EUVD-2026-34245
GHSA-5qmp-p3c4-72qj
Severity by source
CVSS:4.0/AV:L/AC:H/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N/E:P/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
Primary rating from NVD · only source for this CVE.
CVSS VectorNVD
CVSS:4.0/AV:L/AC:H/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N/E:P/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
Lifecycle Timeline
2Blast Radius
ecosystem impact- 14 pypi packages depend on mlflow (14 direct, 0 indirect)
Ecosystem-wide dependent count for version 3.10.1.
DescriptionCVE.org
A flaw has been found in MLflow up to 3.10.0. This issue affects the function mlflow.data.digest_utils of the file mlflow/data/digest_utils.py of the component Dataset Digest Computation. This manipulation causes use of weak hash. It is possible to launch the attack on the local host. The attack is considered to have high complexity. The exploitability is assessed as difficult. The exploit has been published and may be used. The project was informed of the problem early through a pull request but has not reacted yet.
AnalysisAI
Dataset digest computation in MLflow up to version 3.10.0 uses MD5 - a cryptographically broken algorithm - to fingerprint datasets, enabling a local attacker to craft colliding inputs that undermine dataset integrity tracking. Affected functions include compute_pandas_digest, compute_numpy_digest, and hash_dict_of_arrays in mlflow/data/digest_utils.py, which use a truncated 8-character MD5 digest that further reduces the collision space. …
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Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata
Vulnerability AssessmentAI
| Exploitation | Exploitation requires local host access to the system running MLflow (AV:L) - remote network exploitation is not possible. … Additional conditions and limiting factors are described in the full assessment. |
| Risk Assessment | The CVSS 4.0 score of 1.1 accurately reflects the narrow real-world risk profile. … Full risk analysis with EPSS, KEV, and SSVC signal comparison available after sign-in. |
| Exploit Scenario | A locally authenticated MLflow user with low privileges crafts two distinct pandas DataFrames engineered to produce an identical 8-character MD5 digest by exploiting MD5 collision properties and the limited column type coverage in the original sampling logic. The attacker logs the benign dataset under a known run, then substitutes the malicious dataset with the same computed digest, bypassing MLflow's dataset integrity check. … |
| Remediation | No vendor-released patch has been identified at time of analysis. … Detailed patch versions, workarounds, and compensating controls in full report. |
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External POC / Exploit Code
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