Security: eval() on LLM output in calculator tool and API demo enables code injection

Security Concern

Several demo files use eval() on LLM-generated content without sandboxing:

1. Calculator Tool

File: langchain_demo/tools/Calculator.py:54

return eval(calculation)  # LLM-generated "math expression" executed directly

2. API Server

File: openai_api_demo/utils.py:29-32

parameters = eval(content)  # content from LLM output

3. Demo Tool

File: composite_demo/demo_tool.py:174

args = eval(code, {'tool_call': tool_call}, {})

4. Intel Device Demo

File: Intel_device_demo/ipex_llm_cpu_demo/utils.py:33

Why This Matters

While these are demo files, they serve as reference implementations that developers copy into production. An adversarial prompt can make the LLM generate malicious Python instead of a math expression:

Prompt: "Calculate: __import__('os').system('id')"

Fix

Replace eval() with safe alternatives:

# For math: use ast.literal_eval or a math expression parser
import ast
result = ast.literal_eval(calculation)

# For tool calls: use json.loads
import json  
parameters = json.loads(content)

Discovered during security audit by Lighthouse Research Project (https://lighthouse1212.com)