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CVE-2025-55558: Buffer Overflow Vulnerability in pytorch v2.7.0 Leads to Denial of Service (DoS)

Overview

A critical vulnerability, CVE-2025-55558, has been identified in pytorch v2.7.0, which affects machine learning platforms that employ this version of the software. This vulnerability is of significant concern as it can lead to a buffer overflow, causing a Denial of Service (DoS) and potentially compromising system security or causing data leakage.

Vulnerability Summary

CVE ID: CVE-2025-55558
Severity: High (CVSS: 7.5)
Attack Vector: Network
Privileges Required: Low
User Interaction: None
Impact: Denial of Service, potential system compromise and data leakage

Affected Products

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Product | Affected Versions

pytorch | v2.7.0

How the Exploit Works

The vulnerability arises when a PyTorch model, consisting of torch.nn.Conv2d, torch.nn.functional.hardshrink, and torch.Tensor.view-torch.mv(), is compiled by Inductor. The process results in a buffer overflow if the model’s input is not correctly validated. This buffer overflow could then be exploited by an attacker to cause a denial of service, possibly compromising the system or leaking data.

Conceptual Example Code

Below is a conceptual example of how the vulnerability might be exploited. This pseudocode depicts a scenario where a malicious payload triggers the buffer overflow:

# Malicious payload
payload = "A" * 10000  # Oversized input
# PyTorch model
model = torch.nn.Sequential(
torch.nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
torch.nn.functional.hardshrink(),
torch.Tensor.view(-1).mv(payload)  # Trigger buffer overflow
)
# Compile with Inductor
inductor.compile(model)

This code would trigger a buffer overflow in the system running this version of pytorch, leading to a Denial of Service (DoS).

Mitigation

Users are advised to apply the vendor-provided patch as soon as possible to correct this vulnerability. As a temporary mitigation strategy, users can deploy a Web Application Firewall (WAF) or an Intrusion Detection System (IDS) to help identify and block exploit attempts.

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Disclaimer:

The information and code presented in this article are provided for educational and defensive cybersecurity purposes only. Any conceptual or pseudocode examples are simplified representations intended to raise awareness and promote secure development and system configuration practices.

Do not use this information to attempt unauthorized access or exploit vulnerabilities on systems that you do not own or have explicit permission to test.

Ameeba and its authors do not endorse or condone malicious behavior and are not responsible for misuse of the content. Always follow ethical hacking guidelines, responsible disclosure practices, and local laws.
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