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Pimcore Platform - SQL Injection in DataObject composite index handling during class definition import/save

High severity GitHub Reviewed Published May 26, 2026 in pimcore/pimcore

Package

composer pimcore/pimcore (Composer)

Affected versions

<= 12.3.6

Patched versions

12.3.7

Description

Description

An authenticated administrative user who can import or save DataObject class definitions can inject attacker-controlled composite index metadata and trigger unintended SQL execution in the backend.

The vulnerable flow accepts compositeIndices from imported JSON, stores the values without strict validation, and later concatenates them directly into ALTER TABLE ... DROP INDEX and ALTER TABLE ... ADD INDEX statements executed through Doctrine DBAL.

Although the original report focused on compositeIndices.index_key, independent code review shows that the strongest and most reliable injection point is compositeIndices.index_columns, because it is inserted verbatim inside the ADD INDEX (...) clause. This permits injection of additional ALTER TABLE subclauses against Pimcore object tables without relying on stacked queries.

Vulnerability

Root cause

  1. Source:
    • Pimcore\Model\DataObject\ClassDefinition\Service::importClassDefinitionFromJson() accepts compositeIndices directly from imported JSON.
  2. Assignment:
    • Pimcore\Model\DataObject\ClassDefinition::setCompositeIndices() does not enforce an allowlist for index names or column names.
    • The only special handling is a ManyToOne relation rewrite to __id and __type, which is not a security control.
  3. Sink:
    • Pimcore\Model\DataObject\Traits\CompositeIndexTrait::updateCompositeIndices() builds raw SQL with string concatenation and executes it via $this->db->executeQuery(...).
  4. Missing protection:
    • quoteIdentifier() is used for the SHOW INDEXES query, but not for the dynamic ALTER TABLE statements.
    • No server-side schema validation restricts index_key or index_columns to known safe identifier characters.

Confirmed source-to-sink path

  1. importClassDefinitionFromJson() decodes attacker-controlled JSON and forwards compositeIndices.
  2. setCompositeIndices() stores those values without sanitizing identifier content.
  3. ClassDefinition::save() reaches ClassDefinition\Dao::update().
  4. Dao::update() calls updateCompositeIndices() for:
    • object_store_<classId>
    • object_query_<classId>
  5. Localizedfield\Dao also calls updateCompositeIndices() for:
    • localized query tables
    • localized store tables

Why this is exploitable

The vulnerable ADD INDEX statement is built as:

'ALTER TABLE `'.$table.'` ADD INDEX `' . $key.'` ('.$columnName.');'

$columnName is produced from implode(',', $columns) and is not quoted or validated. A malicious index_columns element such as:

slider), DROP COLUMN `oo_className` --

produces SQL of the form:

ALTER TABLE `object_query_<id>` ADD INDEX `c_poc_idx` (slider), DROP COLUMN `oo_className` -- );

This remains a single ALTER TABLE statement, so the base vulnerability does not depend on multi-statement support. The attacker can inject additional DDL clauses affecting the target Pimcore object table.

Impact

The issue allows a privileged attacker to alter backend SQL behavior during class-definition import/save and modify schema on Pimcore object tables associated with the affected class.

Practical impact includes:

  • unauthorized schema modification on object query/store tables
  • backend denial of service by breaking expected table layout
  • data integrity impact for DataObject storage and queries

index_key is also concatenated into SQL without proper identifier escaping, but the most defensible exploitation path is through index_columns.

Relevant code:

  • models/DataObject/ClassDefinition/Service.php:92-137
  • models/DataObject/ClassDefinition.php:994-1006
  • models/DataObject/Traits/CompositeIndexTrait.php:30-85
  • models/DataObject/ClassDefinition/Dao.php:217-218
  • models/DataObject/Localizedfield/Dao.php:945-951

PoC

Application-level PoC

Preconditions:

  • valid authenticated administrative session
  • ability to import or save a class definition containing compositeIndices

The original report reproduced the issue through an authenticated Studio endpoint:

POST /pimcore-studio/api/class/definition/configuration-view/detail/1/import

Minimal malicious JSON fragment:

{
  "compositeIndices": [
    {
      "index_key": "poc_idx",
      "index_type": "query",
      "index_columns": [
        "slider), DROP COLUMN `oo_className` -- "
      ]
    }
  ]
}

Reproduction:

  1. Authenticate as an administrator with permission to manage/import class definitions.
  2. Export an existing class definition or prepare a valid class-definition JSON document.
  3. Replace only the compositeIndices section with the payload above.
  4. Import the modified definition or save the class through the administrative workflow.

Expected result:

  • Pimcore reaches updateCompositeIndices() during class save/import.
  • The backend executes an attacker-influenced ALTER TABLE statement against the target object table.
  • The affected class table is modified unexpectedly, for example by dropping a column or otherwise changing schema.

Minimal source-level confirmation

The behavior is directly visible from the code path:

$newIndicesMap['c_' . $key] = implode(',', $columns);
$columnName = $newIndicesMap[$key];
$this->db->executeQuery(
    'ALTER TABLE `'.$table.'` ADD INDEX `' . $key.'` ('.$columnName.');'
);

No escaping or allowlist validation is applied to $columns before they are interpolated into SQL.

Evidence of Exploitation

  • Video of exploitation:

https://github.com/user-attachments/assets/64a49147-12a5-4550-ba22-cb4383523557

  • Static evidence:

Dragons-img

System Information

Pimcore Platform
Version v12.3.3
Database layer: doctrine/dbal ^4.4
Operating System: Any

Resources

Github Repository: https://github.com/pimcore/pimcore
Security: https://github.com/pimcore/pimcore/security

This issue was discovered by Oscar Uribe, Security Researcher at Fluid Attacks, who reached out to Pimcore.

As part of standard disclosure measures, Fluid Attacks follows a timeline (outlined at https://fluidattacks.com/advisories/policy) that is aligned with ISO/IEC 29147:2018 and ISO/IEC 30111:2019. In short, the timeline works as follows: Fluid Attacks requests acknowledgment of the report within a few days of project maintainers first accessing it, and from there, Fluid Attacks is glad to coordinate a joint disclosure date with the affected party, typically within 90 days of the initial discovery. This allows the maintainers' team reasonable time to assess, develop, and release a fix.

The CVE ID "CVE-2026-5394" has been reserved for this issue, and the advisory will eventually be published at https://fluidattacks.com/advisories/dragons.

References

@kingjia90 kingjia90 published to pimcore/pimcore May 26, 2026
Published to the GitHub Advisory Database May 28, 2026
Reviewed May 28, 2026

Severity

High

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 High
User interaction None
Vulnerable System Impact Metrics
Confidentiality High
Integrity Low
Availability Low
Subsequent System Impact Metrics
Confidentiality None
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:H/UI:N/VC:H/VI:L/VA:L/SC:N/SI:N/SA:N

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(1st percentile)

Weaknesses

Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')

The product constructs all or part of an SQL command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended SQL command when it is sent to a downstream component. Without sufficient removal or quoting of SQL syntax in user-controllable inputs, the generated SQL query can cause those inputs to be interpreted as SQL instead of ordinary user data. Learn more on MITRE.

CVE ID

CVE-2026-5394

GHSA ID

GHSA-r2f4-ff2p-xc64

Source code

Credits

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