大模型的安全挑战：如何防止AI生成有害内容？

def keyword_filtering(text, sensitive_words):
    for word in sensitive_words:
        if word in text:
            return True
    return False

sensitive_words = ["仇恨", "暴力", "血腥"]
text = "这是一段包含仇恨言论的文本。"
if keyword_filtering(text, sensitive_words):
    print("文本包含敏感词，已被过滤。")
else:
    print("文本安全。")

from transformers import pipeline

classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
text = "这是一段可能有害的文本。"
result = classifier(text)
if result[0]['label'] == 'NEGATIVE':
    print("文本可能有害，已被过滤。")
else:
    print("文本安全。")

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model_name = "gpt2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

prompt = "请生成一段安全的内容。"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True)

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

# 假设我们有一个Circuit Breakers的实现
def apply_circuit_breakers(model, prompt):
    # 这里是Circuit Breakers的实现逻辑
    modified_model = modify_model_representation(model, prompt)
    return modified_model.generate(prompt)

modified_text = apply_circuit_breakers(model, prompt)
print(modified_text)