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
Share secrets securely
Ameeba is private infrastructure for communication and sensitive work built on encrypted identity instead of exposed corporate identity systems.
Passwords, credentials, confidential files, screenshots, internal discussions, sensitive AI context, and private coordination should not become exposed across ordinary communication platforms.
- • Encrypted identity
- • Private Spaces for organizations and teams
- • End-to-end encrypted chat, calls, files, and notes
- • Sensitive AI work and protected collaboration
- • Built for information that cannot leak
Our mission is to secure human work alongside AI.
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.