Like Manus, Computer Use Agent(CUA) and Omniparser, we are computer-using agents.AI-driven local automation assistant that uses natural language to make computers work by themselves
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"automate": {
"env": {
"OPENAI_MODEL": "gpt-4o",
"OPENAI_API_KEY": "sk-...",
"OPENAI_BASE_URL": "https://api.openai.com/v1"
},
"args": [
"/absolute/path/to/autoMate/mcp_server.py"
],
"command": "python"
}
}
}Are you the author?
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Give Claude hands and eyes — automate any desktop app, even if it has no API
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'torch' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
PYSEC-2025-41
PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. In version 2.5.1 and prior, a Remote Command Execution (RCE) vulnerability exists in PyTorch when loading a model using torch.load with weights_only=True. This issue has been patched in version 2.6.0.
PyTorch Improper Resource Shutdown or Release vulnerability
A vulnerability, which was classified as problematic, was found in PyTorch 2.6.0. Affected is the function torch.nn.functional.ctc_loss of the file aten/src/ATen/native/LossCTC.cpp. The manipulation leads to denial of service. An attack has to be approached locally. The exploit has been disclosed to the public and may be used. The name of the patch is 46fc5d8e360127361211cb237d5f9eef0223e567. It is recommended to apply a patch to fix this issue.
PyTorch susceptible to local Denial of Service
A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0+cu124. Affected by this issue is the function torch.mkldnn_max_pool2d. The manipulation leads to denial of service. An attack has to be approached locally. The exploit has been disclosed to the public and may be used.
PYSEC-2024-259
In PyTorch <=2.4.1, the RemoteModule has Deserialization RCE. NOTE: this is disputed by multiple parties because this is intended behavior in PyTorch distributed computing.
PYSEC-2024-250
Pytorch before v2.2.0 has an Out-of-bounds Read vulnerability via the component torch/csrc/jit/mobile/flatbuffer_loader.cpp.
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