Raw vector
CVSS:3.1/AV:A/AC:L/PR:H/UI:N/S:C/C:L/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-2023-3299 is a low-severity Insertion of Sensitive Information Into Sent Data (CWE-201) vulnerability in Hashicorp Nomad. Its CVSS base score is 3.4 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Local System (T1005); ranked at the 47th 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-2023-2015
Vulnerability Data
HashiCorp Nomad Enterprise 1.2.11 up to 1.5.6, and 1.4.10 ACL policies using a block without a label generates unexpected results. Fixed in 1.6.0, 1.5.7, and 1.4.11.
- 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
V14.2.3
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.
Controls whether organization resources are exposed to external system spheres by permitting or prohibiting their use.
The control ensures information is not released into a security sphere where the recipient lacks matching access authorizations.
The control ensures information resources are not exposed to the incorrect (public) sphere through review and authorization.
Protects against data mining that would expose resources to unauthorized spheres by enforcing detection and controls.
Restricts information flows to ensure resources are not exposed to incorrect or unauthorized spheres.
Controlling internal connections prevents exposure of resources to unintended internal spheres.
Knowing exact processing and storage locations helps avoid exposure of resources to incorrect spheres.
The control prevents exposure of the media resource to the wrong security sphere.
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.
Monitoring runtime data flows and outputs can detect sensitive data being transmitted.
Protecting data-in-transit can include filtering or encrypting to avoid exposing sensitive content.
Protecting data-in-use includes removing confidential values before they are processed or sent.
Secure SDLC practices directly prevent insertion of sensitive data into application outputs and messages.
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.
Classification identifies sensitive data so it is not inadvertently transmitted.
Labelling makes sensitive data visible to developers and prevents accidental inclusion in outbound messages.
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.