Fix code scanning alert - Missing rate limiting

[MarBeanInc1111]

Tracking issue for:

• [ ] https://github.com/MarBeanInc1111/refactored-engine/security/code-scanning/4

[codeautopilot[bot]]

Potential solution

To solve the bug, we need to implement a proactive rate limiting mechanism that can track and limit the number of requests a user makes within a specified time window. This should be done before the requests reach the API call functions to prevent rate limit errors from occurring. The solution should be integrated into the middleware layer of the application, which will allow for centralized management of rate limiting across all endpoints.

What is causing this bug?

The bug is caused by the absence of a proactive rate limiting system. The current implementation only handles rate limit errors after they have occurred, rather than preventing them. Without a proper rate limiting mechanism in place, users can make an unlimited number of requests in a short period, which can lead to abuse and potential service outages.

Code

To address the missing rate limiting, we can implement a middleware function in pilot/utils/middleware.py that uses an in-memory store or a more persistent solution like Redis to track request counts. Here's a simplified version of the middleware function:

from flask import request, Response
from functools import wraps
import time

# Define rate limits for users
RATE_LIMITS = {
    "user": {"calls": 100, "period": 3600}  # 100 calls per hour per user
}

# In-memory storage for tracking requests
request_counters = {}

def rate_limit(route_function):
    @wraps(route_function)
    def wrapper(*args, **kwargs):
        user_id = request.headers.get("X-User-ID")
        current_time = time.time()

        # Initialize or update the request count and timestamp
        if user_id not in request_counters or current_time - request_counters[user_id]['timestamp'] > RATE_LIMITS["user"]["period"]:
            request_counters[user_id] = {"count": 1, "timestamp": current_time}
        else:
            request_counters[user_id]["count"] += 1

        # Check if the request count exceeds the rate limit
        if request_counters[user_id]["count"] > RATE_LIMITS["user"]["calls"]:
            return Response("Too many requests", status=429)

        return route_function(*args, **kwargs)

    return wrapper

This middleware should be applied to all relevant endpoints in the application to ensure rate limiting is enforced.

Additionally, the User model in pilot/database/models/user.py can be extended with request_count and last_request_time fields to support rate limiting at the database level, which can be useful for persistent tracking across application restarts or in a distributed system.

How to replicate the bug

To replicate the bug, one would need to make a series of API requests to the application's endpoints in quick succession, exceeding what would be considered a reasonable request rate. Since there is no rate limiting in place, the application will not prevent or flag this behavior, which can be observed by the lack of error responses or warnings related to excessive requests.

Task

To complete the task, the following steps should be taken:

1. Implement the rate limiting middleware function in pilot/utils/middleware.py as outlined in the code snippet above.
2. Integrate the middleware with the application's web framework, applying it to all relevant endpoints.
3. Modify the User model in pilot/database/models/user.py to include the request_count and last_request_time fields, if persistent tracking is required.
4. Test the application to ensure that the rate limiting is working as expected, by making requests and verifying that excessive requests are being limited with a 429 Too Many Requests response.
5. Review the pilot/utils/llm_connection.py file to ensure that any existing rate limit handling logic is compatible with the new middleware and adjust as necessary.

Click here to create a Pull Request with the proposed solution

Files used for this task:

