CVE-2014-9390
Git-Scm Git ≤ 1.8.5.6
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2014-9390 is a critical-severity Improper Input Validation (CWE-20) vulnerability in Git-Scm Git. Its CVSS base score is 9.8 (Critical).
Operationally, ranked in the top 0.9% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2020-0110
Vulnerability Data
Git before 1.8.5.6, 1.9.x before 1.9.5, 2.0.x before 2.0.5, 2.1.x before 2.1.4, and 2.2.x before 2.2.1 on Windows and OS X; Mercurial before 3.2.3 on Windows and OS X; Apple Xcode before 6.2 beta 3; mine all versions before 08-12-2014;…
more
libgit2 all versions up to 0.21.2; Egit all versions before 08-12-2014; and JGit all versions before 08-12-2014 allow remote Git servers to execute arbitrary commands via a tree containing a crafted .git/config file with (1) an ignorable Unicode codepoint, (2) a git~1/config representation, or (3) mixed case that is improperly handled on a case-insensitive filesystem.
- CWE(s)
Related Threats
Likely ATT&CK TechniquesAI
Techniques this vulnerability likely enables, inferred from its description, weakness type, and attributed-actor tradecraft. Confidence is per-technique.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 6 hardening rules · 3 OS baselines
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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.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Directly implements checks on information inputs to reject invalid data before processing.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly require and enforce input validation during development.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Testing against a defined set of requirements and using code review plus vulnerability scanning forces validation of inputs and handling of unanticipated conditions, reducing the chance that malformed data will be accepted.
Secure-coding guidelines and mandatory security testing (including code scans) compel developers to validate and sanitize inputs at design and implementation time, lowering the incidence of malformed or malicious data reaching downstream components.
Mandating input controls that include integrity checks and input validation ensures that untrusted data is examined before use, blocking the root cause of many injection and malformed-data weaknesses.
Security-by-design principles explicitly call for data validation and sanitization at every layer, reducing the chance that malformed or malicious input will be processed without scrutiny.
Requiring language-specific secure coding standards, peer review, SAST and documented mitigation of common programming errors forces validation of all inputs before they are trusted.
Regular automated validation of system software and data content, combined with scanning of all inbound files, enforces input validation at the boundary before untrusted content is processed.