CVE-2014-3498 (published 2017-06-08T18:29Z)

The user module in ansible before 1.6.6 allows remote authenticated users to execute arbitrary commands.

Severity: MEDIUM
CVSS v2 base score: 6.5 (AV:N/AC:L/Au:S/C:P/I:P/A:P)
CVSS v3 base score: 8.8, HIGH (CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)

Weakness information:
cwe_id: CWE-20
cwe_name: Improper Input Validation
description: The product receives input or data, but it does
        not validate or incorrectly validates that the input has the
        properties that are required to process the data safely and
        correctly.
extended_description: 
	   Input validation is a frequently-used technique
	   for checking potentially dangerous inputs in order to
	   ensure that the inputs are safe for processing within the
	   code, or when communicating with other components.  When
	   software does not validate input properly, an attacker is
	   able to craft the input in a form that is not expected by
	   the rest of the application. This will lead to parts of the
	   system receiving unintended input, which may result in
	   altered control flow, arbitrary control of a resource, or
	   arbitrary code execution.
	   Input validation is not the only technique for
	   processing input, however.  Other techniques attempt to
	   transform potentially-dangerous input into something safe, such
	   as filtering (CWE-790) - which attempts to remove dangerous
	   inputs - or encoding/escaping (CWE-116), which attempts to
	   ensure that the input is not misinterpreted when it is included
	   in output to another component. Other techniques exist as well
	   (see CWE-138 for more examples.)
	   Input validation can be applied to:
	   
	     raw data - strings, numbers, parameters, file contents, etc.
	     metadata - information about the raw data, such as headers or size
	   
	   Data can be simple or structured.  Structured data
	   can be composed of many nested layers, composed of
	   combinations of metadata and raw data, with other simple or
	   structured data.
	   Many properties of raw data or metadata may need
	   to be validated upon entry into the code, such
	   as:
	   
	     specified quantities such as size, length, frequency, price, rate, number of operations, time, etc.
	     implied or derived quantities, such as the actual size of a file instead of a specified size
	     indexes, offsets, or positions into more complex data structures
	     symbolic keys or other elements into hash tables, associative arrays, etc.
	     well-formedness, i.e. syntactic correctness - compliance with expected syntax 
	     lexical token correctness - compliance with rules for what is treated as a token
	     specified or derived type - the actual type of the input (or what the input appears to be)
	     consistency - between individual data elements, between raw data and metadata, between references, etc.
	     conformance to domain-specific rules, e.g. business logic 
	     equivalence - ensuring that equivalent inputs are treated the same
	     authenticity, ownership, or other attestations about the input, e.g. a cryptographic signature to prove the source of the data
	   
	   Implied or derived properties of data must often
	   be calculated or inferred by the code itself.  Errors in
	   deriving properties may be considered a contributing factor
	   to improper input validation.
	   
	   Note that "input validation" has very different
	   meanings to different people, or within different
	   classification schemes.  Caution must be used when
	   referencing this CWE entry or mapping to it.  For example,
	   some weaknesses might involve inadvertently giving control
	   to an attacker over an input when they should not be able
	   to provide an input at all, but sometimes this is referred
	   to as input validation.
	   Finally, it is important to emphasize that the
	   distinctions between input validation and output escaping
	   are often blurred, and developers must be careful to
	   understand the difference, including how input validation
	   is not always sufficient to prevent vulnerabilities,
	   especially when less stringent data types must be
	   supported, such as free-form text. Consider a SQL injection
	   scenario in which a person's last name is inserted into a
	   query. The name "O'Reilly" would likely pass the validation
	   step since it is a common last name in the English
	   language. However, this valid name cannot be directly
	   inserted into the database because it contains the "'"
	   apostrophe character, which would need to be escaped or
	   otherwise transformed. In this case, removing the
	   apostrophe might reduce the risk of SQL injection, but it
	   would produce incorrect behavior because the wrong name
	   would be recorded.
       
