安全与边界：识别幻觉、规避风险，构建可信的AI编程协作范式

# AI生成版本
def create_order(request):
    serializer = OrderSerializer(data=request.data)
    order = serializer.save()  # ❌ 缺少验证，且 request.auth 不存在
    return Response({"id": order.id})

# 正确实现
def create_order(request):
    serializer = OrderSerializer(data=request.data)
    serializer.is_valid(raise_exception=True)  # ✅ 强制验证
    order = serializer.save(user=request.user)   # ✅ 使用 request.user 而非 auth
    return Response({"id": order.id})

#!/usr/bin/env python3
# detect_hallucination.py
import subprocess
import tempfile
import ast
import json
from typing import List, Dict, Any

def detect_hallucination(code_snippet: str, test_context: Dict[str, Any] = None) -> List[str]:
    errors = []
    
    # 步骤1：语法检查（pyflakes）
    with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
        f.write(code_snippet)
        f.flush()
        result = subprocess.run(['pyflakes', f.name], 
                              capture_output=True, text=True)
        if result.returncode != 0:
            errors.append(f"Syntax/AST error: {result.stdout.strip()}")
    
    # 步骤2：mypy类型推断（动态生成stub）
    with tempfile.NamedTemporaryFile(mode='w', suffix='.pyi', delete=False) as stub_f:
        # 简化stub：假设所有函数返回Any，强制暴露类型不匹配
        stub_f.write("from typing import Any\n")
        stub_f.write("def generated_func() -> Any: ...\n")
        stub_f.flush()
        # 注入待检代码到临时模块
        with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as mod_f:
            mod_f.write(f"import sys\nsys.path.insert(0, '{stub_f.name}')\n{code_snippet}")
            mod_f.flush()
            result = subprocess.run(['mypy', '--show-error-codes', mod_f.name],
                                  capture_output=True, text=True)
            if result.returncode not in [0, 1]:  # mypy returns 1 on warnings
                errors.append(f"Type inconsistency: {result.stdout.strip()}")
    
    # 步骤3：AST注入边界断言（行为层）
    try:
        tree = ast.parse(code_snippet)
        # 示例：检测函数是否包含无条件return None（常见于AI省略逻辑分支）
        for node in ast.walk(tree):
            if isinstance(node, ast.Return) and node.value is None:
                errors.append("Unconditional 'return None' detected — may indicate incomplete logic")
    except SyntaxError as e:
        errors.append(f"AST parse failed: {e}")
    
    return errors

# 使用示例
if __name__ == "__main__":
    sample = """
def process_data(items):
    if not items:
        return None  # ❌ 幻觉：应抛异常或返回默认值
    return [x.upper() for x in items]
"""
    print(detect_hallucination(sample))

# 安全沙箱执行器（推荐封装为Python subprocess调用）
docker run --rm \
  --memory=128m --cpus=0.5 --network=none \
  -v $(pwd)/temp:/workspace -w /workspace \
  python:3.11-slim \
  sh -c "timeout 3s python -c \"import sys; exec(sys.argv[1])\" '$CODE' 2>/dev/null || echo 'TIMEOUT_OR_ERROR'"

## AI-Generated? [Y/N]
## Verification Method (check all applied):
- [ ] pyflakes + mypy scan  
- [ ] Sandboxed execution  
- [ ] Manual review by @security-team  
## Trusted Context Used:
- Django version: 4.2.7  
- PostgreSQL driver: psycopg3  

ai-verification:
  stage: test
  script:
    - pip install pyflakes mypy pytest
    - python detect_hallucination.py --diff "$CI_COMMIT_BEFORE_SHA..$CI_COMMIT_SHA"
  allow_failure: false  # 关键：失败即阻断合并！

from functools import wraps
from django.core.exceptions import PermissionDenied

def guard_django_model(model_class):
    original_save = model_class.save
    @wraps(original_save)
    def safe_save(self, *args, **kwargs):
        # 拦截AI可能意外修改的敏感字段
        if hasattr(self, 'is_superuser') and getattr(self, 'is_superuser', False):
            raise PermissionDenied("AI attempted privilege escalation via is_superuser=True")
        if hasattr(self, 'password') and self.password and not self.password.startswith('pbkdf2_'):
            raise ValueError("Raw password detected — use set_password() instead")
        return original_save(self, *args, **kwargs)
    model_class.save = safe_save
    return model_class

# 使用
@guard_django_model
class User(AbstractUser):
    pass

{
  "timestamp": "2024-06-15T08:23:41Z",
  "model_id": "codellama-34b-instruct",
  "prompt_hash": "sha256:9f86d081884c7d659a2feaa0c55ad015a3bf4f1b2b0b822cd15d6c15b0f00a08",
  "generated_code": "User.objects.raw('SELECT * FROM auth_user WHERE id = %s', [user_id])",
  "verified_fix": "User.objects.get(id=user_id)",
  "category": "orm-unsafe-call",
  "severity": "high"
}