Picklescan

Remote attackers can bypass security checks in `picklescan` versions prior to 0.0.33 by crafting malicious pickle payloads utilizing `operator.methodcaller` function calls, which upon loading by systems relying on `picklescan` for validation, results in arbitrary code execution and system compromise.

A critical vulnerability, CVE-2025-71369, in `picklescan` versions prior to 0.0.28 allows remote attackers to bypass safety checks for malicious Python pickle files that utilize specific `torch.utils.data.datapipes` methods, enabling undetected embedded malicious code to execute during deserialization, which results in remote code execution (RCE) on the victim's system.

Picklescan versions prior to 0.0.34 contain a deserialization vulnerability (CVE-2025-71367) that allows remote attackers to bypass security checks by crafting malicious pickle files using `_operator.attrgetter` in reduce methods, leading to arbitrary code execution when `pickle.load()` processes the file.

A critical deserialization vulnerability (CVE-2025-71366) exists in picklescan versions prior to 0.0.28, allowing remote attackers to bypass safety checks by embedding malicious `torch.utils.bottleneck.__main__.run_cprofile` function calls in pickle files, leading to arbitrary code execution when victims load the crafted files.

picklescan versions prior to 0.0.33 are vulnerable to unsafe deserialization via CVE-2025-71362, allowing attackers to embed malicious code in pickle files that executes due to `numpy.f2py.crackfortran` calling `eval` on arbitrary strings when loaded from untrusted sources, leading to arbitrary code execution.

A high-severity deserialization of untrusted data vulnerability (CVE-2025-71360) in picklescan versions before 0.0.29 allows attackers to embed undetected remote command execution code within malicious pickle files, leading to arbitrary code execution when loaded by victims.

A critical deserialization vulnerability (CVE-2025-71356) in `picklescan` versions prior to 0.0.28 allows attackers to embed undetected malicious code within Python pickle files, leading to remote code execution when these files are loaded by victims.

A critical vulnerability (CVE-2025-71347) exists in picklescan prior to version 0.0.33, allowing remote attackers to bypass security checks by failing to detect malicious pickle files leveraging the numpy.f2py.crackfortran.param_eval function, leading to arbitrary code execution upon deserialization of untrusted data.

CVE-2025-71345 describes a critical vulnerability in `picklescan` versions prior to 0.0.30, where attackers can embed undetected malicious code within pickle files that specifically invoke the `torch.utils.bottleneck.__main__.run_autograd_prof` function, leading to remote code execution upon deserialization by bypassing `picklescan`'s security checks.

A deserialization vulnerability, CVE-2025-71343, in picklescan before version 0.0.30 allows attackers to craft malicious pickle files that evade detection and lead to arbitrary code execution when loaded via `pickle.load()`.

A critical vulnerability (CVE-2025-71342) exists in picklescan versions prior to 0.0.30, where it fails to detect malicious code embedded in Python pickle files by leveraging `idlelib.run.Executive.runcode` in reduce methods, allowing attackers to conceal and execute arbitrary code during `pickle.load` operations, leading to remote code execution (RCE) and potential supply chain attacks, particularly impacting PyTorch models.