CVE-2025-55554
📋 TL;DR
PyTorch v2.8.0 contains an integer overflow vulnerability in torch.nan_to_num-.long() that could allow memory corruption or denial of service. This affects users who process untrusted numerical data with this specific function. The vulnerability requires attacker-controlled input to trigger.
💻 Affected Systems
- PyTorch
📦 What is this software?
Pytorch by Linuxfoundation
⚠️ Risk & Real-World Impact
Worst Case
Memory corruption leading to arbitrary code execution or complete system compromise if combined with other vulnerabilities.
Likely Case
Application crash or denial of service when processing malicious numerical inputs.
If Mitigated
Limited impact with proper input validation and sandboxing of PyTorch operations.
🎯 Exploit Status
Exploitation requires crafting specific numerical inputs to trigger integer overflow. No public exploit code available.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: PyTorch v2.8.1 or later
Vendor Advisory: https://github.com/pytorch/pytorch/issues/151510
Restart Required: No
Instructions:
1. Update PyTorch using pip: pip install --upgrade torch 2. Verify installation with: python -c "import torch; print(torch.__version__)" 3. Ensure version is 2.8.1 or higher.
🔧 Temporary Workarounds
Input validation wrapper
allWrap torch.nan_to_num-.long() calls with input validation to prevent malicious values
# Python example:
def safe_nan_to_num_long(tensor, nan=0.0, posinf=None, neginf=None):
# Add validation logic here
return torch.nan_to_num(tensor, nan=nan, posinf=posinf, neginf=neginf).long()
🧯 If You Can't Patch
- Avoid using torch.nan_to_num-.long() with untrusted user input
- Implement strict input validation and sanitization for all numerical data processed by PyTorch
🔍 How to Verify
Check if Vulnerable:
Check PyTorch version: python -c "import torch; print('VULNERABLE' if torch.__version__ == '2.8.0' else 'NOT VULNERABLE')"Check Version:
python -c "import torch; print(torch.__version__)"Verify Fix Applied:
Verify PyTorch version is 2.8.1 or higher: python -c "import torch; print(torch.__version__)"📡 Detection & Monitoring
Log Indicators:
- Application crashes or segmentation faults when processing numerical data
- Unexpected memory allocation errors in PyTorch logs
Network Indicators:
- Unusual patterns of numerical data submission to PyTorch-based services
SIEM Query:
source="application.logs" AND ("segmentation fault" OR "memory corruption" OR "torch.nan_to_num")