Severity by source
CVSS:4.0/AV:N/AC:H/AT:P/PR:H/UI:P/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/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:X
File-borne pickle requiring the victim to load an attacker artifact via algo_from_pickle, so AV:L and UI:R with no attacker privileges (PR:N); deserialization yields full code execution (C/I/A:H).
Primary rating from Vendor (VulnCheck).
CVSS VectorVendor: VulnCheck
Lifecycle Timeline
5Blast Radius
ecosystem impact- 1 pypi packages depend on monai (1 direct, 0 indirect)
Ecosystem-wide dependent count for version 1.6.0.
DescriptionCVE.org
MONAI before 1.5.2 contains a deserialization of untrusted data vulnerability in the algo_from_pickle function in monai/auto3dseg/utils.py. The function reads a .pkl file and passes its contents to pickle.loads without validating the data source or content. If an application invokes algo_from_pickle on an attacker-supplied pickle file, an object defining __reduce__ is executed during deserialization, resulting in arbitrary code execution in the context of the application.
Articles & Coverage 1
AnalysisAI
Deserialization of untrusted data in MONAI's auto3dseg utility (monai/auto3dseg/utils.py) allows arbitrary code execution when algo_from_pickle() is called on an attacker-supplied .pkl file: the function reads the raw file bytes and hands them to pickle.loads() without any source or content validation, so any object defining __reduce__ runs in the victim application's context. Exploitation is conditional rather than remote-against-a-service: an attacker must first deliver a malicious pickle (model exchange, shared storage, or a pipeline input) and the victim's code must invoke this specific function, and per the authoritative assessment the vector is local with user interaction required (CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H) rather than unauthenticated network exploitation. …
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Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata
Vulnerability AssessmentAI
| Exploitation | Requires that the target application actually calls monai.auto3dseg.utils.algo_from_pickle() on an attacker-controlled .pkl file - i.e. … Additional conditions and limiting factors are described in the full assessment. |
| Risk Assessment | This is a genuine but conditional deserialization RCE (CWE-502): monai/auto3dseg/utils.py:algo_from_pickle() reads a .pkl file and passes its bytes to pickle.loads() with no source or content validation, so any object defining __reduce__ executes on load. … Full risk analysis with EPSS, KEV, and SSVC signal comparison available after sign-in. |
| Exploit Scenario | Full exploit scenario with step-by-step reproduction available after sign-in. |
| Remediation | Vendor-released patch: upgrade to MONAI 1.6.0 (pip install --upgrade 'monai>=1.6.0'); the upstream fix is at https://github.com/Project-MONAI/MONAI/commit/9078a72f3992e49bd4560db510be9ec4ccf972cc and advisory GHSA-89gg-p5r5-q6r4. … Detailed patch versions, workarounds, and compensating controls in full report. |
Recommended ActionAI
Within 24 hours, inventory all systems and pipelines using MONAI, especially the auto3dseg utility, and immediately block or restrict the loading of .pkl files from untrusted sources; instruct teams to avoid calling algo_from_pickle() on externally supplied files and verify the integrity of any required pickle files. …
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Same weakness CWE-502 – Deserialization of Untrusted Data
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External POC / Exploit Code
Leaving vuln.today
EUVD-2026-87842
GHSA-cvqf-4876-w239