CVE-2025-55552
📋 TL;DR
CVE-2025-55552 is an integer overflow vulnerability in PyTorch v2.8.0 that occurs when torch.rot90 and torch.randn_like functions are used together, potentially causing memory corruption or crashes. This affects any application or system using PyTorch v2.8.0 for machine learning workloads.
💻 Affected Systems
- PyTorch
📦 What is this software?
Pytorch by Linuxfoundation
⚠️ Risk & Real-World Impact
Worst Case
Remote code execution leading to complete system compromise if combined with other vulnerabilities, or denial of service causing critical ML inference failures.
Likely Case
Application crashes or unexpected behavior during tensor operations, disrupting ML model training or inference pipelines.
If Mitigated
Controlled crashes with proper error handling, minimal data loss if operations are sandboxed.
🎯 Exploit Status
Proof of concept demonstrates crash but not full exploitation. Requires specific function calls within application code.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: v2.8.1 or later
Vendor Advisory: https://github.com/pytorch/pytorch/issues/147847
Restart Required: No
Instructions:
1. Update PyTorch: pip install torch==2.8.1 2. Verify installation: python -c 'import torch; print(torch.__version__)' 3. Test affected code patterns.
🔧 Temporary Workarounds
Avoid vulnerable function combination
allRefactor code to avoid using torch.rot90 and torch.randn_like together
# Review code for patterns like: torch.rot90(torch.randn_like(...))
# Replace with alternative tensor operations
🧯 If You Can't Patch
- Implement input validation and sanitization for tensor operations
- Run PyTorch in isolated containers with resource limits to contain crashes
🔍 How to Verify
Check if Vulnerable:
Check if code uses torch.rot90 with torch.randn_like. Test with sample inputs to see if crashes occur.Check Version:
python -c 'import torch; print(torch.__version__)'Verify Fix Applied:
After updating, test previously crashing code patterns to ensure normal operation.📡 Detection & Monitoring
Log Indicators:
- PyTorch segmentation faults
- Python process crashes during tensor operations
- Error messages mentioning rot90 or randn_like
Network Indicators:
- Unusual ML API response patterns
- Increased error rates in inference services
SIEM Query:
process_name:python AND (event_type:crash OR error_message:*rot90* OR error_message:*randn_like*)