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...

CVE-2025-71362

The vulnerability CVE-2025-71362 affects the Python tool picklescan prior to version 0.0.33. It fails to detect unsafe deserialization when numpy.f2py.crackfortran calls eval on arbitrary strings, allowing an attacker to embed malicious code in pickle files that executes upon loading from untrust...

CVE-2025-71347

The vulnerability concerns picklescan prior to 0.0.33, which fails to detect malicious pickle files that rely on numpy.f2py.crackfortran.param_eval in reduce methods. This allows remote attackers to embed code that executes during deserialization in applications that load untrusted pickle data, e...

EUVD-2025-210388

Picklescan before 0.0.25 fails to detect unsafe global functions in the Numpy library, allowing attackers to bypass static analysis and execute arbitrary code during deserialization. Attackers can craft malicious pickle files using numpy.testing.private.utils.runstring within the reduce method to...

CVE-2025-71355

Picklescan before 0.0.25 fails to detect unsafe global functions in the Numpy library, allowing attackers to bypass static analysis and execute arbitrary code during deserialization. Attackers can craft malicious pickle files using numpy.testing.private.utils.runstring within the reduce method to...

CVE-2025-71355 Picklescan - Arbitrary Code Execution via Unsafe Numpy Function Detection Bypass

Picklescan before 0.0.25 fails to detect unsafe global functions in the Numpy library, allowing attackers to bypass static analysis and execute arbitrary code during deserialization. Attackers can craft malicious pickle files using numpy.testing.private.utils.runstring within the reduce method to...

CVE-2025-71355

CVE-2025-71355 : Picklescan prior to 0.0.25 fails to detect unsafe global functions in the Numpy library, enabling an attacker to bypass static analysis and execute arbitrary code during deserialization. Attackers can craft malicious pickle files using numpy.testing._private.utils.runstring withi...

CVE-2025-71365

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded...

CVE-2025-71365

The CVE affects picklescan (before 0.0.33) where the detector fails to catch malicious pickle payloads that invoke numpy.f2py.crackfortran.myeval via the reduce method, allowing arbitrary code execution when loaded. Root cause: detection bypass in pickle loading path. Impact: remote code executio...

EUVD-2025-210306

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded...

CVE-2025-71365 picklescan - Arbitrary Code Execution via numpy.f2py.crackfortran.myeval Detection Bypass

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded...

CVE-2025-71365

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded...

CVE-2025-71339

Affected software/component: Picklescan (versions prior to 0.0.33). Vulnerability/gadget: The numpy.f2py.crackfortran._eval_length gadget in pickle reduce methods can bypass safety validation, enabling arbitrary code execution when loading crafted pickle files. Impact (as stated): Arbitrary Pytho...

CVE-2025-71339 Picklescan - Arbitrary Code Execution via numpy.f2py.crackfortran._eval_length Gadget

Picklescan before 0.0.33 fails to detect the numpy.f2py.crackfortran.evallength gadget in pickle reduce methods, allowing arbitrary code execution. Attackers can craft malicious pickle files that execute arbitrary Python code when loaded by victims who trust Picklescan's safety validation...

Unity Linux 20.1050e / 20.1060e / 20.1070e Security Update: numpy (UTSA-2026-016631)

The Unity Linux 20 host has a package installed that is affected by a vulnerability as referenced in the UTSA-2026-016631 advisory. An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific...

Unity Linux 20.1050e / 20.1060e / 20.1070e Security Update: numpy (UTSA-2026-017404)

The Unity Linux 20 host has a package installed that is affected by a vulnerability as referenced in the UTSA-2026-017404 advisory. Null Pointer Dereference vulnerability exists in numpy.sort in NumPy and 1.19 in the PyArrayDescrNew function due to missing return-value validation, which allows...

Keras 3.13.0 HDF5 Shape Fuzzing for Robustness Testing

This script performs fuzz testing against Keras version 3.13.0 on randomly generated tensor shapes using NumPy and HDF5 to evaluate stability and error handling in file creation workflows...

EUVD-2026-18522

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing tomono, while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results...

CVE-2026-34760

Summary: CVE-2026-34760 concerns vLLM’s audio processing path via Librosa. From version 0.5.5 up to before 0.18.0, Librosa used numpy.mean for mono downmix (to_mono), while ITU-R BS.775-4 specifies a weighted downmix. This mismatch creates inconsistency between audio perceived by humans and audio...

CVE-2026-34760

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing tomono, while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results...