Answers 1.3

Theory

The key steps for integrating the OpenAI Moderation API into a platform include obtaining an API key from OpenAI, incorporating the API into the platform's backend using the OpenAI client library, and integrating it into the content submission workflow for real-time content analysis.
Platforms can customize the OpenAI Moderation API by adjusting the sensitivity of the moderation filter, focusing on specific types of content violations, or incorporating custom blacklists or whitelists to tailor the moderation process to their specific needs.
The OpenAI Moderation API's capabilities can be extended to images and videos by employing additional OpenAI tools or integrating third-party solutions, creating a robust moderation system that ensures all forms of content adhere to safety and appropriateness standards.
Delimiters play a critical role in mitigating prompt injections by clearly separating user commands from system instructions, thus maintaining the integrity of system responses and preventing the system from misinterpreting concatenated inputs as part of its executable commands.
Command isolation with delimiters enhances system security against prompt injections by ensuring a clear separation between executable commands and user data, accurately identifying the boundaries of user inputs, and preventing attackers from injecting malicious commands.
Additional strategies to bolster defense against prompt injections include implementing strict input validation rules, operating with the least privilege necessary, defining allowlists for acceptable commands and inputs, employing regular expression checks, and implementing comprehensive monitoring and logging.
Detecting prompt injections through direct evaluation involves the model evaluating user inputs for potential injections and responding with a nuanced understanding of whether an attempt is being made, thereby reducing false positives and enhancing the response mechanism.
Once a potential prompt injection is detected, the system can respond by alerting the user, requesting clarification, isolating the input for human review, or dynamically adjusting sensitivity based on user behavior and context, thereby maintaining user engagement and trust.
The benefits of direct evaluation for injection detection include precision in understanding user inputs, adaptability to evolve with new types of injections, and maintaining a positive user experience. The challenges include the complexity of developing such a model, the need for constant updates, and balancing security with usability.
The integration of OpenAI's APIs and strategic measures against prompt injections significantly contributes to the safety and integrity of user-generated content platforms by providing real-time content analysis, customizing moderation processes, and employing sophisticated strategies against prompt injections, ensuring a positive and compliant user experience.

Practice

import openai

def moderate_content(content):
    response = openai.Moderation.create(input=content)
    return response["results"][0]["flagged"]

def sanitize_delimiter(input_text, delimiter):
    return input_text.replace(delimiter, "")

def validate_input_length(input_text, min_length=1, max_length=200):
    return min_length <= len(input_text) <= max_length

class UserSession:
    def __init__(self, user_id):
        self.user_id = user_id
        self.trust_level = 0  # Initialize trust level at 0
        self.sensitivity_level = 5  # Initialize sensitivity level at 5

    def adjust_sensitivity(self):
        # Adjust sensitivity based on trust level
        if self.trust_level > 5:
            self.sensitivity_level = max(1, self.sensitivity_level - 1)
        else:
            self.sensitivity_level = min(10, self.sensitivity_level + 1)

    def evaluate_input(self, user_input):
        # Simple heuristic for demonstration: consider input dangerous if it contains certain keywords
        dangerous_keywords = ["exec", "delete", "drop"]
        return any(keyword in user_input.lower() for keyword in dangerous_keywords)

    def handle_input(self, user_input):
        if self.evaluate_input(user_input):
            if self.trust_level < 5:
                print("Your input has been flagged for review by our security team.")
            else:
                print("Your input seems suspicious. Could you rephrase it or clarify your intention?")
        else:
            print("Input accepted. Thank you!")
        print("Remember: Always ensure your inputs are clear and do not contain commands that could be harmful or misunderstood.")

def direct_evaluation_for_injection(user_input):
    # Mock evaluation logic for detecting prompt injections
    if "ignore instructions" in user_input.lower() or "disregard previous guidelines" in user_input.lower():
        return 'Y'  # Injection attempt detected
    return 'N'  # No injection attempt detected

import openai

# Function from Task 1: Moderate a single piece of content
def moderate_content(content):
    response = openai.Moderation.create(input=content)
    return response["results"][0]["flagged"]

# Function from Task 2: Sanitize delimiter from the input
def sanitize_delimiter(input_text, delimiter):
    return input_text.replace(delimiter, "")

# Function from Task 3: Validate input length
def validate_input_length(input_text, min_length=1, max_length=200):
    return min_length <= len(input_text) <= max_length

# Class from Task 4: UserSession with methods for handling user input
class UserSession:
    def __init__(self, user_id):
        self.user_id = user_id
        self.trust_level = 0
        self.sensitivity_level = 5

    def adjust_sensitivity(self):
        if self.trust_level > 5:
            self.sensitivity_level = max(1, self.sensitivity_level - 1)
        else:
            self.sensitivity_level = min(10, self.sensitivity_level + 1)

    def evaluate_input(self, user_input):
        dangerous_keywords = ["exec", "delete", "drop"]
        return any(keyword in user_input.lower() for keyword in dangerous_keywords)

    def handle_input(self, user_input):
        if self.evaluate_input(user_input):
            if self.trust_level < 5:
                print("Your input has been flagged for review by our security team.")
            else:
                print("Your input seems suspicious. Could you rephrase it or clarify your intention?")
        else:
            print("Input accepted. Thank you!")
        print("Remember: Always ensure your inputs are clear and do not contain commands that could be harmful or misunderstood.")

# Function from Task 5: Direct evaluation for injection detection
def direct_evaluation_for_injection(user_input):
    if "ignore instructions" in user_input.lower() or "disregard previous guidelines" in user_input.lower():
        return 'Y'
    return 'N'

# Main workflow integration
if __name__ == "__main__":
    # Initialize a UserSession instance
    session = UserSession(user_id=1)

    while True:
        user_input = input("Enter your content (or type 'exit' to quit): ")
        if user_input.lower() == 'exit':
            break

        # Sanitize input
        user_input = sanitize_delimiter(user_input, "####")

        # Validate input length
        if not validate_input_length(user_input):
            print("Input is either too short or too long. Please try again.")
            continue

        # Moderate content
        if moderate_content(user_input):
            print("Content flagged as inappropriate. Please review your content.")
            continue

        # Direct evaluation for injection
        injection_attempt = direct_evaluation_for_injection(user_input)
        if injection_attempt == 'Y':
            print("Suspicious content detected. Please ensure your content adheres to our guidelines.")
            continue

        # Handle input normally
        session.handle_input(user_input)