Security Resilience

CVE-2024-2032

Race Condition in Zenml ≤ 0.55.5

Race Condition
Published
06 June 2024
Modified
21 November 2024
Patch / advisory
View advisory
CVSS Score v3.1 3.1
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:H/PR:H/UI:R/S:U/C:N/I:L/A:L
EPSS Score 0.0029 20th percentile
Risk Priority 27 floored blend · peak EPSS

CVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.

Summary

CVE-2024-2032 is a low-severity Race Condition within a Thread (CWE-366) vulnerability in Zenml Zenml. Its CVSS base score is 3.1 (Low).

Operationally, exploitation aligns with the MITRE ATT&CK technique NTDS (T1003.003); ranked at the 20th percentile by exploit likelihood (below the median); 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 SA-11 (Developer Testing and Evaluation) and SA-15 (Development Process, Standards, and Tools) — see the control section below for these in your framework.

OWASP Top 10 for Web (2025)

A06:2025 Insecure Design

EU & UK References

Vulnerability Data

A race condition vulnerability exists in zenml-io/zenml versions up to and including 0.55.3, which allows for the creation of multiple users with the same username when requests are sent in parallel. This issue was fixed in version 0.55.5. The vulnerability…

more

arises due to insufficient handling of concurrent user creation requests, leading to data inconsistencies and potential authentication problems. Specifically, concurrent processes may overwrite or corrupt user data, complicating user identification and posing security risks. This issue is particularly concerning for APIs that rely on usernames as input parameters, such as PUT /api/v1/users/test_race, where it could lead to further complications.

CWE(s)
CWE-366CWE-362

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 building, deploying, and managing machine learning pipelines, fitting under 'Other Platforms' as it is a platform for ML workflows rather than specific libraries or frameworks like deep learning or NLP tools.

Related Threats

MITRE ATT&CK Enterprise Techniques

T1003.003 NTDS Credential Access
Adversaries may attempt to access or create a copy of the Active Directory domain database in order to steal credential information, as well as obtain other information about domain members such as devices, users, and access rights.
attack.mitre.org →
T1003.004 LSA Secrets Credential Access
Adversaries with SYSTEM access to a host may attempt to access Local Security Authority (LSA) secrets, which can contain a variety of different credential materials, such as credentials for service accounts.
attack.mitre.org →
T1006 Direct Volume Access Stealth
Adversaries may directly access a volume to bypass file access controls and file system monitoring.
attack.mitre.org →
T1007 System Service Discovery Discovery
Adversaries may try to gather information about registered local system services.
attack.mitre.org →
T1008 Fallback Channels Command And Control
Adversaries may use fallback or alternate communication channels if the primary channel is compromised or inaccessible in order to maintain reliable command and control and to avoid data transfer thresholds.
attack.mitre.org →
T1010 Application Window Discovery Discovery
Adversaries may attempt to get a listing of open application windows.
attack.mitre.org →
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2024-4311Same product: Zenml Zenml
CVE-2024-2260Same product: Zenml Zenml
CVE-2024-2171Same product: Zenml Zenml
CVE-2024-2213Same product: Zenml Zenml
CVE-2024-2083Same product: Zenml Zenml
CVE-2024-25723Same product: Zenml Zenml
CVE-2024-4680Same product: Zenml Zenml
CVE-2024-5062Same product: Zenml Zenml
CVE-2024-28424Same product: Zenml Zenml
CVE-2024-9340Same product: Zenml Zenml

Affected Assets

zenml
zenml
≤ 0.55.5

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

—

Detect
Catch it (NIST detect / respond)

—

Harden
Shrink the surface (DISA STIG)

—

Validate
Prove the fix (OWASP ASVS)
  • V15.4.2
  • V15.4.3
  • V17.2.6

Mitigating Controls (NIST 800-53 r5) AI

SA-11 Developer Testing and Evaluation good match

Developer testing and evaluation can include concurrency and stress testing that finds race conditions after they have been coded.

SA-15 Development Process, Standards, and Tools partial match

Requiring documented development standards and tools can mandate use of safe concurrency patterns that avoid introducing races.

SA-8 Security and Privacy Engineering Principles partial match

Security engineering principles include requirements for synchronization primitives and thread-safe design that stop race conditions from being introduced.

SC-39 Process Isolation partial match

Maintaining separate execution domains for each process structurally eliminates unintended concurrent access to the same shared resources.

SC-4 Information in Shared System Resources partial match

Preventing unintended information transfer through shared system resources directly addresses the improper concurrent modification that defines a race condition.

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.PS-06 partial match
blocks

Secure SDLC practices directly require synchronization primitives and concurrency analysis that prevent intra-thread race conditions.

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.8.19 Installation of software on operational systems partial match
removes
A.8.25 Secure development life cycle partial match
blocks

A.8.25 mandates secure SDLC practices (secure coding guidelines, security requirements, checkpoints, testing including code scans/penetration tests, and developer training) that reach race conditions in multithreaded code, but leaves a bounded remainder where implementation is incomplete or the specific synchronization flaw slips past the chosen methodology and tests.

A.8.26 Application security requirements partial match
blocks

A.8.26 only requires that race-condition-relevant requirements (e.g. thread safety, synchronization) be identified during requirements gathering; it performs no enforcement, design, or implementation act that stops the CWE-366 condition from being exploitable when present.

A.8.27 Secure system architecture and engineering principles partial match
blocks

Secure architecture principles address concurrent access and resource synchronization.

A.8.28 Secure coding partial match
prevents

Secure coding standards directly require proper synchronization primitives to prevent race conditions.

A.8.29 Security testing in development and acceptance partial match
finds

Security testing can detect race conditions through concurrency and stress testing.

Hardening callouts derived

DISA STIG baselines are authored against specific weaknesses. Where this CVE cites a broad weakness class, the mapping is not asserted at that abstraction (owner ruling, 2026-09-25: grade only at Base and below), so no rule is shown here rather than a rule shown against the wrong grain.

  • No fine-grained STIG row for CWE-362, a class-level weakness, and none of its children carries one either.

References