Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-24590 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Clear Clearml. Its CVSS base score is 8.0 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Junk Data (T1001.001); ranked in the top 16% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Other AI Platforms; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — 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 is a deserialization of untrusted data flaw (CWE-502) present in versions 0.17.0 through 1.14.2 of the client SDK for Allegro AI’s ClearML platform. It carries a CVSS 3.1 score of 8.0 and permits a maliciously uploaded artifact to trigger arbitrary code execution on a user’s system upon interaction.
An attacker able to upload artifacts to a ClearML instance can craft a malicious payload that executes with high impact on confidentiality, integrity, and availability when a victim user processes the artifact via the vulnerable SDK. The attack requires low complexity, limited privileges, and user interaction over the network.
The supplied references point to HiddenLayer research on MLOps supply-chain risks but contain no explicit mitigation guidance or patch details. The associated EPSS score stands at 0.8283 with no material rise from a lower baseline.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0620
Vulnerability Data
Deserialization of untrusted data can occur in versions 0.17.0 to 1.14.2 of the client SDK of Allegro AI’s ClearML platform, enabling a maliciously uploaded artifact to run arbitrary code on an end user’s system when interacted with.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other AI Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- ClearML is an MLOps platform for managing ML workflows, projects, datasets, experiments, and models, fitting 'Other Platforms' as it is a platform for ML operations not covered by more specific categories like frameworks or libraries.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
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-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
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.