What is execute_python and How AI Executes Code in a Sandbox

## Introduction When we ask ChatGPT to write code, solve a math problem, or process data, a complex process happens behind the scenes. AI doesn't just "think" — it often executes real Python code in an isolated environment to get an accurate result. This technology is called `execute_python`, and today we'll break down how it works, why it's safe, and how AI ensures protection against malicious actions. Have you ever wondered how a neural network can accurately compute the 100th Fibonacci number or build a complex graph? The answer lies in the sandbox. In this article, we'll reveal the details of AI code execution, show examples, and explain why `execute_python` is a key tool for modern intelligent systems. ## What is execute_python? `execute_python` is a function or API that allows an AI model (e.g., GPT-4, Claude, or Gemini) to generate Python code, send it for execution in an isolated environment (sandbox), and receive the result back. This is not just "running code" — it's a whole pipeline: 1. **Code generation** — AI writes a Python script based on the user's request. 2. **Security check** — the code is analyzed for dangerous operations (file reading, network requests, infinite loops). 3. **Execution in sandbox** — the code runs in a container or virtual machine with limited resources. 4. **Return result** — stdout, stderr, created files, or exceptions are passed back to the AI. ### How does it look in practice? Imagine you write: "Find the sum of all prime numbers from 1 to 1000." AI won't iterate through them in its head — it will write code and execute it: ```python def is_prime(n): if n < 2: return False for i in range(2, int(n**0.5) + 1): if n % i == 0: return False return True result = sum(i for i in range(1, 1001) if is_prime(i)) print(result) ``` After execution in the sandbox, AI gets the answer: `76127`. ## Sandbox architecture: how security is ensured A sandbox is an isolated environment that prevents any malicious actions by the code. Main mechanisms: - **Containerization** — code runs in a Docker container without access to the host system. - **Resource limits** — CPU, memory, and execution time are strictly limited (e.g., 5 seconds, 256 MB RAM). - **System call blocking** — operations with the file system (except temporary folder), network connections, and spawning child processes are prohibited. - **Real-time monitoring** — if the code tries to execute a dangerous command (e.g., `os.system('rm -rf /')`), execution is immediately interrupted. ### Why is this important? AI might accidentally generate code that: - Goes into an infinite loop. - Tries to access private data. - Uses too much memory. The sandbox guarantees that even in case of an error or malicious intent (if someone deliberately forces AI to write harmful code) — the system remains intact. ## Examples of using execute_python ### 1. Mathematical calculations AI can perform complex computations that are impossible with purely textual methods: ```python import numpy as np from scipy.integrate import quad # Computing a definite integral result, error = quad(lambda x: np.sin(x**2), 0, 1) print(f"Result: {result}, error: {error}") ``` ### 2. Data processing The user uploads a CSV file, AI reads it, cleans it, and plots a graph: ```python import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv('data.csv') df['date'] = pd.to_datetime(df['date']) plt.plot(df['date'], df['value']) plt.savefig('plot.png') ``` The image is saved and displayed to the user. ### 3. Algorithm testing AI can test the operation of written code on different input data: ```python def sort_list(arr) ```

What is execute_python and How AI Executes Code in a Sandbox

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