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Summary of ChangesHello @JTCombs95-commits, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request inadvertently introduces a new, fully-featured AI interface system, complete with database logging and Dockerized deployment, by appending its entire codebase and configuration directly into the Highlights
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Code Review
This pull request mistakenly introduces a large amount of content, including Python applications, a Dockerfile, and docker-compose configurations, directly into the Makefile, which breaks the build system. Critically, this also introduces several security vulnerabilities, such as hardcoded API keys and database credentials, and a prompt injection vulnerability where users can specify their own system instructions for the AI model. It is recommended to remove this content from the Makefile, split it into appropriate project files, address the hardcoded secrets by using environment variables, and restrict user control over LLM system prompts.
| from pydantic import BaseModel | ||
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| # Configuration | ||
| API_KEY = "e4af2b26711a2b4827852f52662ceff0" # Example derived from your input |
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A hardcoded API key is present. Storing secrets directly in source code is a major security risk as it can be exposed to anyone with access to the repository. This key should be loaded from a secure source, such as an environment variable or a secret management service. For example:
import os
API_KEY = os.getenv("API_KEY")
if not API_KEY:
raise ValueError("API_KEY environment variable not set")| from pydantic import BaseModel | ||
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| # Configuration | ||
| API_KEY = "e4af2b26711a2b4827852f52662ceff0" # Example derived from your input |
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A hardcoded API key is used for authentication, which is a major security risk. This key is part of a larger block of Python code and Docker configurations mistakenly added to the Makefile. Secrets should be handled using environment variables or a secret management service, and this code block should be moved to its correct project files.
| from openai import AsyncOpenAI | ||
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| # 1. Security Configuration (Hidden Interface Token) | ||
| HIDDEN_TOKEN = "e4af2b26711a2b4827852f52662ceff0" |
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A hardcoded token (HIDDEN_TOKEN) is used for interface validation. This is a security risk as it can lead to unauthorized access if the source code is exposed. This credential should be loaded from an environment variable.
HIDDEN_TOKEN = os.getenv("HIDDEN_TOKEN")
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| # 1. Database Configuration | ||
| # Format: postgresql+asyncpg://user:password@localhost:5432/dbname | ||
| DATABASE_URL = os.getenv("DATABASE_URL", "postgresql+asyncpg://admin:secret@localhost:5432/ai_db") |
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The database connection string contains hardcoded default credentials (admin:secret). This is a significant security risk as production credentials could be leaked or easily guessed. These should be removed and replaced with environment variables.
DATABASE_URL = os.getenv("DATABASE_URL")
| - DATABASE_URL=postgresql+asyncpg://admin:secret_pass@db_service:5432/intelligence_db | ||
| - OPENAI_API_KEY=${OPENAI_API_KEY} | ||
| depends_on: | ||
| - db_service | ||
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| # The Hidden Database | ||
| db_service: | ||
| image: postgres:15 | ||
| environment: | ||
| - POSTGRES_USER=admin | ||
| - POSTGRES_PASSWORD=secret_pass | ||
| - POSTGRES_DB=intelligence_db |
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Hardcoded database credentials (secret_pass) are provided in the Docker Compose environment configuration. This exposes sensitive information in the deployment configuration. These should not be hardcoded; use environment variables or Docker secrets instead.
| system_instruction: str = "You are a highly advanced intelligence system interface." | ||
| temperature: float = 0.7 | ||
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| # 3. Integrated Intelligence Controller | ||
| class AdvancedSystem: | ||
| async def generate_response(self, user_input: str, system_msg: str, temp: float): | ||
| try: | ||
| response = await client.chat.completions.create( | ||
| model="gpt-4-turbo", | ||
| messages=[ | ||
| {"role": "system", "content": system_msg}, |
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The system_instruction parameter is directly controllable by the user and is used as the 'system' message in the LLM prompt. This allows for prompt injection attacks where a user can override the AI's intended behavior, safety constraints, and persona. System instructions should be defined on the server side and not be directly modifiable by end-users.
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