在模型加载和合并管道中，通过不受限制的 `torch.load()` 调用进行不安全的反序列化

def load_model_from_config(config, ckpt, verbose=False):
    logger.info(f"Loading model from {ckpt}")
    try:
        # Vulnerable: Deserializes the file using unrestricted pickle unpickling
        pl_sd = torch.load(ckpt, map_location="cpu")
        sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd

def merge(file1, file2, out, a):
    # Vulnerable: Accepts arbitrary user file paths from GUI entry fields
    model_0 = torch.load(file1)
    model_1 = torch.load(file2)

def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None):
    # Vulnerable: Deserializes learned token embedding
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