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PYTHON-5990 Forced Unix domain socket connection via a .sock KMS endpoint in client-side field level encryption

Moderate
Jibola published GHSA-qx36-8mw2-4r3x Sep 24, 2026

Package

pip pymongo (pip)

Affected versions

>= 3.9.0, <= 4.18.1

Patched versions

4.18.2

Description

Summary

PyMongo passed the KMS endpoint of a data key verbatim into parse_host(), which returns any string ending in .sock unchanged instead of validating it as a hostname and port. The driver's connection code then treats such an address as a Unix domain socket path and connects to it with AF_UNIX. Because the endpoint originates from masterKey.endpoint in a key vault document, a party who can write to the key vault could redirect the driver's KMS connection to an arbitrary Unix domain socket path on the application host.

Impact

An application using client-side field level encryption (CSFLE) or Queryable Encryption is affected if an attacker can write to its key vault collection. Setting masterKey.endpoint on a data key to a .sock-suffixed string causes the next KMS request for that key (key cache TTL is ~60 seconds) to open an AF_UNIX connection to the attacker-chosen filesystem path from inside the victim application process. The documented custom KMS endpoint feature supports TCP hosts only, so this crosses a boundary the feature was never intended to allow.

Impact is limited to the side effects of the connection itself. The socket is still wrapped in a verifying TLS context using the .sock string as server_hostname, and insecure KMS TLS options are rejected, so the handshake always fails and the KMS message is never sent. The attacker controls the connect target but not the transmitted bytes (a fixed TLS ClientHello).

Applications that do not use CSFLE or Queryable Encryption are not affected. Applications whose key vault is not writable by untrusted parties are not affected.

Patches

Fixed in PyMongo TBD _EncryptionIO.kms_request now rejects a .sock-suffixed KMS endpoint with pymongo.errors.ConfigurationError immediately after parsing, before any connection is attempted, on both the synchronous and asynchronous paths. No application code changes are required beyond upgrading.

Workarounds

If you cannot upgrade immediately:

  • Restrict write access to the key vault collection to trusted principals only. This is the recommended configuration regardless of this issue.
  • Validate masterKey.endpoint on data keys you create, and audit existing key vault documents for endpoints ending in .sock.

Details

  • _EncryptionIO.fetch_keys reads key vault documents from the server and hands them to libmongocrypt, which surfaces the stored masterKey.endpoint verbatim as kms_context.endpoint.
  • _EncryptionIO.kms_request passed that string to parse_host(endpoint, 443). parse_host returns entities ending in .sock verbatim, skipping the hostname and port validation applied to every other input.
  • _create_connection (and the async equivalent) checks host.endswith(".sock") and performs an AF_UNIX sock.connect(host), treating the string as a filesystem path.

References

  • Fix commit: 10d9634d -- "SECBUG-4279 Reject Unix domain socket KMS endpoints"
  • PYTHON-5990
  • [Link to CVE once assigned]

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required Low
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity None
Availability None
Subsequent System Impact Metrics
Confidentiality Low
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the 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 system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:N/SC:L/SI:N/SA:N

CVE ID

CVE-2026-96747

Weaknesses

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. Learn more on MITRE.

Server-Side Request Forgery (SSRF)

The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination. Learn more on MITRE.