CVE-2025-55552

Executive Summary

CVE-2025-55552 is a security advisory affecting PyTorch v2.8.0, where unexpected behavior occurs when the components torch.rot90 and torch.randn_like are used together. The vulnerability has a CVSS score of 0.0, indicating no current severity assessment, and no EPSS score, suggesting low exploitation likelihood. No public proof-of-concept (PoC) or patch details are available. The issue may lead to undefined behavior or memory corruption, potentially impacting model integrity or system stability.

Supply Chain & Package Security

This vulnerability affects the PyTorch Python package (PyPI ecosystem). Users should verify package integrity via checksums and monitor for updates. The lack of patch details increases supply chain risk, as malicious actors could exploit the behavior in unpatched environments.

CVSS Breakdown & Threat Level

• CVSS Score: 0.0 (No severity assigned)
• CVSS Vector: UNKNOWN
• Attack Vector: UNKNOWN
• Attack Complexity: UNKNOWN
• Privileges Required: UNKNOWN
• User Interaction: UNKNOWN
• Scope: UNKNOWN
• Confidentiality Impact: UNKNOWN
• Integrity Impact: UNKNOWN
• Availability Impact: UNKNOWN
• Threat Level: Low (based on no public exploits or active exploitation)

Deep Dive & Mechanics

The vulnerability arises from an interaction between torch.rot90 (which rotates a tensor by 90 degrees) and torch.randn_like (which generates a random tensor with the same shape as an input). When used together, the combination may cause unexpected behavior, such as:

• Incorrect tensor shapes or strides
• Memory corruption due to improper handling of rotated dimensions
• Potential for out-of-bounds reads/writes

The root cause likely involves a bug in the underlying C++/CUDA implementation where rot90 modifies tensor metadata (e.g., strides) that randn_like does not correctly account for, leading to undefined behavior.

Vulnerable vs. Patched Code

Vulnerable Code (PyTorch v2.8.0):

import torch

# Example that triggers unexpected behavior
x = torch.randn(3, 4, 5)
y = torch.rot90(x, k=1, dims=[0, 1])
z = torch.randn_like(y)  # May produce incorrect shape or crash

Patched Code (Hypothetical):

import torch

# Ensure rot90 output is contiguous before using randn_like
x = torch.randn(3, 4, 5)
y = torch.rot90(x, k=1, dims=[0, 1]).contiguous()
z = torch.randn_like(y)  # Works correctly

The patch would involve ensuring rot90 returns a contiguous tensor or that randn_like handles non-contiguous inputs properly.

Exploitation & Lab Setup

Red Team

Lab Setup:

• Install PyTorch v2.8.0: pip install torch==2.8.0
• Use a Python environment with CUDA (optional)

Exploitation:

1. Create a script that repeatedly calls torch.rot90 and torch.randn_like to trigger memory corruption.
2. Monitor for crashes or unexpected behavior.
3. If memory corruption occurs, attempt to leverage it for arbitrary code execution (theoretical).

Example Script:

import torch
import sys

def trigger_bug():
    for _ in range(1000):
        x = torch.randn(10, 10, 10)
        y = torch.rot90(x, k=1, dims=[0, 1])
        z = torch.randn_like(y)
        if z.shape != y.shape:
            print("Shape mismatch detected")
            sys.exit(1)

if __name__ == "__main__":
    trigger_bug()

Blue Team

Detection:

• Monitor for crashes in PyTorch processes.
• Use memory analysis tools to detect out-of-bounds access.
• Implement input validation to avoid using rot90 with randn_like in production.

Mitigation:

• Upgrade to a patched version when available.
• Use contiguous() after rot90 as a workaround.
• Restrict usage of these functions in untrusted environments.

Indicators of Compromise (IOCs)

• File Hashes: None available
• Network Indicators: None
• Behavioral: Unexpected crashes in PyTorch when using torch.rot90 and torch.randn_like together
• Logs: Python tracebacks indicating shape mismatch or memory errors

Detection Engineering

YARA Rule

rule CVE_2025_55552_PyTorch_Rot90_RandnLike
{
    meta:
        description = "Detects PyTorch code using rot90 and randn_like together"
        author = "Valters IT"
        date = "2025-01-01"
        cve = "CVE-2025-55552"
    strings:
        $rot90 = "torch.rot90"
        $randn_like = "torch.randn_like"
    condition:
        all of them
}

Sigma Rule

title: CVE-2025-55552 PyTorch Rot90 and RandnLike Usage
id: 12345678-1234-1234-1234-123456789012
status: experimental
description: Detects usage of torch.rot90 and torch.randn_like in Python scripts
logsource:
    category: process_creation
    product: windows
detection:
    selection:
        CommandLine|contains:
            - 'torch.rot90'
            - 'torch.randn_like'
    condition: selection
falsepositives:
    - Legitimate usage in research
level: medium

Suricata Rule

alert http $HOME_NET any -> $EXTERNAL_NET any (msg:"CVE-2025-55552 PyTorch Rot90 and RandnLike Usage"; flow:to_server,established; content:"torch.rot90"; http_uri; content:"torch.randn_like"; http_uri; classtype:attempted-recon; sid:1000001; rev:1;)

Remediation & Mitigation

1. Immediate Workaround: Add .contiguous() after torch.rot90 before using torch.randn_like.
2. Upgrade: Monitor PyTorch releases for a patch and upgrade to a fixed version.
3. Input Validation: Avoid using these functions together in production code.
4. Monitoring: Implement detection rules to alert on usage of these functions.
5. Supply Chain: Verify package integrity and use trusted sources.

Valters IT
Securing Digital Frontiers