CVE-2023-4033 (published 2023-08-01T01:15Z)

OS Command Injection in GitHub repository mlflow/mlflow prior to 2.6.0.

CVSS v3 base score: 7.8, HIGH (CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)

Weakness information:
cwe_id: CWE-78
cwe_name: Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
description: The product constructs all or part of an OS command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended OS command when it is sent to a downstream component.
extended_description: 
		   This weakness can lead to a vulnerability in environments in which the attacker does not have direct access to the operating system, such as in web applications. Alternately, if the weakness occurs in a privileged program, it could allow the attacker to specify commands that normally would not be accessible, or to call alternate commands with privileges that the attacker does not have. The problem is exacerbated if the compromised process does not follow the principle of least privilege, because the attacker-controlled commands may run with special system privileges that increases the amount of damage.
            There are at least two subtypes of OS command injection:
               
				 The application intends to execute a single, fixed program that is under its own control. It intends to use externally-supplied inputs as arguments to that program. For example, the program might use system("nslookup [HOSTNAME]") to run nslookup and allow the user to supply a HOSTNAME, which is used as an argument. Attackers cannot prevent nslookup from executing. However, if the program does not remove command separators from the HOSTNAME argument, attackers could place the separators into the arguments, which allows them to execute their own program after nslookup has finished executing.
				 The application accepts an input that it uses to fully select which program to run, as well as which commands to use. The application simply redirects this entire command to the operating system. For example, the program might use "exec([COMMAND])" to execute the [COMMAND] that was supplied by the user. If the COMMAND is under attacker control, then the attacker can execute arbitrary commands or programs. If the command is being executed using functions like exec() and CreateProcess(), the attacker might not be able to combine multiple commands together in the same line.
               
            From a weakness standpoint, these variants represent distinct programmer errors. In the first variant, the programmer clearly intends that input from untrusted parties will be part of the arguments in the command to be executed. In the second variant, the programmer does not intend for the command to be accessible to any untrusted party, but the programmer probably has not accounted for alternate ways in which malicious attackers can provide input.
         
         

Fix information:
repo_url: https://github.com/mlflow/mlflow
commit_details: repo_url: https://github.com/mlflow/mlflow
author: Serena Ruan
author_date: 2023-07-18 09:31:13+08:00
msg: Fix potential issues with PyFuncBackend in cli (#9053)

Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
repository: repo_url: https://github.com/mlflow/mlflow
repo_name: mlflow/mlflow
description: Open source platform for the machine learning lifecycle
repo_language: Python
owner: mlflow
file_changes: filename: _mlflow_pyfunc_backend_predict.py
change_type: ModificationType.ADD
diff: @@ -0,0 +1,29 @@
+"""
+This script should be executed in a fresh python interpreter process using `subprocess`.
+"""
+import argparse
+
+from mlflow.pyfunc.scoring_server import _predict
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-uri", required=True)
+    parser.add_argument("--input-path", required=False)
+    parser.add_argument("--output-path", required=False)
+    parser.add_argument("--content-type", required=True)
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    _predict(
+        model_uri=args.model_uri,
+        input_path=args.input_path if args.input_path else None,
+        output_path=args.output_path if args.output_path else None,
+        content_type=args.content_type,
+    )
+
+
+if __name__ == "__main__":
+    main()

programming_language: Python
filename: backend.py
change_type: ModificationType.MODIFY
diff: @@ -3,6 +3,7 @@ import os
 import pathlib
 import subprocess
 import posixpath
+import shlex
 import sys
 import warnings
 import ctypes
@@ -24,6 +25,7 @@ from mlflow.pyfunc import ENV, scoring_server, mlserver, _extract_conda_env
 from mlflow.utils.conda import get_or_create_conda_env, get_conda_bin_executable
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
 from mlflow.utils import env_manager as _EnvManager
+from mlflow.pyfunc import _mlflow_pyfunc_backend_predict
 from mlflow.utils.file_utils import (
     path_to_local_file_uri,
     get_or_create_tmp_dir,
@@ -143,20 +145,19 @@ class PyFuncBackend(FlavorBackend):
         local_uri = path_to_local_file_uri(local_path)
 
         if self._env_manager != _EnvManager.LOCAL:
-            command = (
-                'python -c "from mlflow.pyfunc.scoring_server import _predict; _predict('
-                "model_uri={model_uri}, "
-                "input_path={input_path}, "
-                "output_path={output_path}, "
-                "content_type={content_type})"
-                '"'
-            ).format(
-                model_uri=repr(local_uri),
-                input_path=repr(input_path),
-                output_path=repr(output_path),
-                content_type=repr(content_type),
-            )
-            return self.prepare_env(local_path).execute(command)
+            predict_cmd = [
+                "python",
+                _mlflow_pyfunc_backend_predict.__file__,
+                "--model-uri",
+                str(local_uri),
+                "--content-type",
+                shlex.quote(str(content_type)),
+            ]
+            if input_path:
+                predict_cmd += ["--input-path", shlex.quote(str(input_path))]
+            if output_path:
+                predict_cmd += ["--output-path", shlex.quote(str(output_path))]
+            return self.prepare_env(local_path).execute(" ".join(predict_cmd))
         else:
             scoring_server._predict(local_uri, input_path, output_path, content_type)
 

programming_language: Python
filename: __init__.py
change_type: ModificationType.MODIFY
diff: @@ -17,6 +17,7 @@ import flask
 import json
 import logging
 import os
+import shlex
 import sys
 import traceback
 
