CVE-2023-51437
Apache Pulsar ≤ 2.10.5
Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:NSummary
CVE-2023-51437 is a high-severity Observable Discrepancy (CWE-203) vulnerability in Apache Pulsar. Its CVSS base score is 7.4 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Account Discovery (T1087); ranked in the top 48% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to IA-6 (Authentication Feedback) and SI-11 (Error Handling) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0609
Vulnerability Data
Observable timing discrepancy vulnerability in Apache Pulsar SASL Authentication Provider can allow an attacker to forge a SASL Role Token that will pass signature verification. Users are recommended to upgrade to version 2.11.3, 3.0.2, or 3.1.1 which fixes the issue.…
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Users should also consider updating the configured secret in the `saslJaasServerRoleTokenSignerSecretPath` file. Any component matching an above version running the SASL Authentication Provider is affected. That includes the Pulsar Broker, Proxy, Websocket Proxy, or Function Worker. 2.11 Pulsar users should upgrade to at least 2.11.3. 3.0 Pulsar users should upgrade to at least 3.0.2. 3.1 Pulsar users should upgrade to at least 3.1.1. Any users running Pulsar 2.8, 2.9, 2.10, and earlier should upgrade to one of the above patched versions. For additional details on this attack vector, please refer to https://codahale.com/a-lesson-in-timing-attacks/ .
- 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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Mitigating Controls (NIST 800-53 r5) AI
Obscures authentication feedback so that success/failure differences are not observable to attackers.
Requires error messages to avoid revealing exploitable details, directly stopping observable response discrepancies.
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.