Inclusion of Functionality from Untrusted Control Sphere

- func handler(w http.ResponseWriter, r *http.Request) {
-     modelPath := r.FormValue("model")
-     model, _ := loadModel(modelPath)
-     resp, _ := http.Get("http://example.com/model.onnx")
+ var allowedModels = map[string]string{
+     "sentiment": "https://models.example.com/sentiment-v2.onnx",
+     "classify":  "https://models.example.com/classify-v1.onnx",
+ }
+ 
+ func handler(w http.ResponseWriter, r *http.Request) {
+     modelID := r.FormValue("model")
+     url, ok := allowedModels[modelID]
+     if !ok {
+         http.Error(w, "invalid model", http.StatusBadRequest)
+         return
+     }
+     data, _ := downloadModel(url)
+     if !verifyChecksum(data, expectedChecksums[modelID]) {
+         return fmt.Errorf("checksum verification failed")
+     }
+     model, _ := loadModel(data)
  }
  

- app.post('/predict', async (req, res) => {
-   const model = await loadModel(req.body.modelId);
+ const ALLOWED_MODELS = { 'sentiment-v1': true, 'classify-v2': true };
+ 
+ app.post('/predict', async (req, res) => {
+   if (!ALLOWED_MODELS[req.body.modelId]) {
+     return res.status(400).json({ error: 'Model not allowed' });
+   }
+   const model = await loadVerifiedModel(req.body.modelId);
    const result = await model.predict(req.body.input);
  });
  

  apiVersion: v1
  kind: Pod
  spec:
    containers:
    - name: app
-     image: nginx:latest
+     image: nginx:1.25.3-alpine
  

  import torch
  from transformers import AutoModel
- 
- model = torch.load('model.pt')
- nlp_model = AutoModel.from_pretrained('custom/model', trust_remote_code=True)
+ from safetensors.torch import load_model
+ 
+ # Safe: weights_only prevents arbitrary code execution
+ model = torch.load('model.pt', weights_only=True)
+ 
+ # Even safer: use SafeTensors format
+ load_model(model, 'model.safetensors')
+ 
+ # Allowlist for HuggingFace models
+ ALLOWED_MODELS = ['bert-base-uncased', 'distilbert-base-uncased']
+ model_id = request.json['model']
+ if model_id not in ALLOWED_MODELS:
+     raise ValueError('Model not in allowlist')
+ nlp_model = AutoModel.from_pretrained(model_id)