CVE-2023-39522
Goauthentik Authentik ≤ 2023.5.6
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:NSummary
CVE-2023-39522 is a medium-severity Observable Discrepancy (CWE-203) vulnerability in Goauthentik Authentik. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Account Discovery (T1087); ranked at the 41th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-2364
Vulnerability Data
goauthentik is an open-source Identity Provider. In affected versions using a recovery flow with an identification stage an attacker is able to determine if a username exists. Only setups configured with a recovery flow are impacted by this. Anyone with…
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a user account on a system with the recovery flow described above is susceptible to having their username/email revealed as existing. An attacker can easily enumerate and check users' existence using the recovery flow, as a clear message is shown when a user doesn't exist. Depending on configuration this can either be done by username, email, or both. This issue has been addressed in versions 2023.5.6 and 2023.6.2. Users are advised to upgrade. There are no known workarounds for this issue.
- 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.
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.
Prevents attackers from using observable differences in error responses to infer internal system details or state.
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.
Accurate, synchronized timestamps reduce observable timing discrepancies that an attacker could exploit to infer sensitive information or distinguish between success and failure paths.