CVE-2022-1053 (published 2022-05-06T17:15Z)

Keylime does not enforce that the agent registrar data is the same when the tenant uses it for validation of the EK and identity quote and the verifier for validating the integrity quote. This allows an attacker to use one AK, EK pair from a real TPM to pass EK validation and give the verifier an AK of a software TPM. A successful attack breaks the entire chain of trust because a not validated AK is used by the verifier. This issue is worse if the validation happens first and then the agent gets added to the verifier because the timing is easier and the verifier does not validate the regcount entry being equal to 1,

Severity: MEDIUM
CVSS v2 base score: 6.4 (AV:N/AC:L/Au:N/C:P/I:P/A:N)
CVSS v3 base score: 9.1, CRITICAL (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N)

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/keylime/keylime
commit_details: repo_url: https://github.com/keylime/keylime
author: Thore Sommer
author_date: 2022-04-06 16:08:38+02:00
msg: tenant, verifier: let the tenant provide the AK and mTLS certificate

This eliminates the need for the verifier to connect to the registrar.

Signed-off-by: Thore Sommer <mail@thson.de>
repository: repo_url: https://github.com/keylime/keylime
repo_name: keylime/keylime
description: A CNCF Project to Bootstrap & Maintain Trust on the Edge / Cloud and IoT
repo_language: Python
owner: keylime
file_changes: filename: cloud_verifier_tornado.py
change_type: ModificationType.MODIFY
diff: @@ -19,7 +19,6 @@ import tornado.web
 
 from keylime import config
 from keylime import json
-from keylime import registrar_client
 from keylime.agentstates import AgentAttestStates
 from keylime.common import states, validators, retry
 from keylime.db.verifier_db import VerfierMain
@@ -444,6 +443,8 @@ class AgentsHandler(BaseHandler):
                     agent_data['accept_tpm_encryption_algs'] = json_body['accept_tpm_encryption_algs']
                     agent_data['accept_tpm_signing_algs'] = json_body['accept_tpm_signing_algs']
                     agent_data['supported_version'] = json_body['supported_version']
+                    agent_data['ak_tpm'] = json_body['ak_tpm']
+                    agent_data['mtls_cert'] = json_body.get('mtls_cert', None)
                     agent_data['hash_alg'] = ""
                     agent_data['enc_alg'] = ""
                     agent_data['sign_alg'] = ""
@@ -457,22 +458,8 @@ class AgentsHandler(BaseHandler):
                     agent_data['verifier_ip'] = config.get('cloud_verifier', 'cloudverifier_ip')
                     agent_data['verifier_port'] = config.get('cloud_verifier', 'cloudverifier_port')
 
-                    # We fetch the registrar data directly here because we require it for connecting to the agent
-                    # using mTLS
-                    registrar_client.init_client_tls('cloud_verifier')
-                    registrar_data = registrar_client.getData(config.get("cloud_verifier", "registrar_ip"),
-                                                              config.get("cloud_verifier", "registrar_port"), agent_id)
-                    if registrar_data is None:
-                        web_util.echo_json_response(self, 400,
-                                                    f"Data for agent {agent_id} could not be found in registrar!")
-                        logger.warning("Data for agent %s could not be found in registrar!", agent_id)
-                        return
-
-                    agent_data['mtls_cert'] = registrar_data.get('mtls_cert', None)
-                    agent_data['ak_tpm'] = registrar_data['aik_tpm']
-
                     # TODO: Always error for v1.0 version after initial upgrade
-                    if registrar_data.get('mtls_cert', None) is None and agent_data['supported_version'] != "1.0":
+                    if agent_data['mtls_cert'] is None and agent_data['supported_version'] != "1.0":
                         web_util.echo_json_response(self, 400, "mTLS certificate for agent is required!")
                         return
 
@@ -510,7 +497,7 @@ class AgentsHandler(BaseHandler):
 
                         # Prepare SSLContext for mTLS connections
                         agent_mtls_cert_enabled = config.getboolean('cloud_verifier', 'agent_mtls_cert_enabled', fallback=False)
-                        mtls_cert = registrar_data.get('mtls_cert', None)
+                        mtls_cert = agent_data['mtls_cert']
                         agent_data['ssl_context'] = None
                         if agent_mtls_cert_enabled and mtls_cert:
                             agent_data['ssl_context'] = web_util.generate_agent_mtls_context(mtls_cert, self.mtls_options)

programming_language: Python
filename: tenant.py
change_type: ModificationType.MODIFY
diff: @@ -631,6 +631,8 @@ class Tenant():
             'accept_tpm_hash_algs': self.accept_tpm_hash_algs,
             'accept_tpm_encryption_algs': self.accept_tpm_encryption_algs,
             'accept_tpm_signing_algs': self.accept_tpm_signing_algs,
+            'ak_tpm': self.registrar_data['aik_tpm'],
+            'mtls_cert': self.registrar_data.get('mtls_cert', None),
             'supported_version': self.supported_version,
         }
         json_message = json.dumps(data)

programming_language: Python