CVE-2025-71362

picklescan before 0.0.33 fails to detect unsafe deserialization when numpy.f2py.crackfortran functions call eval on arbitrary strings. Attackers can embed malicious code in pickle files that executes when loaded from untrusted sources.

picklescan before 0.0.33 fails to detect unsafe deserialization when numpy.f2py.crackfortran functions call eval on arbitrary strings. Attackers can embed malicious code in pickle files that executes when loaded from untrusted sources.

Summary

An unsafe deserialization vulnerability allows an attacker to execute arbitrary code on the host when loading a malicious pickle payload from an untrusted source.

Details

The numpy.f2py.crackfortran module exposes many functions that call eval on arbitrary strings of values. This is the case for getlincoef and _eval_length. This list is probably not exhaustive.

According to https://numpy.org/doc/stable/reference/security.html#advice-for-using-numpy-on-untrusted-data, the whole numpy.f2py should be considered unsafe when loading a pickle.

PoC

from numpy.f2py.crackfortran import getlincoef

class EvilClass:
    def __reduce__(self):
        payload = "__import__('os').system('echo \"successful attack\"')"
        return getlincoef, (payload, [])

Impact

Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files from untrusted sources. What is the impact? Attackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded. Supply Chain Attack: Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects.

Note

The problem was originally reported to the joblib project, but this was deemed unrelated to joblib itself. However, I checked that picklescan was indeed vulnerable.

picklescan before 0.0.33 fails to detect unsafe deserialization when numpy.f2py.crackfortran functions call eval on arbitrary strings. Attackers can embed malicious code in pickle files that executes when loaded from untrusted sources.

picklescan before 0.0.33 fails to detect unsafe deserialization when numpy.f2py.crackfortran functions call eval on arbitrary strings. Attackers can embed malicious code in pickle files that executes when loaded from untrusted sources.

picklescan before 0.0.33 fails to detect unsafe deserialization when numpy.f2py.crackfortran functions call eval on arbitrary strings. Attackers can embed malicious code in pickle files that executes when loaded from untrusted sources.

Summary

An unsafe deserialization vulnerability allows an attacker to execute arbitrary code on the host when loading a malicious pickle payload from an untrusted source.

Details

The numpy.f2py.crackfortran module exposes many functions that call eval on arbitrary strings of values. This is the case for getlincoef and _eval_length. This list is probably not exhaustive.

According to https://numpy.org/doc/stable/reference/security.html#advice-for-using-numpy-on-untrusted-data, the whole numpy.f2py should be considered unsafe when loading a pickle.

PoC

from numpy.f2py.crackfortran import getlincoef

class EvilClass:
    def __reduce__(self):
        payload = "__import__('os').system('echo \"successful attack\"')"
        return getlincoef, (payload, [])

Impact

Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files from untrusted sources. What is the impact? Attackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded. Supply Chain Attack: Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects.

Note

The problem was originally reported to the joblib project, but this was deemed unrelated to joblib itself. However, I checked that picklescan was indeed vulnerable.