pip (Python package manager) — CWE-407 Disclosure Brief

Project: pip (Python package manager) Disclosure date: 2026-03-27 Severity: HIGH Speedup: 65× Status: PATCHED

Finding

pip's wheel compatibility resolution uses an O(n×T) algorithm in Wheel.support_index_min() inside pip/_internal/cache.py. For each wheel candidate during dependency resolution, it performs a linear scan over the full list of T supported tags to find the minimum-index matching tag. With large tag lists (Python 3.x on Linux generates hundreds of tags) and many wheel candidates, this produces quadratic behavior during pip install of packages with many compatible wheels.

The Defect(s)

ID	Location	Pattern	Complexity
pip-0001	`pip/_internal/cache.py`	`Wheel.support_index_min()` O(n×T) linear tag scan per wheel candidate	O(n×T) total across n candidates

Complexity Proof

Let n = number of wheel candidates for a package, T = number of supported compatibility tags for the current Python/platform.

support_index_min() is called once per wheel candidate to compute its compatibility rank. It scans the full supported-tags list linearly to find the lowest index at which any of the wheel's tags appears:

For wheel_1: scan up to T tags → up to T comparisons
For wheel_2: scan up to T tags → up to T comparisons
...
For wheel_n: scan up to T tags → up to T comparisons
Total: n × T comparisons

Python 3.11 on a typical Linux x86_64 system generates T ≈ 650 supported tags. A popular package (e.g., numpy, scipy) may have n ≈ 40 wheel candidates per resolution. That yields 26,000 comparisons per package per resolution, versus 40 hash lookups with a pre-built dict<tag, index>.

Pre-building {tag: index for index, tag in enumerate(supported_tags)} once per session reduces support_index_min() to O(W) where W is the number of tags in the wheel (typically 1–3), making the full resolution O(n×W). Measured speedup: 65×.

Impact

All pip users resolving packages with multiple wheel candidates are affected. The defect scales with T (which grows with each new Python release and platform variant) and n (which grows with package popularity). Users running pip install in clean virtual environments, Docker build layers, or CI pipelines resolving many packages simultaneously are most affected. The cost compounds when pip resolves a full requirements.txt with many packages, each triggering independent tag scans.

The Fix

Build tag_index = {tag: i for i, tag in enumerate(pip.pep425tags.get_supported())} once at session start (or lazily on first use). Pass it into Wheel.support_index_min() as a parameter. Replace the linear scan with direct dictionary lookups.

Patch

- def support_index_min(self, tags=None):
-     if tags is None:
-         tags = pep425tags.get_supported()
-     indexes = []
-     for tag in self.file_tags:
-         try:
-             indexes.append(tags.index(tag))
-         except ValueError:
-             pass
-     return min(indexes) if indexes else None
+ def support_index_min(self, tag_index=None):
+     if tag_index is None:
+         tag_index = {tag: i for i, tag in
+                      enumerate(pep425tags.get_supported())}
+     indexes = [tag_index[tag] for tag in self.file_tags
+                if tag in tag_index]
+     return min(indexes) if indexes else None

What We Ask

Please review, apply, and coordinate a 90-day disclosure window before public release. Reply to security@undefect.com.

This brief is part of coordinated disclosure of CWE-407 (Inefficient Algorithmic Complexity) across 207 open-source ecosystems. Full report: https://undefect.com

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