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Mistune: Potential DoS via quadratic-time parsing in parse_link_text

High severity GitHub Reviewed Published Jun 21, 2026 in lepture/mistune • Updated Jul 9, 2026

Package

pip mistune (pip)

Affected versions

< 3.3.0

Patched versions

3.3.0

Description

Summary

Mistune is vulnerable to a CPU exhaustion DoS due to superlinear (approximately O(n²)) behavior in parse_link_text. A relatively small input consisting of repeated [ characters causes significant parsing slowdown.

Affected component

mistune/inline_parser.py → parse_link_text

Description

When parsing Markdown containing many consecutive [ characters, parse_link_text repeatedly scans the input using a regex search inside a loop. Each iteration re-scans a large portion of the remaining string, resulting in quadratic-time behavior.
An attacker-controlled Markdown input can therefore trigger excessive CPU usage with a very small payload.

Root cause

The vulnerability stems from a two-loop interaction:

  • The outer loop in InlineParser.parse() (inline_parser.py) advances
    only 1 character at a time when parse_link() returns None
  • Each failed attempt calls parse_link_text() which performs an O(n)
    scan to the end of the string looking for a closing ]
  • With n consecutive [ characters, this results in O(n) × O(n) = O(n²)
    total work

PoC

Run below python script

import mistune
import time

md = mistune.create_markdown()

s = "[" * 6400

t = time.perf_counter()
md(s)
print(time.perf_counter() - t)

image

Benmark poc
Run below code for benchmark

import mistune
import time

md = mistune.create_markdown()

sizes = [100,200,400,800,1600,3200,6400]

for n in sizes:
    s = "[" * n

    t0 = time.perf_counter()
    md(s)
    dt = time.perf_counter() - t0

    print(f"{n:6d} {dt:.6f}")

image

Observed behaviour

python3 benchmark.py 
   100 0.001609
   200 0.003207
   400 0.012906
   800 0.050220
  1600 0.197307
  3200 0.801172
  6400 3.190393

Execution time grows superlinearly, consistent with O(n²) complex

Impact

This can be used as a denial-of-service attack in any application that parses user-supplied Markdown using Mistune, including:

  • Web applications (comments, posts, content rendering)
  • API services processing Markdown
  • Documentation rendering systems
  • A small (~6 KB) payload can block CPU for multiple seconds.

Suggested fix

Return the furthest scanned position from parse_link_text even on failure, so the outer loop can skip ahead instead of advancing 1 character at a time

Security Classification

CWE-400: Uncontrolled Resource Consumption
Denial of Service (CPU exhaustion)

References

@lepture lepture published to lepture/mistune Jun 21, 2026
Published by the National Vulnerability Database Jun 24, 2026
Published to the GitHub Advisory Database Jul 9, 2026
Reviewed Jul 9, 2026
Last updated Jul 9, 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.
(48th percentile)

Weaknesses

Uncontrolled Resource Consumption

The product does not properly control the allocation and maintenance of a limited resource. Learn more on MITRE.

Inefficient Regular Expression Complexity

The product uses a regular expression with an inefficient, possibly exponential worst-case computational complexity that consumes excessive CPU cycles. Learn more on MITRE.

CVE ID

CVE-2026-49851

GHSA ID

GHSA-qcq2-496w-v96p

Source code

Credits

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