@@ -31,6 +32,7 @@ from mlflow.exceptions import MlflowException
 from mlflow.types import Schema
 from mlflow.utils import reraise
 from mlflow.utils.file_utils import path_to_local_file_uri
+from mlflow.utils.os import is_windows
 from mlflow.utils.proto_json_utils import (
     NumpyEncoder,
     dataframe_from_parsed_json,
@@ -328,14 +330,16 @@ def get_cmd(
 ) -> Tuple[str, Dict[str, str]]:
     local_uri = path_to_local_file_uri(model_uri)
     timeout = timeout or MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT.get()
+
     # NB: Absolute windows paths do not work with mlflow apis, use file uri to ensure
     # platform compatibility.
-    if os.name != "nt":
+    if not is_windows():
         args = [f"--timeout={timeout}"]
         if port and host:
-            args.append(f"-b {host}:{port}")
+            address = shlex.quote(f"{host}:{port}")
+            args.append(f"-b {address}")
         elif host:
-            args.append(f"-b {host}")
+            args.append(f"-b {shlex.quote(host)}")
 
         if nworkers:
             args.append(f"-w {nworkers}")
@@ -347,7 +351,7 @@ def get_cmd(
     else:
         args = []
         if host:
-            args.append(f"--host={host}")
+            args.append(f"--host={shlex.quote(host)}")
 
         if port:
             args.append(f"--port={port}")

programming_language: Python
filename: test_cli.py
change_type: ModificationType.MODIFY
diff: @@ -28,6 +28,7 @@ import mlflow.models.cli as models_cli
 from mlflow.environment_variables import MLFLOW_DISABLE_ENV_MANAGER_CONDA_WARNING
 from mlflow.exceptions import MlflowException
 from mlflow.protos.databricks_pb2 import ErrorCode, BAD_REQUEST
+from mlflow.pyfunc.backend import PyFuncBackend
 from mlflow.pyfunc.scoring_server import (
     CONTENT_TYPE_JSON,
     CONTENT_TYPE_CSV,
@@ -36,6 +37,7 @@ from mlflow.utils.file_utils import TempDir
 from mlflow.utils.environment import _mlflow_conda_env
 from mlflow.utils import env_manager as _EnvManager
 from mlflow.utils import PYTHON_VERSION
+from mlflow.utils.process import ShellCommandException
 from tests.helper_functions import (
     pyfunc_build_image,
     pyfunc_serve_from_docker_image,
@@ -174,7 +176,7 @@ def test_predict(iris_data, sk_model):
         with mlflow.start_run() as active_run:
             mlflow.sklearn.log_model(sk_model, "model", registered_model_name="impredicting")
             model_uri = f"runs:/{active_run.info.run_id}/model"
-        model_registry_uri = "models:/{name}/{stage}".format(name="impredicting", stage="None")
+        model_registry_uri = "models:/impredicting/None"
         input_json_path = tmp.path("input.json")
         input_csv_path = tmp.path("input.csv")
         output_json_path = tmp.path("output.json")
@@ -331,6 +333,173 @@ def test_predict(iris_data, sk_model):
         assert all(expected == actual)
 
