CVE-2023-6568 (published 2023-12-07T05:15Z)

A reflected Cross-Site Scripting (XSS) vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the Content-Type header in POST requests. An attacker can inject malicious JavaScript code into the Content-Type header, which is then improperly reflected back to the user without adequate sanitization or escaping, leading to arbitrary JavaScript execution in the context of the victim's browser. The vulnerability is present in the mlflow/server/auth/__init__.py file, where the user-supplied Content-Type header is directly injected into a Python formatted string and returned to the user, facilitating the XSS attack.

CVSS v3 base score: 6.1, MEDIUM (CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N)

Weakness information:
cwe_id: CWE-79
cwe_name: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
description: The product does not neutralize or incorrectly neutralizes user-controllable input before it is placed in output that is used as a web page that is served to other users.
extended_description: 
            Cross-site scripting (XSS) vulnerabilities occur when:
            
               Untrusted data enters a web application, typically from a web request.
               The web application dynamically generates a web page that contains this untrusted data.
               During page generation, the application does not prevent the data from containing content that is executable by a web browser, such as JavaScript, HTML tags, HTML attributes, mouse events, Flash, ActiveX, etc.
               A victim visits the generated web page through a web browser, which contains malicious script that was injected using the untrusted data.
               Since the script comes from a web page that was sent by the web server, the victim's web browser executes the malicious script in the context of the web server's domain.
               This effectively violates the intention of the web browser's same-origin policy, which states that scripts in one domain should not be able to access resources or run code in a different domain.
            
            There are three main kinds of XSS:
            
               
                  Type 1: Reflected XSS (or Non-Persistent) - 
         			The server reads data directly from the HTTP request and reflects it back in the HTTP response. Reflected XSS exploits occur when an attacker causes a victim to supply dangerous content to a vulnerable web application, which is then reflected back to the victim and executed by the web browser. The most common mechanism for delivering malicious content is to include it as a parameter in a URL that is posted publicly or e-mailed directly to the victim. URLs constructed in this manner constitute the core of many phishing schemes, whereby an attacker convinces a victim to visit a URL that refers to a vulnerable site. After the site reflects the attacker's content back to the victim, the content is executed by the victim's browser.
               
                  Type 2: Stored XSS (or Persistent) - 
               The application stores dangerous data in a database, message forum, visitor log, or other trusted data store. At a later time, the dangerous data is subsequently read back into the application and included in dynamic content. From an attacker's perspective, the optimal place to inject malicious content is in an area that is displayed to either many users or particularly interesting users. Interesting users typically have elevated privileges in the application or interact with sensitive data that is valuable to the attacker. If one of these users executes malicious content, the attacker may be able to perform privileged operations on behalf of the user or gain access to sensitive data belonging to the user. For example, the attacker might inject XSS into a log message, which might not be handled properly when an administrator views the logs.
            
               
                  Type 0: DOM-Based XSS - 
               In DOM-based XSS, the client performs the injection of XSS into the page; in the other types, the server performs the injection. DOM-based XSS generally involves server-controlled, trusted script that is sent to the client, such as Javascript that performs sanity checks on a form before the user submits it. If the server-supplied script processes user-supplied data and then injects it back into the web page (such as with dynamic HTML), then DOM-based XSS is possible.
            
            
            Once the malicious script is injected, the attacker can perform a variety of malicious activities. The attacker could transfer private information, such as cookies that may include session information, from the victim's machine to the attacker. The attacker could send malicious requests to a web site on behalf of the victim, which could be especially dangerous to the site if the victim has administrator privileges to manage that site. Phishing attacks could be used to emulate trusted web sites and trick the victim into entering a password, allowing the attacker to compromise the victim's account on that web site. Finally, the script could exploit a vulnerability in the web browser itself possibly taking over the victim's machine, sometimes referred to as "drive-by hacking."
            In many cases, the attack can be launched without the victim even being aware of it. Even with careful users, attackers frequently use a variety of methods to encode the malicious portion of the attack, such as URL encoding or Unicode, so the request looks less suspicious.
         
