CVE-2026-69146 is a medium-severity (CVSS 6.5) CWE-862 vulnerability in mlflow. O3 Security confirms whether CVE-2026-69146 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
MLflow: LogInputs endpoint bypasses per-run UPDATE authorization in basic-auth
Real-World Exposure
How broadly this vulnerability is actually deployed: weekly install volume shows current usage, and reverse-dependency count shows how many other packages break if it stays unpatched.
mlflownpmDescription
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 every HTTP handler through a before-request hook (_before_request) that looks up the relevant permission validator in BEFORE_REQUEST_VALIDATORS. Validators are built from the BEFORE_REQUEST_HANDLERS dictionary, which maps each protobuf request class to a callable. When a class is absent from the dict (or mapped to None), get_before_request_handler returns None, and the resulting entry in BEFORE_REQUEST_VALIDATORS is (path, method): None.
Inside _before_request:
# mlflow/server/auth/__init__.py _before_request()
if validator := _find_validator(request): # None is falsy -- branch skipped
if not validator():
return make_forbidden_response()
elif _is_proxy_artifact_path(request.path): # not a proxy path
...
# falls through: any authenticated request is allowed
The LogInputs protobuf class is not present in BEFORE_REQUEST_HANDLERS:
# mlflow/server/auth/__init__.py BEFORE_REQUEST_HANDLERS dict
# LogInputs is absent; all run-write operations below ARE present:
LogBatch: validate_can_update_run,
LogMetric: validate_can_update_run,
SetTag: validate_can_update_run,
LogParam: validate_can_update_run,
# LogInputs: <missing>
The route /api/2.0/mlflow/runs/log-inputs (and the identical /ajax-api/ variant) therefore admits any valid credential, regardless of which experiment or run is targeted. The LogInputs handler writes DatasetInput records directly to the run's lineage table without any ownership check.
PoC
Prerequisites: MLflow v3.13.0 running with --app-name basic-auth. Two accounts: alice (creates experiment 2 and run A) and bob (creates experiment 4 and run B).
- Confirm the authorized endpoint correctly denies alice's write to bob's run:
POST /api/2.0/mlflow/runs/log-metric HTTP/1.1
Authorization: Basic YWxpY2U6YWxpY2VfcGFzc3dvcmQxMjM= (alice:alice_password123)
Content-Type: application/json
{"run_id": "<bob_run_id>", "key": "test", "value": 1.0, "timestamp": 0, "step": 0}
Response: HTTP 403 Permission denied
- Inject a dataset record into bob's run as alice:
POST /api/2.0/mlflow/runs/log-inputs HTTP/1.1
Authorization: Basic YWxpY2U6YWxpY2VfcGFzc3dvcmQxMjM= (alice:alice_password123)
Content-Type: application/json
{"run_id": "<bob_run_id>", "datasets": [{"dataset": {"name": "ATTACKER_injected", "digest": "evil123", "profile": "attacker_controlled"}}]}
Response: HTTP 200 {}
- Confirm injection persisted:
GET /api/2.0/mlflow/runs/get?run_id=<bob_run_id> HTTP/1.1
Authorization: Basic Ym9iOmJvYl9wYXNzd29yZF9uZXcxMjM= (bob:bob_password_new123)
Response: HTTP 200 -- dataset_inputs array contains {"name":"ATTACKER_injected","digest":"evil123","profile":"attacker_controlled"}.
Impact
Any authenticated MLflow user can corrupt the dataset lineage metadata of any other user's run. In ML compliance workflows, dataset provenance records are audit evidence for model reproducibility and regulatory review. Injecting fake or misleading dataset entries into a competitor's runs can silently invalidate audit trails, cause misattribution of model training data, or introduce confusion about which datasets were used to train a model. The attacker needs only a valid credential; no elevated permissions are required.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 📦npm | mlflow | all versions | 3.15.0 |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for mlflow. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update mlflow to 3.15.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-69146 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether CVE-2026-69146 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to CVE-2026-69146. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is CVE-2026-69146 in your dependencies?
O3 detects CVE-2026-69146 across npm dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.