OPENSUSE-SU-2026:21690-1
Vulnerability from csaf_opensuse - Published: 2026-08-30 14:33 - Updated: 2026-08-30 14:33Summary
Security update for python-sqlparse
Severity
Important
Notes
Title of the patch: Security update for python-sqlparse
Description of the patch: This update for python-sqlparse fixes the following issues:
- CVE-2026-54284: high algorithm complexity when parsing SQL payloads with hundreds of nesting levels can lead to denial
of service via excessive resource consumption (bsc#1275459).
- CVE-2026-59893: quadratic complexity when processing unmatched dollar-quoted literal and multiline-comment delimiters
can lead to denial of service (bsc#1275461).
- CVE-2026-59894: improper backlash escaping allows for injection of Python or PHP code via a crafted SQL input
(bsc#1275460).
- CVE-2026-71491: quadratic complexity in `group_comments` when processing comment-only statements can lead to denial
of services (bsc#1275466).
- Incomplete GHSA-27jp-wm6q-gp25 patch (bsc#1268597).
Patchnames: openSUSE-Leap-16.0-1546
Terms of use: CSAF 2.0 data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).
7.5 (High)
Affected products
Recommended
1 product
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: openSUSE Leap 16.0:python313-sqlparse-0.5.3-160000.4.1.noarch | — |
Vendor Fix
|
Threats
Impact
important
7.5 (High)
Affected products
Recommended
1 product
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: openSUSE Leap 16.0:python313-sqlparse-0.5.3-160000.4.1.noarch | — |
Vendor Fix
|
Threats
Impact
important
5.5 (Medium)
Affected products
Recommended
1 product
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: openSUSE Leap 16.0:python313-sqlparse-0.5.3-160000.4.1.noarch | — |
Vendor Fix
|
Threats
Impact
moderate
7.5 (High)
Affected products
Recommended
1 product
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: openSUSE Leap 16.0:python313-sqlparse-0.5.3-160000.4.1.noarch | — |
Vendor Fix
|
Threats
Impact
important
References
19 references
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"text": "sqlparse is a non-validating SQL parser module for Python. Prior to 0.6.0, TokenList construction and string conversion in sqlparse/sql.py repeatedly flatten nested token subtrees constructed by group_parenthesis and group_case, causing quadratic CPU consumption through sqlparse.parse(), sqlparse.format(), and sqlparse.split() before depth and token limits terminate processing. This issue is fixed in version 0.6.0.",
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"text": "sqlparse is a non-validating SQL parser module for Python. Prior to 0.6.0, SQL_REGEX in sqlparse/keywords.py and the per-position loop in sqlparse/lexer.py repeatedly scan unmatched dollar-quoted literal and multiline-comment delimiters, causing quadratic CPU consumption through sqlparse.parse(), sqlparse.format(), and sqlparse.split(). This issue is fixed in version 0.6.0.",
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"text": "sqlparse is a non-validating SQL parser module for Python. Prior to 0.6.0, sqlparse/filters/output.py fails to escape existing backslashes before quotes in sqlparse.format output_format=\u0027python\u0027 and output_format=\u0027php\u0027 and the corresponding sqlformat -l modes, allowing crafted SQL to terminate the generated string and inject Python or PHP code when a downstream consumer executes or imports the generated source. This issue is fixed in version 0.6.0.",
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"text": "sqlparse is a non-validating SQL parser module for Python. Prior to 0.6.0, group_comments in sqlparse/engine/grouping.py repeatedly rescans comment-only statements before the MAX_GROUPING_TOKENS guard, causing quadratic CPU consumption through sqlparse.parse() and sqlparse.format(sql, strip_comments=True). This issue is fixed in version 0.6.0.",
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}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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