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form-data uses unsafe random function in form-data for choosing boundary

Critical severity GitHub Reviewed Published Jul 18, 2025 in form-data/form-data • Updated Jul 21, 2025

Package

npm form-data (npm)

Affected versions

< 2.5.4
>= 3.0.0, < 3.0.4
>= 4.0.0, < 4.0.4

Patched versions

2.5.4
3.0.4
4.0.4

Description

Summary

form-data uses Math.random() to select a boundary value for multipart form-encoded data. This can lead to a security issue if an attacker:

  1. can observe other values produced by Math.random in the target application, and
  2. can control one field of a request made using form-data

Because the values of Math.random() are pseudo-random and predictable (see: https://blog.securityevaluators.com/hacking-the-javascript-lottery-80cc437e3b7f), an attacker who can observe a few sequential values can determine the state of the PRNG and predict future values, includes those used to generate form-data's boundary value. The allows the attacker to craft a value that contains a boundary value, allowing them to inject additional parameters into the request.

This is largely the same vulnerability as was recently found in undici by parrot409 -- I'm not affiliated with that researcher but want to give credit where credit is due! My PoC is largely based on their work.

Details

The culprit is this line here: https://github.com/form-data/form-data/blob/426ba9ac440f95d1998dac9a5cd8d738043b048f/lib/form_data.js#L347

An attacker who is able to predict the output of Math.random() can predict this boundary value, and craft a payload that contains the boundary value, followed by another, fully attacker-controlled field. This is roughly equivalent to any sort of improper escaping vulnerability, with the caveat that the attacker must find a way to observe other Math.random() values generated by the application to solve for the state of the PRNG. However, Math.random() is used in all sorts of places that might be visible to an attacker (including by form-data itself, if the attacker can arrange for the vulnerable application to make a request to an attacker-controlled server using form-data, such as a user-controlled webhook -- the attacker could observe the boundary values from those requests to observe the Math.random() outputs). A common example would be a x-request-id header added by the server. These sorts of headers are often used for distributed tracing, to correlate errors across the frontend and backend. Math.random() is a fine place to get these sorts of IDs (in fact, opentelemetry uses Math.random for this purpose)

PoC

PoC here: https://github.com/benweissmann/CVE-2025-7783-poc

Instructions are in that repo. It's based on the PoC from https://hackerone.com/reports/2913312 but simplified somewhat; the vulnerable application has a more direct side-channel from which to observe Math.random() values (a separate endpoint that happens to include a randomly-generated request ID).

Impact

For an application to be vulnerable, it must:

  • Use form-data to send data including user-controlled data to some other system. The attacker must be able to do something malicious by adding extra parameters (that were not intended to be user-controlled) to this request. Depending on the target system's handling of repeated parameters, the attacker might be able to overwrite values in addition to appending values (some multipart form handlers deal with repeats by overwriting values instead of representing them as an array)
  • Reveal values of Math.random(). It's easiest if the attacker can observe multiple sequential values, but more complex math could recover the PRNG state to some degree of confidence with non-sequential values.

If an application is vulnerable, this allows an attacker to make arbitrary requests to internal systems.

References

@ljharb ljharb published to form-data/form-data Jul 18, 2025
Published by the National Vulnerability Database Jul 18, 2025
Published to the GitHub Advisory Database Jul 21, 2025
Reviewed Jul 21, 2025
Last updated Jul 21, 2025

Severity

Critical

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 High
Attack Requirements None
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability None
Subsequent System Impact Metrics
Confidentiality High
Integrity High
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:H/AT:N/PR:N/UI:N/VC:H/VI:H/VA:N/SC:H/SI:H/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.
(17th percentile)

Weaknesses

Use of Insufficiently Random Values

The product uses insufficiently random numbers or values in a security context that depends on unpredictable numbers. Learn more on MITRE.

CVE ID

CVE-2025-7783

GHSA ID

GHSA-fjxv-7rqg-78g4

Source code

Credits

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