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wisp has Allocation of Resources Without Limits or Throttling

High severity GitHub Reviewed Published Apr 1, 2026 in gleam-wisp/wisp • Updated Apr 3, 2026

Package

erlang wisp (Erlang)

Affected versions

< 2.2.2

Patched versions

2.2.2

Description

Summary

A multipart form parsing bug allows any unauthenticated user to bypass configured request size limits and trigger a denial of service by exhausting server memory or disk.

Details

The issue is in the multipart parsing logic, specifically in multipart_body and multipart_headers.

When parsing multipart data, the implementation distinguishes between:

  • chunks where a boundary is found
  • chunks where more data is required

In the normal case (boundary found), the parser correctly accounts for consumed bytes by calling decrement_quota.

However, in the MoreRequiredForBody branch, the parser appends incoming data to the output but recurses without decrementing the quota. This means that any chunk that does not contain the multipart boundary is effectively “free” from a quota perspective. Only the final chunk, the one containing the boundary, is counted.

The same pattern exists in multipart_headers, where MoreRequiredForHeaders also recurses without decrementing the quota.

As a result, an attacker can send arbitrarily large multipart bodies split across many chunks that avoid the boundary. The parser will accumulate the data (in memory for form fields, on disk for file uploads) without enforcing max_body_size or max_files_size.

Impact

This is a denial of service vulnerability caused by uncontrolled resource consumption.

Any application using require_form or require_multipart_form on user-controlled input is affected. An unauthenticated attacker can send large multipart requests that bypass configured limits and cause:

  • memory exhaustion (for form fields accumulated in memory)
  • disk exhaustion (for file uploads written to temporary storage)

In both cases, the application may become unavailable or be terminated by the operating system.

Workaround

Deploy a reverse proxy (such as nginx or HAProxy) in front of the application and enforce request body size limits there. This ensures large multipart requests are rejected before they reach the vulnerable parser.

Resources

References

@lpil lpil published to gleam-wisp/wisp Apr 1, 2026
Published by the National Vulnerability Database Apr 2, 2026
Published to the GitHub Advisory Database Apr 3, 2026
Reviewed Apr 3, 2026
Last updated Apr 3, 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 None
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity None
Availability High
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:N/UI:N/VC:N/VI:N/VA:H/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.
(18th percentile)

Weaknesses

Allocation of Resources Without Limits or Throttling

The product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated. Learn more on MITRE.

CVE ID

CVE-2026-32145

GHSA ID

GHSA-8645-p2v4-73r2

Source code

Credits

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