Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2025-23045 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Cvat Computer Vision Annotation Tool. Its CVSS base score is 8.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 40th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
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.
CVE-2025-23045 is a deserialization vulnerability (CWE-502) in the Computer Vision Annotation Tool (CVAT), an interactive video and image annotation tool for computer vision tasks. It affects CVAT deployments that run serverless functions of type "tracker" from the CVAT Git repository, specifically TransT and SiamMask. Custom tracker functions may also be vulnerable if they use unsafe serialization libraries such as pickle or jsonpickle. The flaw enables an attacker with an account on an affected CVAT instance to execute arbitrary code within the context of the Nuclio function container. The vulnerability has a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H) and was published on 2025-01-28.
An authenticated attacker with a standard account on the CVAT instance can exploit this vulnerability remotely over the network with low complexity. By targeting the affected tracker functions, they can achieve remote code execution in the Nuclio container, potentially leading to high confidentiality, integrity, and availability impacts, such as data exfiltration, modification of annotation data, or container compromise.
Mitigation requires upgrading to CVAT version 2.26.0 or later, as detailed in the GitHub security advisory (GHSA-wq36-mxf8-hv62) and the fixing commit (563e1dfde64b15fa042b23f9d09cd854b35f0366). If upgrading is not feasible, administrators should immediately shut down any running instances of the TransT or SiamMask functions.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-3093
Vulnerability Data
Computer Vision Annotation Tool (CVAT) is an interactive video and image annotation tool for computer vision. An attacker with an account on an affected CVAT instance is able to run arbitrary code in the context of the Nuclio function container.…
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This vulnerability affects CVAT deployments that run any of the serverless functions of type tracker from the CVAT Git repository, namely TransT and SiamMask. Deployments with custom functions of type tracker may also be affected, depending on how they handle state serialization. If a function uses an unsafe serialization library such as pickle or jsonpickle, it's likely to be vulnerable. Upgrade to CVAT 2.26.0 or later. If you are unable to upgrade, shut down any instances of the TransT or SiamMask functions you're running.
- CWE(s)
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.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.