CVE-2024-31583
📋 TL;DR
PyTorch versions before v2.2.0 contain a use-after-free vulnerability in the mobile interpreter component. This allows attackers to potentially execute arbitrary code or cause denial of service by manipulating freed memory. Users running vulnerable PyTorch versions in production or development environments are affected.
💻 Affected Systems
- PyTorch
📦 What is this software?
Pytorch by Linuxfoundation
⚠️ Risk & Real-World Impact
Worst Case
Remote code execution leading to complete system compromise, data theft, or ransomware deployment.
Likely Case
Application crash or denial of service affecting machine learning inference pipelines.
If Mitigated
Limited impact with proper sandboxing and memory protection mechanisms in place.
🎯 Exploit Status
Exploitation requires control over PyTorch mobile model inputs or interpreter state.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: v2.2.0 and later
Vendor Advisory: https://github.com/pytorch/pytorch/security/advisories
Restart Required: Yes
Instructions:
1. Update PyTorch to v2.2.0 or later using pip: 'pip install --upgrade torch>=2.2.0' 2. Restart all applications using PyTorch. 3. Rebuild any containers or deployments with updated version.
🔧 Temporary Workarounds
Disable mobile interpreter
allAvoid using PyTorch's mobile interpreter component if not required
Memory hardening
linuxEnable ASLR and other memory protection mechanisms
sysctl -w kernel.randomize_va_space=2
🧯 If You Can't Patch
- Isolate PyTorch processes with containerization or sandboxing
- Implement strict input validation for PyTorch mobile models
🔍 How to Verify
Check if Vulnerable:
Check PyTorch version: 'python -c "import torch; print(torch.__version__)"' - if version < 2.2.0, vulnerable.Check Version:
python -c "import torch; print(torch.__version__)"Verify Fix Applied:
Verify version is >=2.2.0 and test mobile interpreter functionality.📡 Detection & Monitoring
Log Indicators:
- Segmentation faults in PyTorch processes
- Unexpected memory access errors
- Mobile interpreter crashes
Network Indicators:
- Unusual outbound connections from ML inference services
SIEM Query:
source="*pytorch*" AND ("segmentation fault" OR "SIGSEGV" OR "use-after-free")🔗 References
- https://gist.github.com/1047524396/43e19a41f2b36503a4a228c32cdbc176
- https://github.com/pytorch/pytorch/blob/v2.1.2/torch/csrc/jit/mobile/interpreter.cpp#L132
- https://github.com/pytorch/pytorch/commit/9c7071b0e324f9fb68ab881283d6b8d388a4bcd2
- https://gist.github.com/1047524396/43e19a41f2b36503a4a228c32cdbc176
- https://github.com/pytorch/pytorch/blob/v2.1.2/torch/csrc/jit/mobile/interpreter.cpp#L132
- https://github.com/pytorch/pytorch/commit/9c7071b0e324f9fb68ab881283d6b8d388a4bcd2
