CVE-2023-28200
Apple Macos 11.0 – 11.7.5
Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2023-28200 is a medium-severity Improper Input Validation (CWE-20) vulnerability in Apple Macos. Its CVSS base score is 5.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Steganography (T1001.002); ranked at the 23th 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
- 🇪🇺 ENISA EUVD: EUVD-2023-31908
Vulnerability Data
A validation issue was addressed with improved input sanitization. This issue is fixed in macOS Ventura 13.3, iOS 15.7.4 and iPadOS 15.7.4, macOS Monterey 12.6.4, macOS Big Sur 11.7.5. An app may be able to disclose kernel memory.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 1 hardening rule · 1 OS baseline
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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.
Misdirection can normalize or falsify responses to eliminate observable discrepancies that aid reconnaissance.
Observable discrepancies in system behavior can be modulated to create covert storage or timing channels; the required analysis detects and constrains such avenues.
Directly implements checks on information inputs to reject invalid data before processing.
Prevents attackers from using observable differences in error responses to infer internal system details or state.
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 prevent observable response discrepancies via consistent error handling and timing.
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.
Hardening callouts derived
DISA STIG baselines are authored against specific weaknesses. Where this CVE cites a broad weakness class, the mapping is not asserted at that abstraction (owner ruling, 2026-09-25: grade only at Base and below), so no rule is shown here rather than a rule shown against the wrong grain.
- No fine-grained STIG row for CWE-20, a class-level weakness, and none of its children carries one either.