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Quadratic CPU blow-up reassembling fragmented WebSocket messages

High
mtrudel published GHSA-vg8x-66vg-5pxh Jul 24, 2026

Package

erlang bandit (Erlang)

Affected versions

>= 1.11.0 and < 1.12.1

Patched versions

1.12.1

Description

Summary

Quadratic-complexity CPU exhaustion in Bandit's WebSocket fragment reassembly. Each non-final continuation frame is appended to a left-nested iolist and the entire accumulated buffer is re-measured with IO.iodata_length/1 on every frame, giving O(n^2) work in the frame count. Any unauthenticated client that can open a WebSocket can trigger it.

Details

1. Accumulation. Bandit.WebSocket.Connection.handle_frame/3 (lib/bandit/websocket/connection.ex) conses each non-final continuation frame onto the running iolist and stores it back into fragment_frame. The append is O(1).

2. Quadratic guard. Bandit.WebSocket.Connection.oversize_message?/2 then calls IO.iodata_length/1 on the whole buffer to compare against max_fragmented_message_size. Walking the buffer on every frame makes reassembly O(n^2) in the continuation-frame count.

3. No effective cap. max_fragmented_message_size (default 8 MB) bounds bytes, not frames, and 1-byte continuations are valid. Millions of tiny frames fit under the byte cap while forcing on the order of 10^13 traversal steps. The zero-byte-frame guard does not help.

4. Read timeout is idle-only. Bandit's WebSocket read timeout applies between WebSock.handle_in/2-driving callbacks; the reassembly loop runs synchronously inside a single callback, so the timeout never fires during the CPU-bound work.

PoC

  1. Open a WebSocket to any Bandit endpoint (Phoenix /socket, /live, custom upgrade).
  2. Send an initial binary frame with fin=0 and a 1-byte payload.
  3. Stream tens of thousands of 1-byte fin=0 continuation frames, staying under max_fragmented_message_size.
  4. Close with a fin=1 continuation. Server burns seconds to hours in Bandit.WebSocket.Connection.oversize_message?/2. Scale across connections to starve all schedulers.

Impact

An unauthenticated remote attacker can pin a BEAM scheduler per connection with modest bandwidth, and scale to full server DoS via concurrent connections. Any application serving WebSockets through Bandit is affected, including stock Phoenix Channels and LiveView.

References

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

CVE ID

CVE-2026-65623

Weaknesses

Inefficient Algorithmic Complexity

An algorithm in a product has an inefficient worst-case computational complexity that may be detrimental to system performance and can be triggered by an attacker, typically using crafted manipulations that ensure that the worst case is being reached. Learn more on MITRE.

Credits