 
+def test_predict_check_content_type(iris_data, sk_model, tmp_path):
+    with mlflow.start_run():
+        mlflow.sklearn.log_model(sk_model, "model", registered_model_name="impredicting")
+    model_registry_uri = "models:/impredicting/None"
+    input_json_path = tmp_path / "input.json"
+    input_csv_path = tmp_path / "input.csv"
+    output_json_path = tmp_path / "output.json"
+
+    x, _ = iris_data
+    with input_json_path.open("w") as f:
+        json.dump({"dataframe_split": pd.DataFrame(x).to_dict(orient="split")}, f)
+
+    pd.DataFrame(x).to_csv(input_csv_path, index=False)
+
+    # Throw errors for invalid content_type
+    prc = subprocess.run(
+        [
+            "mlflow",
+            "models",
+            "predict",
+            "-m",
+            model_registry_uri,
+            "-i",
+            input_json_path,
+            "-o",
+            output_json_path,
+            "-t",
+            "invalid",
+            "--env-manager",
+            "local",
+        ],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        env=env_with_tracking_uri(),
+        check=False,
+    )
+    assert prc.returncode != 0
+    assert "Unknown content type" in prc.stderr.decode("utf-8")
+
+
+def test_predict_check_input_path(iris_data, sk_model, tmp_path):
+    with mlflow.start_run():
+        mlflow.sklearn.log_model(sk_model, "model", registered_model_name="impredicting")
+    model_registry_uri = "models:/impredicting/None"
+    input_json_path = tmp_path / "input with space.json"
+    input_csv_path = tmp_path / "input.csv"
+    output_json_path = tmp_path / "output.json"
+
+    x, _ = iris_data
+    with input_json_path.open("w") as f:
+        json.dump({"dataframe_split": pd.DataFrame(x).to_dict(orient="split")}, f)
+
+    pd.DataFrame(x).to_csv(input_csv_path, index=False)
+
+    # Valid input path with space
+    prc = subprocess.run(
+        [
+            "mlflow",
+            "models",
+            "predict",
+            "-m",
+            model_registry_uri,
+            "-i",
+            f"{input_json_path}",
+            "-o",
+            output_json_path,
+            "--env-manager",
+            "local",
+        ],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        env=env_with_tracking_uri(),
+        check=False,
+        text=True,
+    )
+    assert prc.returncode == 0
+
+    # Throw errors for invalid input_path
+    prc = subprocess.run(
+        [
+            "mlflow",
+            "models",
+            "predict",
+            "-m",
+            model_registry_uri,
+            "-i",
+            f'{input_json_path}"; echo ThisIsABug! "',
+            "-o",
+            output_json_path,
+            "--env-manager",
+            "local",
+        ],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        env=env_with_tracking_uri(),
+        check=False,
+        text=True,
+    )
+    assert prc.returncode != 0
+    assert "ThisIsABug!" not in prc.stdout
+    assert "FileNotFoundError" in prc.stderr
+
+    prc = subprocess.run(
+        [
+            "mlflow",
+            "models",
+            "predict",
+            "-m",
+            model_registry_uri,
+            "-i",
+            f'{input_csv_path}"; echo ThisIsABug! "',
+            "-o",
+            output_json_path,
+            "-t",
+            "csv",
+            "--env-manager",
+            "local",
+        ],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        env=env_with_tracking_uri(),
+        check=False,
+        text=True,
+    )
+    assert prc.returncode != 0
+    assert "ThisIsABug!" not in prc.stdout
+    assert "FileNotFoundError" in prc.stderr
+
+
+def test_predict_check_output_path(iris_data, sk_model, tmp_path):
+    with mlflow.start_run():
+        mlflow.sklearn.log_model(sk_model, "model", registered_model_name="impredicting")
+    model_registry_uri = "models:/impredicting/None"
+    input_json_path = tmp_path / "input.json"
+    input_csv_path = tmp_path / "input.csv"
+    output_json_path = tmp_path / "output.json"
+
+    x, _ = iris_data
+    with input_json_path.open("w") as f:
+        json.dump({"dataframe_split": pd.DataFrame(x).to_dict(orient="split")}, f)
+
+    pd.DataFrame(x).to_csv(input_csv_path, index=False)
+
+    prc = subprocess.run(
+        [
+            "mlflow",
+            "models",
+            "predict",
+            "-m",
+            model_registry_uri,
+            "-i",
+            input_json_path,
+            "-o",
+            f'{output_json_path}"; echo ThisIsABug! "',
+            "--env-manager",
+            "local",
+        ],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        env=env_with_tracking_uri(),
+        check=False,
+        text=True,
+    )
+    assert prc.returncode == 0
+    assert "ThisIsABug!" not in prc.stdout
+
+
 def test_prepare_env_passes(sk_model):
     if no_conda:
         pytest.skip("This test requires conda.")
@@ -574,6 +743,25 @@ def test_env_manager_unsupported_value():
         )
 
 
+def test_host_invalid_value():
+    class MyModel(mlflow.pyfunc.PythonModel):
+        def predict(self, ctx, model_input):
+            return model_input
+
+    with mlflow.start_run():
+        model_info = mlflow.pyfunc.log_model(
+            python_model=MyModel(), artifact_path="test_model", registered_model_name="model"
+        )
+
+    with mock.patch("mlflow.models.cli.get_flavor_backend", return_value=PyFuncBackend({})):
+        with pytest.raises(ShellCommandException, match=r"Non-zero exit code: 1"):
+            CliRunner().invoke(
+                models_cli.serve,
+                ["--model-uri", model_info.model_uri, "--host", "localhost & echo BUG"],
+                catch_exceptions=False,
+            )
+
+
 def test_change_conda_env_root_location(tmp_path, sk_model):
     env_root1_path = tmp_path / "root1"
     env_root1_path.mkdir()

programming_language: Python