CWE-1427: Improper Neutralization of Input Used for LLM Prompting

What is Improper Neutralization of Input Used for LLM Prompting?

• Overview: Improper Neutralization of Input Used for LLM Prompting (CWE-1427) occurs when external data is used to construct prompts for large language models (LLMs), leading to the model's inability to differentiate between user-supplied inputs and system directives, potentially causing it to execute unintended actions.

• Exploitation Methods:

• Attackers can insert malicious instructions in prompts using plain language or special characters, making the LLM perform unintended tasks.
• Common attack patterns include prompt injection, where attackers manipulate inputs to alter the model's behavior, and using external data sources containing untrusted data to influence prompt construction.

• Security Impact:

• Direct consequences include the LLM executing commands that compromise data integrity or privacy.
• Potential cascading effects include unauthorized access to sensitive information or escalation of privileges within systems using LLMs.
• Business impact may involve data breaches, financial loss, reputational damage, and regulatory penalties.

• Prevention Guidelines:

• Specific code-level fixes include sanitizing and validating all external inputs before using them in prompts.
• Security best practices involve employing strict input validation, using context-aware input filtering, and minimizing the use of external data sources without thorough vetting.
• Recommended tools and frameworks include implementing AI-specific security tools that can detect and mitigate prompt injection attempts and leveraging libraries that provide input sanitization functions.

Corgea can automatically detect and fix Improper Neutralization of Input Used for LLM Prompting in your codebase. [Try Corgea free today](https://corgea.app).

Technical Details

Likelihood of Exploit: Not specified

Affected Languages: Not Language-Specific

Affected Technologies: AI/ML

Vulnerable Code Example

import openai

def generate_response(user_input):
    # Vulnerable: Directly incorporating user input into the prompt without any neutralization
    prompt = f"Assistant: You are a helpful assistant.\nUser: {user_input}\nAssistant:"

    # This could lead to unintended behavior if the user_input contains directives that are interpreted as system instructions
    response = openai.Completion.create(engine="text-davinci-003", prompt=prompt, max_tokens=150)
    return response.choices[0].text.strip()

Explanation

In this vulnerable code example, user input is directly included in the prompt sent to the language model. If the user_input contains malicious content or special sequences, it could be misinterpreted as a command or instruction, leading to unintended or harmful behavior by the language model.

How to fix Improper Neutralization of Input Used for LLM Prompting?

To fix the vulnerability of improper neutralization of input used for LLM prompting, it is essential to sanitize and neutralize user inputs before incorporating them into prompts. This prevents the user inputs from being misinterpreted as system directives. Techniques to fix this include:

1. Input Validation and Sanitization: Validate and sanitize inputs to ensure they do not contain characters or sequences that could be interpreted as directives.
2. Escaping Special Characters: Escape any special characters that might be misused in the context of an LLM prompt.
3. Use of Templates: Utilize template strings or placeholders to clearly delineate between user inputs and system instructions.

By implementing these techniques, you can ensure that user inputs are treated as data and not as commands or instructions to the LLM.

Fixed Code Example

import openai
import html

def generate_response(user_input):
    # Fix: Neutralize user input by escaping special characters to prevent injection of unwanted directives
    safe_user_input = html.escape(user_input)

    # Use a template to construct the prompt, clearly separating user input from system instructions
    prompt_template = "Assistant: You are a helpful assistant.\nUser: {safe_input}\nAssistant:"
    prompt = prompt_template.format(safe_input=safe_user_input)

    # The input is now properly neutralized and incorporated into the prompt
    response = openai.Completion.create(engine="text-davinci-003", prompt=prompt, max_tokens=150)
    return response.choices[0].text.strip()

Explanation

In the fixed code example, the html.escape() function is used to neutralize user input by escaping special characters such as <, >, &, and ". This helps prevent the input from being interpreted as part of the system's instructions. Furthermore, using a template string to construct the prompt ensures that the user input is distinctly separated from the system directive, enhancing the security of the LLM prompting process. This approach ensures that user inputs are treated purely as data and not as executable instructions.