CVE-2024-25723
Zenml ≤ 0.42.2
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-25723 is a high-severity Improper Access Control (CWE-284) vulnerability in Zenml Zenml. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 0.7% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Other AI Platforms; in the Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and AC-4 (Information Flow Enforcement) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
The vulnerability affects the ZenML Server component of the ZenML machine learning operations package for Python prior to version 0.46.7. It stems from improper access control in the /api/v1/users/{user_name_or_id}/activate REST API endpoint, which permits password changes based solely on a supplied username without additional authentication checks. The flaw is tracked under CWE-284 and carries a CVSS 3.1 base score of 8.8.
An authenticated remote attacker who knows or can enumerate a valid username can supply a new password in the request body to the activation endpoint, thereby taking over the account and achieving full privilege escalation with high impact on confidentiality, integrity, and availability.
ZenML has released fixes in versions 0.46.7, 0.44.4, 0.43.1, and 0.42.2; the project repository and accompanying security advisory recommend immediate upgrade to one of these releases. The current EPSS score stands at 0.8964, reflecting elevated exploitation likelihood for this machine-learning platform component.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0730
Vulnerability Data
ZenML Server in the ZenML machine learning package before 0.46.7 for Python allows remote privilege escalation because the /api/v1/users/{user_name_or_id}/activate REST API endpoint allows access on the basis of a valid username along with a new password in the request body.…
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These are also patched versions: 0.44.4, 0.43.1, and 0.42.2.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other AI Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- ZenML is an open-source MLOps framework for machine learning workflows, fitting 'Other Platforms' as an ML operations platform/server, not matching more specific categories like deep learning frameworks or NLP libraries.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 8 hardening rules · 4 OS baselines
V10.3.5
Mitigating Controls (NIST 800-53 r5) AI
Directly enforces approved authorizations for logical access, stopping unauthorized actors from reaching resources.
Enforces flow-control policies that restrict information movement between subjects and objects.
Documents duties and assigns access so that no single account can bypass intended restrictions.
Limits each account to the minimum privileges needed, reducing the chance of unauthorized access.
Defines account types, assigns/removes access, and reviews accounts to ensure only authorized actors can reach resources.
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.AA-05 directly enforces policy-based access management and least privilege, eliminating most improper-access-control defects, yet CWE-284 also covers implementation flaws and design gaps outside a single management control.
Hardened baselines and deviation monitoring directly eliminate most configuration-induced access-control defects, yet CWE-284 also encompasses code-level and design flaws outside the scope of configuration management alone.
Secure SDLC practices catch most access-control defects during design/coding/testing (mostly), yet leave residual risk from runtime configuration, architecture, and operational controls (partial).
PR.AA-01 supplies managed identities/credentials that support but do not implement access-control decisions, so it only partially prevents CWE-284 in either direction.
Authentication directly blocks unauthenticated actors (partial prevention of CWE-284) but leaves authorization logic, policy enforcement, and role checks untouched, so the control neither eliminates nor fully mitigates the broader weakness.
PR.DS-01 encryption mitigates impact of failed access checks on stored data but neither implements nor constrains access-control logic itself.
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.
Defining and enforcing explicit access rights and restrictions for each entity directly stops the assignment of permissions that exceed what is required, eliminating the root condition that allows improper access control.
Formal authorization, role-based provisioning, and timely revocation of access rights directly stop the creation of accounts or permissions that exceed what the business actually needs.
By enforcing explicit rules on which identities or groups may perform read, write, delete or execute operations and by denying anonymous access to sensitive data, the control directly stops the creation of overly permissive or missing access-control checks.
Requiring one-to-one mapping of identities to entities and timely removal of unused identities directly stops attackers from leveraging stale or shared accounts to bypass access restrictions.
By explicitly transferring security roles and responsibilities when personnel change jobs or leave, the control reduces the chance that former employees retain access rights they no longer need, thereby limiting improper access control.
Physical entry controls enforce explicit authorization and authentication at every access point, directly stopping unauthorized actors from reaching information-processing assets.
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` (authoritative rows only; rows rated `none` are excluded).
Oracle Linux 8 (2 rules)
- V-248597 There must be no "shosts.equiv" files on the OL 8 operating system. prevents CWE-284
- V-248598 There must be no ".shosts" files on the OL 8 operating system. prevents CWE-284
Oracle Linux 9 (2 rules)
- V-271758 OL 9 file systems must not contain .shosts files. prevents CWE-284
- V-271757 OL 9 file systems must not contain shosts.equiv files. prevents CWE-284
RHEL 7 (2 rules)
- V-204606 The Red Hat Enterprise Linux operating system must not contain .shosts files. prevents CWE-284
- V-204607 The Red Hat Enterprise Linux operating system must not contain shosts.equiv files. prevents CWE-284
RHEL 8 (2 rules)
- V-230283 There must be no shosts.equiv files on the RHEL 8 operating system. prevents CWE-284
- V-230284 There must be no .shosts files on the RHEL 8 operating system. prevents CWE-284