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Reindentation of tuple lists causes near-cap quadratic CPU consumption

Moderate
andialbrecht published GHSA-cfqr-cjx5-5jcm Aug 13, 2026

Package

pip sqlparse (pip)

Affected versions

<=0.5.5

Patched versions

0.6.0

Description

Summary

When SQL is formatted with reindentation enabled, ReindentFilter repeatedly rebuilds prefixes of the current statement to calculate token offsets. An attacker who controls SQL sent to this opt-in formatting path can supply a parenthesized tuple list that remains just below the grouping-token cap. Thousands of offset calculations then traverse an expanding token tree, causing multi-second CPU consumption from an input of roughly 16 KB and degrading service availability.

Details

ReindentFilter._get_offset() joins the tokens returned by _flatten_up_to_token() to calculate the current output position. Each call begins by flattening the current statement from its start and walks until the target token. Tuple-list reindentation invokes this calculation repeatedly as it processes many parenthesized values, so later calls redo an increasingly large amount of prior work.

The vulnerable path is reached through sqlparse.format(sql, reindent=True) and sqlformat --reindent. A carefully sized tuple list completes grouping below the configured token cap and then enters the expensive reindentation path; a slightly larger input may instead be rejected quickly by the cap.

Relevant code locations:

  • sqlparse/formatter.py:170 — enabling ReindentFilter
  • sqlparse/filters/reindent.py:30 — repeated flattening from the statement start
  • sqlparse/filters/reindent.py:44 — prefix joining for offset calculation
  • sqlparse/filters/reindent.py:216 — tuple-list processing path

PoC

A complete validated reproduction is attached as reindent_tuple_list_cpu_dos-poc.zip. The archive contains reproduction/ at its root, uses Git and Docker, and compares two same-shape tuple-list inputs formatted with reindentation enabled.

Extract the archive beside this report, then run:

./reproduction/run.sh

Observed result:

The 600-tuple baseline completed in 0.649 seconds, while the below-cap 1,425-tuple input completed in 4.999 seconds. The run emitted EVOHUNT_REINDENT_DOS_VERIFIED and completed successfully.

Verification method:

The verification helper formats two same-shape tuple-list payloads with reindent=True and fails unless the larger payload completes successfully, takes at least 2.0 seconds, and takes at least 4x the baseline.

Limitations:

No reproduction blocker was recorded. Timing varies by host, and exploitation requires the reindentation option or corresponding CLI mode to be enabled.

Impact

This is a CPU resource-exhaustion vulnerability in workflows that reindent attacker-controlled SQL. A small crafted tuple-list input can occupy a worker for several seconds, enabling request delays, reduced throughput, or worker starvation when payloads are processed repeatedly or concurrently.

The affected reindentation behavior is opt-in, and sqlparse itself does not provide network exposure; reachability depends on the consuming application or CLI workflow. The demonstrated effect is CPU consumption in a single formatting call, not process termination, code execution, or confidentiality or integrity impact.

Severity

Moderate

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 Local
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity None
Availability Low
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:L/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

CVE ID

No known CVE

Weaknesses

No CWEs

Credits