2vulnerabilities
- Critical0 (0.0%)
- High1 (50.0%)
- Medium0 (0.0%)
- Moderate1 (50.0%)
- Low0 (0.0%)
mlflow
Ecosystem: npm
Downloads: 7.53 K
- 0CriticalAffects 0 versions
- 1HighAffects 1 version
- 0MediumAffects 0 versions
- 1ModerateAffects 1 version
- 0LowAffects 0 versions
Monthly breakdown
- 2 vulnerabilities
- Critical: 0
- High: 1
- Medium: 0
- Moderate: 1
- Low: 0
Vulnerabilities (2)
Known vulnerabilities affecting mlflow, based on data from OSV.
| ID | Severity | Summary | Affected versions | Published | Source |
|---|---|---|---|---|---|
| GHSA-3p64-6gvh-82v5 | Moderate | MLflow: LogInputs endpoint bypasses per-run UPDATE authorization in basic-auth### Summary When MLflow is deployed with the built-in basic-auth plugin (`--app-name basic-auth`), any authenticated user can inject arbitrary dataset records into another user's run by calling `POST /api/2.0/mlflow/runs/log-inputs`. The `LogInputs` proto handler is absent from the `BEFORE_REQUEST_HANDLERS` map in `mlflow/server/auth/__init__.py`, so the before-request hook skips authorization entirely and the request succeeds. Standard write endpoints on the same run -- such as `POST /api/2.0/mlflow/runs/log-metric` -- correctly return HTTP 403. ### Details MLflow's basic-auth app gates ev |
| GitHub (opens in a new tab) | |
| GHSA-gqch-g4w5-7qcw | High | MLflow: CreateModelVersion source validation does not check READ permission on referenced run_id### Summary The `_validate_source_run` and `_validate_source_model` functions in `mlflow/server/handlers.py` verify that a model version source path is within the artifact directory of a specified run or logged model, but do not check whether the caller has READ permission on that run or model. An authenticated MLflow user can therefore reference another user's run_id in `CreateModelVersion`, creating a model version whose artifact URI points at the victim's artifact directory. If the calling user has MANAGE permission on the registered model (which they do after creation), they can then read |
| GitHub (opens in a new tab) |
- GHSA-3p64-6gvh-82v5ModerateMLflow: LogInputs endpoint bypasses per-run UPDATE authorization in basic-auth
< 3.15.0
### Summary When MLflow is deployed with the built-in basic-auth plugin (`--app-name basic-auth`), any authenticated user can inject arbitrary dataset records into another user's run by calling `POST /api/2.0/mlflow/runs/log-inputs`. The `LogInputs` proto handler is absent from the `BEFORE_REQUEST_HANDLERS` map in `mlflow/server/auth/__init__.py`, so the before-request hook skips authorization entirely and the request succeeds. Standard write endpoints on the same run -- such as `POST /api/2.0/mlflow/runs/log-metric` -- correctly return HTTP 403. ### Details MLflow's basic-auth app gates ev
CVE: CVE-2026-69146
- GHSA-gqch-g4w5-7qcwHighMLflow: CreateModelVersion source validation does not check READ permission on referenced run_id
< 3.15.0
### Summary The `_validate_source_run` and `_validate_source_model` functions in `mlflow/server/handlers.py` verify that a model version source path is within the artifact directory of a specified run or logged model, but do not check whether the caller has READ permission on that run or model. An authenticated MLflow user can therefore reference another user's run_id in `CreateModelVersion`, creating a model version whose artifact URI points at the victim's artifact directory. If the calling user has MANAGE permission on the registered model (which they do after creation), they can then read
CVE: CVE-2026-69148