| Gói / sản phẩm | Phiên bản bị ảnh hưởng | Phiên bản đã vá |
|---|---|---|
| sqlparse | < 0.6.0 | 0.6.0 |
A comment-only statement (-- c\n*n) may cause a Denial of Service (DoS).
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:
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.format(sql, strip_comments=True), used by query loggers, SQL firewalls, ORMs, and migration tools.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.
Denial of Service
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