CVE-2023-44192
Juniper Junos ≤ 20.4
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2023-44192 is a high-severity Improper Input Validation (CWE-20) vulnerability in Juniper Junos. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Junk Data (T1001.001); ranked at the 48th 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-2023-48551
Vulnerability Data
An Improper Input Validation vulnerability in the Packet Forwarding Engine of Juniper Networks Junos OS allows an unauthenticated, network-based attacker to cause memory leak, leading to Denial of Service (DoS). On all Junos OS QFX5000 Series platforms, when pseudo-VTEP (Virtual…
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Tunnel End Point) is configured under EVPN-VXLAN scenario, and specific DHCP packets are transmitted, DMA memory leak is observed. Continuous receipt of these specific DHCP packets will cause memory leak to reach 99% and then cause the protocols to stop working and traffic is impacted, leading to Denial of Service (DoS) condition. A manual reboot of the system recovers from the memory leak. To confirm the memory leak, monitor for "sheaf:possible leak" and "vtep not found" messages in the logs. This issue affects: Juniper Networks Junos OS QFX5000 Series: * All versions prior to 20.4R3-S6; * 21.1 versions prior to 21.1R3-S5; * 21.2 versions prior to 21.2R3-S5; * 21.3 versions prior to 21.3R3-S4; * 21.4 versions prior to 21.4R3-S3; * 22.1 versions prior to 22.1R3-S2; * 22.2 versions prior to 22.2R2-S2, 22.2R3; * 22.3 versions prior to 22.3R2-S1, 22.3R3; * 22.4 versions prior to 22.4R1-S2, 22.4R2.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
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.
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 enforce proper memory allocation/deallocation via coding standards, reviews, and tooling.
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.