Changes on pilot/database/models/user.py

The provided `user.py` file defines a simple `User` model with `email` and `password` fields. To implement rate limiting, we need to track the number of requests a user makes within a certain time frame. This typically involves adding fields to store the count of requests and the timestamp of the last request. Here's a report on what changes could be made to the `User` model to support rate limiting: 1. **Request Count Field**: Add an integer field to track the number of requests a user has made in the current time window. 2. **Timestamp Field**: Add a datetime field to track the time of the last request made by the user. This will help in resetting the request count when the time window expires. 3. **Time Window Field**: Optionally, you could add a field to customize the time window for rate limiting on a per-user basis. Here's an example of how the `User` model could be modified: ```python from peewee import CharField, IntegerField, DateTimeField from datetime import datetime from database.models.components.base_models import BaseModel class User(BaseModel): email = CharField(unique=True) password = CharField() request_count = IntegerField(default=0) last_request_time = DateTimeField(default=datetime.now) # Optional: rate_limit_window = IntegerField(default=60) # Time window in minutes ``` With these changes, the application can track how many requests a user has made and when the last request occurred. The rate limiting logic would then reset the `request_count` when the current time exceeds the `last_request_time` plus the rate limiting time window. Please note that the actual implementation of the rate limiting logic would not be done in this model file but rather in middleware or a separate utility that checks and updates these fields with each request. If the application requires more sophisticated rate limiting, such as different limits for different types of users or endpoints, additional fields or related models might be necessary. In conclusion, the `User` model in `user.py` needs to be extended with additional fields to support rate limiting. The fields `request_count` and `last_request_time` are essential for a basic rate limiting feature. Additional logic to enforce rate limiting will need to be implemented elsewhere in the application, such as in middleware.

Changes on pilot/main.py

After reviewing the provided source code in `pilot/main.py`, it appears that this file is the main entry point for a Python application. It handles the initialization of the application, argument parsing, environment setup, and the main execution flow including error handling and graceful exits. Regarding the task at hand, which is to implement or integrate a rate limiting middleware to protect the endpoints from abuse, there are a few key points to consider: 1. **Middleware Integration**: The current file does not show any web server or API framework being used. Middleware is typically integrated into web frameworks like Flask, Django, or FastAPI. Since this file does not show the use of such a framework, it's not possible to directly implement middleware here. 2. **Endpoint Definition**: There is no indication of any API endpoints being defined in this file. Rate limiting is usually applied to API endpoints to prevent abuse, but without knowing where these endpoints are defined, it's not possible to apply rate limiting in this file. 3. **Rate Limiting Logic**: To implement rate limiting, you would need to keep track of the number of requests made by a user or IP address within a certain time frame and limit the requests once a threshold is reached. This logic is not present in the file. 4. **External Libraries**: If the application does have web server functionality, it might be using an external library for handling HTTP requests. In such a case, rate limiting could be implemented by using existing rate limiting libraries or middleware compatible with that web server framework. 5. **Database Interaction**: The file does interact with a database to check for existence and create tables. If rate limiting is to be implemented, it might require additional tables or fields to track request counts and timestamps, but that would be part of the database schema and not directly related to this file. 6. **Environment Variables**: The file uses environment variables to configure certain aspects of the application. If rate limiting settings need to be configurable, they could potentially be set through environment variables as well. In conclusion, `pilot/main.py` does not contain any direct implementation of web server functionality or API endpoints where rate limiting middleware could be applied. To proceed with the task, you would need to identify the part of the application that handles HTTP requests and define the rate limiting logic there, possibly in a separate middleware file or within the web framework's existing middleware system. If this application is not a web server and does not serve HTTP requests, then the task of implementing rate limiting may not be applicable to this file.

