Description:

In Scikit-learn version 0.23.2 calling the predict() method maliciously crafted model SVM can result in a segmentation fault. Such models can be introduced via pickle, json, or any other model permanence standard. The behaviour is triggered when one of the members of the _n_support array has a very large value, example 1000000 when calling libsvm.predict()

####Tested environment:

Ubuntu 9.3.0-17ubuntu1~20.04
Python 3.8.5 (default, Jul 28 2020, 12:59:40)
[GCC 9.3.0] on linux
Numpy version: '1.19.2'
Sklearn.version: '0.23.2'

Steps/Code to Reproduce

from sklearn import svm
from sklearn import datasets


if __name__ == '__main__':
    X,y = datasets.load_iris(return_X_y=True)
    clf = svm.SVC()
    clf.fit(X, y)
    clf._n_support[0] = 1000000
    y_pred = clf.predict(X)

Expected Results

not to fail

Actual Results

Segmentation fault, this is a debugger trace

Thread 1 "python3-dbg" received signal SIGSEGV, Segmentation fault. 0x00007fffd7174df7 in svm_predict_values () from /home/pablo/.local/lib/python3.8/site-packages/sklearn/svm/_libsvm.cpython-38-x86_64-linux-gnu.so (gdb) bt #0 0x00007fffd7174df7 in svm_predict_values () from /home/pablo/.local/lib/python3.8/site-packages/sklearn/svm/_libsvm.cpython-38-x86_64-linux-gnu.so #1 0x00007fffd717504f in svm_predict () from /home/pablo/.local/lib/python3.8/site-packages/sklearn/svm/_libsvm.cpython-38-x86_64-linux-gnu.so #2 0x00007fffd7163e57 in copy_predict () from /home/pablo/.local/lib/python3.8/site-packages/sklearn/svm/_libsvm.cpython-38-x86_64-linux-gnu.so #3 0x00007fffd716bb3a in ?? () from /home/pablo/.local/lib/python3.8/site-packages/sklearn/svm/_libsvm.cpython-38-x86_64-linux-gnu.so #4 0x00007fffd716d47d in ?? () from /home/pablo/.local/lib/python3.8/site-packages/sklearn/svm/_libsvm.cpython-38-x86_64-linux-gnu.so #5 0x000000000043663e in cfunction_call_varargs (func=0x7fffd76e9dd0, args=0x7fffd40fc1d0, kwargs=0x7fffd40fbdd0) at ../Objects/call.c:742 #6 0x000000000043920c in PyCFunction_Call (func=<optimized out>, args=<optimized out>, kwargs=<optimized out>) at ../Objects/call.c:772 #7 0x000000000043708b in _PyObject_MakeTpCall (callable=callable@entry=0x7fffd76e9dd0, args=args@entry=0x18433a0, nargs=<optimized out>, keywords=keywords@entry=0x7fffd7821de0) at ../Objects/call.c:159 #8 0x00000000004ebe5b in _PyObject_Vectorcall (kwnames=0x7fffd7821de0, nargsf=9223372036854775816, args=0x18433a0, callable=0x7fffd76e9dd0) at ../Include/cpython/abstract.h:125 #9 call_function (kwnames=0x7fffd7821de0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x954500) at ../Python/ceval.c:4963 #10 _PyEval_EvalFrameDefault (f=0x1843210, throwflag=<optimized out>) at ../Python/ceval.c:3515 #11 0x00000000004df2ef in PyEval_EvalFrameEx (f=

Versions

System:
python: 3.8.5 (default, Jul 28 2020, 12:59:40) [GCC 9.3.0]
executable: /usr/bin/python3
machine: Linux-5.4.0-53-generic-x86_64-with-glibc2.29

Python dependencies:
pip: 20.0.2
setuptools: 45.2.0
sklearn: 0.23.2
numpy: 1.19.2
scipy: 1.5.2
Cython: None
pandas: 1.1.2
matplotlib: 3.3.2
joblib: 0.16.0
threadpoolctl: 2.1.0

Built with OpenMP: True