CVE-2025-55551
📋 TL;DR
A vulnerability in PyTorch's torch.linalg.lu component allows attackers to trigger a Denial of Service (DoS) through specific slice operations. This affects systems running PyTorch v2.8.0 where LU decomposition functions are used. The vulnerability can crash applications or services relying on these mathematical operations.
💻 Affected Systems
- PyTorch
📦 What is this software?
Pytorch by Linuxfoundation
⚠️ Risk & Real-World Impact
Worst Case
Complete service disruption causing application crashes and unavailability of machine learning inference/prediction services.
Likely Case
Application instability and crashes when processing specific tensor operations, disrupting machine learning workflows.
If Mitigated
Minimal impact with proper input validation and error handling in place, though performance degradation may still occur.
🎯 Exploit Status
Proof of concept code is available in GitHub issues. Exploitation requires the ability to trigger specific tensor operations but doesn't require authentication to the vulnerable system.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: v2.8.1 or later
Vendor Advisory: https://github.com/pytorch/pytorch/issues/151401
Restart Required: Yes
Instructions:
1. Update PyTorch using pip: pip install --upgrade torch==2.8.1
2. Restart all applications and services using PyTorch
3. Verify the update with: python -c "import torch; print(torch.__version__)"
🔧 Temporary Workarounds
Input validation wrapper
allWrap torch.linalg.lu calls with input validation to prevent triggering the vulnerable slice operations
# Python code to wrap vulnerable function
import torch
def safe_lu(input):
# Add input validation logic here
if input.dim() > 2:
raise ValueError("Input validation failed")
return torch.linalg.lu(input)
Function replacement
allReplace torch.linalg.lu with alternative decomposition methods where possible
# Consider using torch.linalg.qr or other decomposition methods
# instead of torch.linalg.lu for affected operations
🧯 If You Can't Patch
- Implement strict input validation and sanitization for all tensor operations
- Deploy monitoring and alerting for application crashes related to torch.linalg operations
🔍 How to Verify
Check if Vulnerable:
Check PyTorch version: python -c "import torch; print(torch.__version__)" - if output is 2.8.0, system is vulnerable.Check Version:
python -c "import torch; print(torch.__version__)"Verify Fix Applied:
After patching, verify version is 2.8.1 or later and test LU decomposition operations with previously problematic inputs.📡 Detection & Monitoring
Log Indicators:
- Application crashes with segmentation faults
- Python tracebacks mentioning torch.linalg.lu
- Memory allocation errors in system logs
Network Indicators:
- Sudden drops in API response rates for ML services
- Increased error rates in ML inference endpoints
SIEM Query:
source="application.logs" AND ("segmentation fault" OR "torch.linalg.lu" OR "memory allocation failed")