or rejects untrusted serialized data before deserialization occurs.
Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.
Integrity verification of serialized information can detect tampering before deserialization occurs.
Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.