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Mass Assignment in AdonisJS Lucid Allows Overwriting Internal ORM State

High severity GitHub Reviewed Published Jan 13, 2026 in adonisjs/lucid • Updated Jan 13, 2026

Package

npm @adonisjs/lucid (npm)

Affected versions

<= 21.8.1
>= 22.0.0-next.0, < 22.0.0-next.6

Patched versions

21.8.2
22.0.0-next.6

Description

Summary

Description
A Mass Assignment (CWE-915) vulnerability in AdonisJS Lucid may allow a remote attacker who can influence data that is passed into Lucid model assignments to overwrite the internal ORM state. This may lead to logic bypasses and unauthorized record modification within a table or model. This affects @adonisjs/lucid through version 21.8.1 and 22.x pre-release versions prior to 22.0.0-next.6. This has been patched in @adonisjs/lucid versions 21.8.2 and 22.0.0-next.6.

Details

A vulnerability in the BaseModelImpl class of @adonisjs/lucid may allow an attacker to overwrite internal class properties (such as $isPersisted, $attributes, or $isDeleted) when passing plain objects to model assignment methods.

The library relies on a this.hasOwnProperty(key) check to validate assignment targets. However, because internal ORM state properties are initialized as instance properties, they pass this check. Consequently, if an attacker can influence specific keys (like $isPersisted) into the payload passed to merge() or $consumeAdapterResult(), they can hijack the ORM's internal logic.

The exposed internal properties include:

  • $attributes: The raw storage for model data.
  • $isPersisted: Controls whether save() performs an INSERT or an UPDATE.
  • $original: Stores the original state of the record used to calculate changes.
  • $isDeleted: Prevents operations on deleted models.

This issue propagates to the entire write surface of the library, including:

  • Instance methods fill and merge.
  • Single record creation methods create, createQuietly, firstOrNew, and firstOrCreate.
  • Conditional updates via updateOrCreate.
  • Bulk operations createMany, createManyQuietly, fetchOrNewUpMany, fetchOrCreateMany, and updateOrCreateMany.

Impact

Applications are vulnerable if they pass unvalidated data or validated data that retains unknown properties to the model. This occurs because internal keys exist as instance properties, causing them to pass the hasOwnProperty check and bypass Lucid's default rejection of unknown properties.

Applications utilizing strict allow lists for input validation that discard unknown properties are not affected.

For example, if a developer passes request.all(), request.except() or a schema with allowUnknownProperties to Model.create(), the ORM's internal logic can be hijacked. Because the Model.create() > save() decision is based on $isPersisted, and merge() can assign to the own-property $isPersisted, an attacker who can inject "$isPersisted": true into the payload can force save() to take the UPDATE branch rather than the INSERT branch, while setting $attributes can bypass validators or field restrictions.

Patches

This issue has been patched in @adonisjs/lucid version 21.8.2 and 22.0.0-next.6. Please upgrade to this version or later.

Developers can mitigate this issue by strictly validating model inputs with an allow list that drops unknown keys if possible.

References

@RomainLanz RomainLanz published to adonisjs/lucid Jan 13, 2026
Published by the National Vulnerability Database Jan 13, 2026
Published to the GitHub Advisory Database Jan 13, 2026
Reviewed Jan 13, 2026
Last updated Jan 13, 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 Present
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity High
Availability None
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:P/PR:N/UI:N/VC:N/VI:H/VA:N/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.
(26th percentile)

Weaknesses

Improperly Controlled Modification of Dynamically-Determined Object Attributes

The product receives input from an upstream component that specifies multiple attributes, properties, or fields that are to be initialized or updated in an object, but it does not properly control which attributes can be modified. Learn more on MITRE.

CVE ID

CVE-2026-22814

GHSA ID

GHSA-g5gc-h5hp-555f

Source code

Credits

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