Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/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-2023-27591 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Miniflux Project Miniflux. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Cloud Services (T1021.007); ranked in the top 46% 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-2023-31333
Vulnerability Data
Miniflux is a feed reader. Prior to version 2.0.43, an unauthenticated user can retrieve Prometheus metrics from a publicly reachable Miniflux instance where the `METRICS_COLLECTOR` configuration option is enabled and `METRICS_ALLOWED_NETWORKS` is set to `127.0.0.1/8` (the default). A patch is…
more
available in Miniflux 2.0.43. As a workaround, set `METRICS_COLLECTOR` to `false` (default) or run Miniflux behind a trusted reverse-proxy.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 3 hardening rules · 3 OS baselines
V16.4.2V8.4.2V11.7.1V13.2.4
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.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.
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.
PR.IR-01's segmentation + zero-trust rules directly enforce minimum-necessary access at network boundaries, removing most instances of overly broad policy; CWE-1220 can still exist in application-layer or intra-segment controls that this single outcome does not address.
PR.PS-06's general SDLC practices can surface granularity issues during design/review (partial prevention) but do not specifically target access-control policy breadth, so they remove only part of CWE-1220's risk.
CWE-1220 permits unauthorized access to sensitive assets but neither prevents nor impairs the delivery of adverse-event information to authorized recipients.
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.
A.5.10 only requires awareness, a topic-specific acceptable-use policy stating expected behaviours/monitoring, and procedures referencing classification and other controls; per the owner's explicit rule on labelling and similar dependency-style clauses, this does not itself prevent exposure (CWE-200) unless access/DLP/etc. act on it.
Requiring the return of all physical and electronic assets plus secure deletion of organizational data from personal devices reduces the chance that sensitive information remains accessible to departing personnel.
A.5.12 only defines and communicates a classification scheme so that protection needs can be understood; per the owner's explicit rule, labeling/classification does not itself prevent exposure and requires other controls (e.g. access enforcement) to act on it.
By requiring classification labels and metadata on all information assets, the control makes it harder for sensitive data to be inadvertently exposed because downstream systems and users can recognize and enforce handling rules based on those labels.
Requiring encryption, access controls, and recipient authentication for transfers directly reduces the chance that sensitive data reaches an unauthorized observer.
A.5.15 directly mandates defining and implementing granular access control rules (including need-to-know, role-based restrictions, segregation, dynamic factors, and consistency with classification), which stops most insufficient-granularity cases from being exploitable; the bounded remainder is access-control defects inside individual applications or physical mechanisms that the clause explicitly delegates elsewhere (8.26, 7.2-7.4).
Hardening callouts derived
Configuration rules from DISA STIG baselines that bear on weaknesses of the type cited by this CVE. Each rule is shown with the relationship its mapping actually records, against the CWE it was authored against. Derived via CVE→CWE over `controls_xwalks` (higher-evidence rows only; rows rated `none` are excluded).
Windows Server 2016 (1 rule)
- V-225025 Local accounts with blank passwords must be restricted to prevent access from the network. blocks CWE-1220