         

Fix information:
repo_url: https://github.com/mlflow/mlflow
commit_details: repo_url: https://github.com/mlflow/mlflow
author: Yuki Watanabe
author_date: 2023-11-30 09:38:30+09:00
msg: Add Content-Type header validation (#10526)

Signed-off-by: B-Step62 <yuki.watanabe@databricks.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: rest-api.rst
change_type: ModificationType.MODIFY
diff: @@ -12,6 +12,8 @@ The API is hosted under the ``/api`` route on the MLflow tracking server. For ex
 experiments on a tracking server hosted at ``http://localhost:5000``, access
 ``http://localhost:5000/api/2.0/mlflow/users/create``.
 
+.. important::
+    The MLflow REST API requires content type ``application/json`` for all POST requests.
 
 .. contents:: Table of Contents
     :local:

programming_language: Markdown
filename: rest-api.rst
change_type: ModificationType.MODIFY
diff: @@ -11,6 +11,9 @@ The API is hosted under the ``/api`` route on the MLflow tracking server. For ex
 experiments on a tracking server hosted at ``http://localhost:5000``, make a POST request to
 ``http://localhost:5000/api/2.0/mlflow/experiments/search``.
 
+.. important::
+    The MLflow REST API requires content type ``application/json`` for all POST requests.
+
 .. contents:: Table of Contents
     :local:
     :depth: 1
@@ -18,7 +21,6 @@ experiments on a tracking server hosted at ``http://localhost:5000``, make a POS
 ===========================
 
 
-
 .. _mlflowMlflowServicecreateExperiment:
 
 Create Experiment

programming_language: Markdown
filename: __init__.py
change_type: ModificationType.MODIFY
diff: @@ -762,7 +762,11 @@ def create_user():
         user = store.create_user(username, password)
         return make_response({"user": user.to_json()})
     else:
-        return make_response(f"Invalid content type: '{content_type}'", 400)
+        message = (
+            "Invalid content type. Must be one of: "
+            "application/x-www-form-urlencoded, application/json"
+        )
+        return make_response(message, 400)
 
 
 @catch_mlflow_exception

programming_language: Python
filename: handlers.py
change_type: ModificationType.MODIFY
diff: @@ -91,6 +91,7 @@ from mlflow.protos.service_pb2 import (
     UpdateExperiment,
     UpdateRun,
 )
+from mlflow.server.validation import _validate_content_type
 from mlflow.store.artifact.artifact_repo import MultipartUploadMixin
 from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository
 from mlflow.store.db.db_types import DATABASE_ENGINES
@@ -403,6 +404,7 @@ def _validate_param_against_schema(schema, param, value, proto_parsing_succeeded
 
 
 def _get_request_json(flask_request=request):
+    _validate_content_type(flask_request, ["application/json"])
     return flask_request.get_json(force=True, silent=True)
 
 
@@ -1112,6 +1114,7 @@ def get_metric_history_bulk_handler():
 @_disable_if_artifacts_only
 def search_datasets_handler():
     MAX_EXPERIMENT_IDS_PER_REQUEST = 20
+    _validate_content_type(request, ["application/json"])
     experiment_ids = request.json.get("experiment_ids", [])
     if not experiment_ids:
         raise MlflowException(
@@ -1179,6 +1182,8 @@ def create_promptlab_run_handler():
                 error_code=INVALID_PARAMETER_VALUE,
             )
 
