Overview

This report provides an in-depth analysis of a high-risk vulnerability, identified as CVE-2025-55553. This vulnerability resides in the PyTorch machine learning library, specifically in the component proxy_tensor.py of version 2.7.0. It can be exploited by attackers to cause a Denial of Service (DoS), potentially leading to system compromise or data leakage. The severity of this vulnerability and the widespread utilization of PyTorch necessitate immediate attention and mitigation.

Vulnerability Summary

CVE ID: CVE-2025-55553
Severity: High (CVSS Score: 7.5)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: Potential system compromise or data leakage

Affected Products

Product | Affected Versions

PyTorch | v2.7.0

How the Exploit Works

The exploit works by taking advantage of a syntax error in the proxy_tensor.py component of PyTorch v2.7.0. By sending specially crafted requests or data to the vulnerable system, an attacker can cause a denial of service condition. This occurs due to the system’s inability to handle the incorrect syntax, which results in a halt or excessive consumption of system resources. This could potentially lead to a system shutdown, compromise, or data leakage.

Conceptual Example Code

Here is a conceptual example of how the vulnerability might be exploited. The attacker could craft a malicious payload that triggers the syntax error in the proxy_tensor.py component. The following pseudocode demonstrates the concept:

import torch
# Create a tensor with malicious data
malicious_tensor = torch.tensor([INVALID_SYNTAX])
# Send the malicious tensor to the proxy_tensor component
proxy_tensor.process(malicious_tensor)

The above pseudocode is conceptual and only serves to illustrate the exploitation process. The actual exploit may differ significantly depending on the context and the attacker’s intent.