CVE-2024-50382
Botan Project Botan ≤ 3.6.0
Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2024-50382 is a medium-severity Observable Discrepancy (CWE-203) vulnerability in Botan Project Botan. Its CVSS base score is 5.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Account Discovery (T1087); ranked at the 43th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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-2024-44835
Vulnerability Data
Botan before 3.6.0, when certain LLVM versions are used, has compiler-induced secret-dependent control flow in lib/utils/ghash/ghash.cpp in GHASH in AES-GCM. There is a branch instead of an XOR with carry. This was observed for Clang in LLVM 15 on RISC-V.
- 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.