         

Fix information:
repo_url: https://github.com/ansible/ansible
commit_details: repo_url: https://github.com/ansible/ansible
author: James Cammarata
author_date: 2014-06-30 15:59:32-05:00
msg: When parsing json from untrusted sources, remove templating tags
repository: repo_url: https://github.com/ansible/ansible
repo_name: ansible/ansible
description: Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com.
repo_language: Python
owner: ansible
file_changes: filename: script.py
change_type: ModificationType.MODIFY
diff: @@ -49,7 +49,7 @@ class InventoryScript(object):
     def _parse(self, err):
 
         all_hosts = {}
-        self.raw  = utils.parse_json(self.data)
+        self.raw  = utils.parse_json(self.data, from_remote=True)
         all       = Group('all')
         groups    = dict(all=all)
         group     = None

programming_language: Python
filename: __init__.py
change_type: ModificationType.MODIFY
diff: @@ -497,7 +497,7 @@ class Runner(object):
                 cmd2 = conn.shell.remove(tmp, recurse=True)
                 self._low_level_exec_command(conn, cmd2, tmp, sudoable=False)
 
-        data = utils.parse_json(res['stdout'])
+        data = utils.parse_json(res['stdout'], from_remote=True)
         if 'parsed' in data and data['parsed'] == False:
             data['msg'] += res['stderr']
         return ReturnData(conn=conn, result=data)

programming_language: Python
filename: return_data.py
change_type: ModificationType.MODIFY
diff: @@ -43,8 +43,7 @@ class ReturnData(object):
         self.diff = diff
 
         if type(self.result) in [ str, unicode ]:
-            self.result = utils.parse_json(self.result)
-
+            self.result = utils.parse_json(self.result, from_remote=True)
 
         if self.host is None:
             raise Exception("host not set")

programming_language: Python
filename: __init__.py
change_type: ModificationType.MODIFY
diff: @@ -313,7 +313,38 @@ def json_loads(data):
 
     return json.loads(data)
 
-def parse_json(raw_data):
+def _clean_data(orig_data):
+    ''' remove template tags from a string '''
+    data = orig_data
+    if isinstance(orig_data, basestring):
+        for pattern,replacement in (('{{','{#'), ('}}','#}'), ('{%','{#'), ('%}','#}')):
+            data = data.replace(pattern, replacement)
+    return data
+
+def _clean_data_struct(orig_data):
+    '''
+    walk a complex data structure, and use _clean_data() to
+    remove any template tags that may exist
+    '''
+    if isinstance(orig_data, dict):
+        data = orig_data.copy()
+        for key in data:
+            new_key = _clean_data_struct(key)
+            new_val = _clean_data_struct(data[key])
+            if key != new_key:
+                del data[key]
+            data[new_key] = new_val
+    elif isinstance(orig_data, list):
+        data = orig_data[:]
+        for i in range(0, len(data)):
+            data[i] = _clean_data_struct(data[i])
+    elif isinstance(orig_data, basestring):
+        data = _clean_data(orig_data)
+    else:
+        data = orig_data
+    return data
+
+def parse_json(raw_data, from_remote=False):
     ''' this version for module return data only '''
 
     orig_data = raw_data
@@ -322,7 +353,7 @@ def parse_json(raw_data):
     data = filter_leading_non_json_lines(raw_data)
 
     try:
-        return json.loads(data)
+        results = json.loads(data)
     except:
         # not JSON, but try "Baby JSON" which allows many of our modules to not
         # require JSON and makes writing modules in bash much simpler
@@ -332,7 +363,6 @@ def parse_json(raw_data):
         except:
             print "failed to parse json: "+ data
             raise
-
         for t in tokens:
             if "=" not in t:
                 raise errors.AnsibleError("failed to parse: %s" % orig_data)
@@ -347,7 +377,11 @@ def parse_json(raw_data):
             results[key] = value
         if len(results.keys()) == 0:
             return { "failed" : True, "parsed" : False, "msg" : orig_data }
-        return results
+
+    if from_remote:
+        results = _clean_data_struct(results)
+
+    return results
 
 def smush_braces(data):
     ''' smush Jinaj2 braces so unresolved templates like {{ foo }} don't get parsed weird by key=value code '''

programming_language: Python
filename: template.py
change_type: ModificationType.MODIFY
diff: @@ -80,7 +80,6 @@ class Flags:
 
 FILTER_PLUGINS = None
 _LISTRE = re.compile(r"(\w+)\[(\d+)\]")
-JINJA2_OVERRIDE='#jinja2:'
 
 def lookup(name, *args, **kwargs):
     from ansible import utils
@@ -231,16 +230,6 @@ def template_from_file(basedir, path, vars, vault_password=None):
     except:
         raise errors.AnsibleError("unable to read %s" % realpath)
 
-
-    # Get jinja env overrides from template
-    if data.startswith(JINJA2_OVERRIDE):
-        eol = data.find('\n')
-        line = data[len(JINJA2_OVERRIDE):eol]
-        data = data[eol+1:]
-        for pair in line.split(','):
-            (key,val) = pair.split(':')
-            setattr(environment,key.strip(),ast.literal_eval(val.strip()))
-
     environment.template_class = J2Template
     try:
         t = environment.from_string(data)

programming_language: Python