MBM7/skops-dtype-decompression-bomb-poc

skops — .skops NPY Dtype Decompression Bomb (PoC)

Repo: MBM7/skops-dtype-decompression-bomb-poc
Status: Responsible disclosure — submitted to Huntr
Severity: High / CWE-789
Package: skops (PyPI) — HuggingFace official sklearn model format

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Summary

A crafted 8.9 KB .skops sklearn model file causes skops.io.loads() to allocate 2 GB of memory before failing.

File size	Claimed dtype itemsize	Peak allocation	Amplification
8,935 bytes	500,000,000	500 MB	1 : 55,959
8,935 bytes	2,000,000,000	2,000 MB	1 : 223,838

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Root Cause

skops/io/_numpy.py, NdArrayNode._construct():

content = np.load(
    self.children["content"],   # BytesIO of .npy file from .skops ZIP
    allow_pickle=False
)   # ← np.load allocates count * dtype.itemsize BEFORE reading data

The .skops format is a ZIP archive containing schema.json + .npy files. When loading, NdArrayNode reads each .npy file into a BytesIO and passes it directly to np.load() — with no validation of dtype.itemsize or shape.

A crafted .npy file inside the ZIP claiming dtype=|S2000000000 (2 GB itemsize) causes np.load() to pre-allocate 2 GB before attempting to read data.

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Attack

Replace any .npy file inside a legitimate .skops model archive with a dtype bomb .npy header. schema.json remains unchanged — skops trusts it and loads all .npy files unconditionally:

import skops.io as sio

# Load triggers NdArrayNode._construct() → np.load() → 2GB allocation
trusted = sio.get_untrusted_types(data=bomb_skops_bytes)
model = sio.loads(bomb_skops_bytes, trusted=trusted)

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Distinct from numpy NPY finding

	numpy NPY	skops
Entry point	np.load('file.npy')	skops.io.loads(skops_bytes)
Format	.npy raw	.skops ZIP (sklearn model)
Ecosystem	NumPy arrays	HuggingFace sklearn model sharing
Context	Direct array loading	sklearn model deserialization

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Reproduce

pip install skops scikit-learn numpy
python poc_skops_dtype_bomb.py

Expected:

.skops file size  : 8,935 bytes
Expected allocation: 2,000,000,000 bytes
Amplification     : 1:223,838
Result            : ValueError: EOF: reading array data...
Peak memory       : 2000 MB  ← allocated before error

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Impact

Any service loading user-supplied .skops files from HuggingFace Hub or other sources can be OOM-killed by a sub-10KB payload. skops is the official HuggingFace format for sharing sklearn models — directly relevant to the AI/ML supply chain.

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Suggested Fix

In NdArrayNode._construct(), validate before np.load():

MAX_ARRAY_BYTES = 512 * 1024 * 1024  # 512 MB
buf = self.children["content"]
# Peek at NPY header without allocating
hdr = np.lib.format.read_array_header_1_0(buf)
buf.seek(0)
if hdr[1].itemsize > MAX_ARRAY_BYTES:
    raise ValueError(
        f"NPY dtype itemsize {hdr[1].itemsize} exceeds limit — "
        "possible decompression bomb"
    )
content = np.load(buf, allow_pickle=False)

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Environment

Package	Version
skops	0.14.0
numpy	2.4.4
Python	3.12

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skops was created as a safer alternative to pickle for sklearn models.
Ironic: the safety-focused format has an unvalidated allocation path.