+    _validate_content_type(request, ["application/json"])
+
     args = request.json
     experiment_id = args.get("experiment_id")
     assert_arg_exists("experiment_id", experiment_id)

programming_language: Python
filename: validation.py
change_type: ModificationType.ADD
diff: @@ -0,0 +1,31 @@
+from typing import List
+
+from mlflow.exceptions import MlflowException
+from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
+
+
+def _validate_content_type(flask_request, allowed_content_types: List[str]):
+    """
+    Validates that the request content type is one of the allowed content types.
+
+    :param flask_request: Flask request object (flask.request)
+    :param allowed_content_types: A list of allowed content types
+    """
+    if flask_request.method not in ["POST", "PUT"]:
+        return
+
+    if flask_request.content_type is None:
+        raise MlflowException(
+            message="Bad Request. Content-Type header is missing.",
+            error_code=INVALID_PARAMETER_VALUE,
+        )
+
+    # Remove any parameters e.g. "application/json; charset=utf-8" -> "application/json"
+    content_type = flask_request.content_type.split(";")[0]
+    if content_type not in allowed_content_types:
+        message = f"Bad Request. Content-Type must be one of {allowed_content_types}."
+
+        raise MlflowException(
+            message=message,
+            error_code=INVALID_PARAMETER_VALUE,
+        )

programming_language: Python
filename: test_handlers.py
change_type: ModificationType.MODIFY
diff: @@ -156,6 +156,7 @@ def test_all_model_registry_endpoints_available():
 def test_can_parse_json():
     request = mock.MagicMock()
     request.method = "POST"
+    request.content_type = "application/json"
     request.get_json = mock.MagicMock()
     request.get_json.return_value = {"name": "hello"}
     msg = _get_request_message(CreateExperiment(), flask_request=request)
@@ -165,12 +166,23 @@ def test_can_parse_json():
 def test_can_parse_post_json_with_unknown_fields():
     request = mock.MagicMock()
     request.method = "POST"
+    request.content_type = "application/json"
     request.get_json = mock.MagicMock()
     request.get_json.return_value = {"name": "hello", "WHAT IS THIS FIELD EVEN": "DOING"}
     msg = _get_request_message(CreateExperiment(), flask_request=request)
     assert msg.name == "hello"
 
 
+def test_can_parse_post_json_with_content_type_params():
+    request = mock.MagicMock()
+    request.method = "POST"
+    request.content_type = "application/json; charset=utf-8"
+    request.get_json = mock.MagicMock()
+    request.get_json.return_value = {"name": "hello"}
+    msg = _get_request_message(CreateExperiment(), flask_request=request)
+    assert msg.name == "hello"
+
+
 def test_can_parse_get_json_with_unknown_fields():
     request = mock.MagicMock()
     request.method = "GET"
@@ -184,12 +196,33 @@ def test_can_parse_get_json_with_unknown_fields():
 def test_can_parse_json_string():
     request = mock.MagicMock()
     request.method = "POST"
+    request.content_type = "application/json"
     request.get_json = mock.MagicMock()
     request.get_json.return_value = '{"name": "hello2"}'
     msg = _get_request_message(CreateExperiment(), flask_request=request)
     assert msg.name == "hello2"
 
 
+def test_can_block_post_request_with_invalid_content_type():
+    request = mock.MagicMock()
+    request.method = "POST"
+    request.content_type = "text/plain"
+    request.get_json = mock.MagicMock()
+    request.get_json.return_value = {"name": "hello"}
+    with pytest.raises(MlflowException, match=r"Bad Request. Content-Type"):
+        _get_request_message(CreateExperiment(), flask_request=request)
+
+
+def test_can_block_post_request_with_missing_content_type():
+    request = mock.MagicMock()
+    request.method = "POST"
+    request.content_type = None
+    request.get_json = mock.MagicMock()
+    request.get_json.return_value = {"name": "hello"}
+    with pytest.raises(MlflowException, match=r"Bad Request. Content-Type"):
+        _get_request_message(CreateExperiment(), flask_request=request)
+
+
 def test_search_runs_default_view_type(mock_get_request_message, mock_tracking_store):
     """
     Search Runs default view type is filled in as ViewType.ACTIVE_ONLY

programming_language: Python