CVE-2025-71345

picklescan before 0.0.30 fails to detect malicious pickle files that invoke torch.utils.bottleneck.main.run_autograd_prof function. Attackers can embed undetected code in pickle files that executes during deserialization, enabling remote code execution.

picklescan before 0.0.30 fails to detect malicious pickle files that invoke torch.utils.bottleneck.main.run_autograd_prof function. Attackers can embed undetected code in pickle files that executes during deserialization, enabling remote code execution.

picklescan before 0.0.30 fails to detect malicious pickle files that invoke torch.utils.bottleneck.main.run_autograd_prof function. Attackers can embed undetected code in pickle files that executes during deserialization, enabling remote code execution.

Summary

Using torch.utils.bottleneck.__main__.run_autograd_prof function, which is a pytorch library function to execute remote pickle file.

Details

The attack payload executes in the following steps:

First, the attacker craft the payload by calling to torch.utils.bottleneck.main.run_autograd_prof function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution.

PoC

import torch.utils.bottleneck.__main__ as bottleneck_main

class EvilTorchUtilsBottleneckRunAutogradProf:
    def __reduce__(self):
        code = '__import__("os").system("whoami")'
        globs = {}
        return bottleneck_main.run_autograd_prof, (code, globs)

Impact

Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. 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.

Corresponding

https://github.com/FredericDT https://github.com/Qhaoduoyu

picklescan before 0.0.30 fails to detect malicious pickle files that invoke torch.utils.bottleneck.main.run_autograd_prof function. Attackers can embed undetected code in pickle files that executes during deserialization, enabling remote code execution.

picklescan before 0.0.30 fails to detect malicious pickle files that invoke torch.utils.bottleneck.main.run_autograd_prof function. Attackers can embed undetected code in pickle files that executes during deserialization, enabling remote code execution.

Summary

Using torch.utils.bottleneck.__main__.run_autograd_prof function, which is a pytorch library function to execute remote pickle file.

Details

The attack payload executes in the following steps:

First, the attacker craft the payload by calling to torch.utils.bottleneck.main.run_autograd_prof function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution.

PoC

import torch.utils.bottleneck.__main__ as bottleneck_main

class EvilTorchUtilsBottleneckRunAutogradProf:
    def __reduce__(self):
        code = '__import__("os").system("whoami")'
        globs = {}
        return bottleneck_main.run_autograd_prof, (code, globs)

Impact

Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. 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.

Corresponding

https://github.com/FredericDT https://github.com/Qhaoduoyu