Changes on pilot/utils/middleware.py

As the `pilot/utils/middleware.py` file is a new file and currently contains only the comment `// This is a new file`, there is no existing code to analyze for bugs or issues related to rate limiting. To address the issue opened by the user, the task for this file is to create a rate limiting middleware function. Since there is no existing code, I will provide a brief outline of what the middleware function could look like in Python, using a common web framework like Flask as an example. However, please note that the actual implementation may vary depending on the specific web framework and requirements of the application. ```python from flask import request, Response from functools import wraps import time # A simple in-memory rate limiter for demonstration purposes # In production, you might use a more robust solution like Redis RATE_LIMITS = { "user": {"calls": 100, "period": 3600} # 100 calls per hour per user } # In-memory storage for tracking requests request_counters = {} def rate_limit(route_function): @wraps(route_function) def wrapper(*args, **kwargs): # Identify the user (this is just a placeholder, actual implementation may vary) user_id = request.headers.get("X-User-ID") # Get the current timestamp current_time = time.time() # Initialize or update the request count and timestamp if user_id not in request_counters or current_time - request_counters[user_id]['timestamp'] > RATE_LIMITS["user"]["period"]: request_counters[user_id] = {"count": 1, "timestamp": current_time} else: request_counters[user_id]["count"] += 1 # Check if the request count exceeds the rate limit if request_counters[user_id]["count"] > RATE_LIMITS["user"]["calls"]: # If the rate limit is exceeded, return a 429 Too Many Requests response return Response("Too many requests", status=429) # If the rate limit is not exceeded, call the actual route function return route_function(*args, **kwargs) return wrapper # Example usage with a Flask route # @app.route('/some_endpoint') # @rate_limit # def some_endpoint(): # return "This endpoint is rate-limited." ``` This is a basic example of how a rate limiting middleware could be implemented. It uses an in-memory dictionary to track the number of requests made by each user within a specified time period. If the number of requests exceeds the limit, a `429 Too Many Requests` response is returned. For a production environment, you would likely need a more sophisticated solution that can handle distributed systems and persistence, such as using Redis or another database to store request counts. Since the file is new and there is no other code to review, this concludes the analysis and report for `pilot/utils/middleware.py`. The next steps would be to implement the middleware function as outlined above, integrate it with the web application, and test it to ensure it properly limits the rate of requests to the endpoints.

Changes on pilot/utils/llm_connection.py

Upon reviewing the `pilot/utils/llm_connection.py` file, the following observations and recommendations can be made regarding the implementation of rate limiting: 1. **Rate Limiting Logic**: The function `rate_limit_exceeded_sleep` is present in the file, which suggests that there is some handling of rate limiting errors. This function is called when a rate limit error is caught, and it extracts the wait time from the error message and sleeps for the specified duration plus an extra buffer time. However, this is reactive and does not proactively prevent rate limiting errors. 2. **Retry Decorator**: The `retry_on_exception` decorator is used to wrap functions that make API calls. It includes logic to handle exceptions, including rate limit exceeded errors. However, this decorator does not implement rate limiting; it merely handles the situation after the fact by waiting and retrying. 3. **No Middleware Integration**: There is no middleware integration in this file for rate limiting. Middleware would be a more centralized and proactive approach to rate limiting, as it could intercept requests before they reach the API call functions and enforce rate limits. 4. **No Endpoint-Specific Rate Limiting**: The file does not contain any logic to apply rate limiting on a per-endpoint basis. This would be necessary if different endpoints have different rate limits or if you want to enforce custom rate limits for certain endpoints. 5. **No Rate Limit Tracking**: There is no mechanism in place to track the number of requests made to the API within a certain time frame, which would be necessary for implementing rate limiting. Rate limiting typically requires keeping track of the number of requests over time and limiting requests once a certain threshold is reached. 6. **No User-Specific Rate Limiting**: The file does not contain any logic for user-specific rate limiting. In a multi-user system, it might be necessary to track and limit requests on a per-user basis to prevent abuse. 7. **Potential Place for Rate Limiting**: The `stream_gpt_completion` function is where the actual API request is made. This would be a logical place to integrate proactive rate limiting logic to prevent sending too many requests in a short period. **Recommendations**: - Implement a rate limiting mechanism that proactively limits the number of requests sent to the API within a given time frame. This could be done by integrating a rate limiting library or middleware. - Consider user-specific and endpoint-specific rate limiting if the application's use case requires it. - Track the number of requests made and enforce limits before making API calls, rather than only handling rate limit errors after they occur. - If middleware is used, ensure that it is properly integrated into the application's request handling flow. In summary, while the file contains some handling of rate limit errors, it lacks a proactive rate limiting implementation. To address the code scanning alert for missing rate limiting, a middleware or similar mechanism should be integrated to enforce rate limits before the API call functions are reached.

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