Addressing User Data Exposure And Performance Issues In Leaderboards

Jul 10, 2025 by Jeany 69 views

Addressing User Data Exposure and Performance Issues on the Leaderboard

This article addresses critical issues concerning user data exposure and performance bottlenecks within the leaderboard functionality of the Heartbound application. The primary concerns revolve around the excessive data being transmitted via the /api/users/leaderboard endpoint, leading to both security vulnerabilities and significant performance degradation. This comprehensive analysis will delve into the specifics of the problem, propose concrete solutions, and underscore the importance of implementing backend filtering and Data Transfer Objects (DTOs) to safeguard user information and enhance application efficiency.

The Problem: Excessive Data Exposure and Slow Loading Times

When the endpoint GET https://heartbound-d00f1b55e2ad.herokuapp.com/api/users/leaderboard is called, it returns a comprehensive dataset containing the full information of every user. This includes sensitive details such as user IDs, usernames, avatars, display names, pronouns, "about" sections, banner colors, banner URLs, roles, credits, levels, experience points, message counts, voice call statistics, equipped items, and daily streaks. The response structure resembles the following:

[
 {
 "id": "316657253065293824",
 "username": ".sevlar",
 "avatar": "https://cdn.discordapp.com/attachments/1390851076072411241/1391747512087609364/B48D1F45-A555-42C1-A83F-2A40B0963D4A.png?ex=686d0560&is=686bb3e0&hm=46b4ecb982937c916beabfca920acfdc33461c594074571e538eeac7e41ce463&",
 "displayName": "",
 "pronouns": "",
 "about": "‎",
 "bannerColor": "#ED1C27",
 "bannerUrl": "https://cdn.discordapp.com/attachments/1390851076072411241/1391748004079210576/iu.png?ex=686d05d5&is=686bb455&hm=089cb6d3a49f451eaa57388e1d7b03931244d3026d9192ec2d5a9c637999a5b1&",
 "roles": [
 "USER"
 ],
 "credits": 42765,
 "level": 10,
 "experience": 925,
 "xpForNextLevel": 1100,
 "messageCount": 288,
 "messagesToday": 0,
 "messagesThisWeek": 27,
 "messagesThisTwoWeeks": 288,
 "voiceRank": 19,
 "voiceTimeMinutesToday": 233,
 "voiceTimeMinutesThisWeek": 233,
 "voiceTimeMinutesThisTwoWeeks": 1363,
 "voiceTimeMinutesTotal": 1363,
 "equippedUserColorId": "505666a6-30d3-44d9-854e-cb81103407a6",
 "equippedListingId": null,
 "equippedAccentId": null,
 "equippedBadgeId": "b32f538b-4725-4e8e-8fad-58dc3177579d",
 "badgeUrl": "https://res.cloudinary.com/drywja3ta/image/upload/v1751507062/bjf3tijcnbweoensvih1.png",
 "badgeName": "vesper",
 "nameplateColor": "#F8BBD0",
 "dailyStreak": 3,
 "lastDailyClaim": "2025-07-07T10:48:01.26463"
 },
 {
 // ... all other 1000+ users entries ...
 }
]

This extensive data payload has significant implications for both data security and application performance.

Data Exposure

Exposing such a wealth of user information in a single API response creates a considerable security risk. Malicious actors could exploit this vulnerability to harvest user data, potentially leading to identity theft, phishing attacks, or other harmful activities. The inclusion of non-essential information for the leaderboard, such as personal descriptions and detailed activity metrics, unnecessarily broadens the scope of potential data breaches. Thus, it is critical to limit the amount of personal data exposed to only necessary information.

Performance Bottleneck

The sheer size of the response payload—containing data for every registered user—severely impacts the leaderboard's loading time. The original report indicates that it takes approximately 2 seconds to load the leaderboard, which is an unacceptable delay in user experience terms. When a leaderboard API returns thousands of user entries, the server's processing load increases significantly, consuming more resources and leading to slower response times. Furthermore, transmitting this large payload over the network consumes considerable bandwidth, adding to the performance overhead. This excessive data transfer can also strain the client-side application, as it needs to process and render a massive amount of information, further contributing to the sluggish performance.

Proposed Solutions: A Two-Pronged Approach

To effectively address these issues, a two-pronged solution is recommended:

Implement a Data Transfer Object (DTO): Create a dedicated DTO tailored specifically for leaderboard data. This DTO should include only the essential fields required for the leaderboard display, such as user ID, username, experience points, credits, voice time, and level. By excluding extraneous data, the size of the API response can be significantly reduced, thereby improving both security and performance. This approach aligns with the principle of least privilege, ensuring that only necessary data is exposed.
Implement Backend Filtering and Pagination: Modify the backend logic to retrieve only the top N users for the leaderboard (e.g., the top 100) and implement pagination. This will prevent the server from processing and transmitting data for all users, dramatically reducing the response payload size and improving loading times. This approach reduces the processing load on the server, minimizes network bandwidth consumption, and enhances the responsiveness of the leaderboard feature.

Solution 1: Creating a Leaderboard DTO

To address the issue of excessive data exposure, the creation of a specialized Data Transfer Object (DTO) is paramount. A DTO acts as a container for data that is transmitted between different layers of an application, in this case, between the backend server and the frontend client. By defining a DTO specifically for the leaderboard, we can carefully curate the data that is exposed, ensuring that only essential information is included. This minimizes the risk of exposing sensitive user data and reduces the overall size of the data payload.

Implementing a DTO involves creating a new class or structure that mirrors the specific data requirements of the leaderboard. For the Heartbound application, this Leaderboard DTO should include the following fields:

User ID: A unique identifier for each user.
Username: The user's display name on the platform.
Experience Points: The user's current experience points, which contribute to their leaderboard ranking.
Credits: The number of credits the user has accumulated.
Voice Time: The total time the user has spent in voice channels, often used as a metric for engagement.
Level: The user's current level within the platform's progression system.

By explicitly defining these fields in the DTO, all other user data—such as personal descriptions, contact information, and detailed activity logs—are excluded from the API response. This significantly reduces the potential attack surface and protects user privacy. Furthermore, the smaller payload size translates directly into faster response times, enhancing the user experience.

The implementation of the DTO should occur within the backend service that handles the /api/users/leaderboard endpoint. Instead of returning the full user object, the service should map the relevant fields from the user object to the Leaderboard DTO before sending the response. This ensures that the client only receives the data it needs for the leaderboard display, nothing more.

Solution 2: Backend Filtering and Pagination for Efficient Data Retrieval

The second part of the solution focuses on improving performance by limiting the amount of data retrieved from the database and transmitted over the network. The key strategies here are backend filtering and pagination. Backend filtering involves querying the database to retrieve only the data that is necessary for the leaderboard display. In the context of the Heartbound application, this means retrieving only the top N users based on their leaderboard ranking (e.g., the top 100 users). Pagination, on the other hand, involves dividing the result set into smaller, more manageable chunks, allowing the client to request data in increments rather than receiving the entire dataset at once.

The current implementation of the /api/users/leaderboard endpoint retrieves data for all registered users, regardless of their leaderboard ranking. This is highly inefficient, as the majority of this data is not needed for the leaderboard display, which typically shows only the top users. By implementing backend filtering, we can significantly reduce the amount of data retrieved from the database, thereby reducing the processing load on the server and improving response times.

To implement backend filtering, the database query should be modified to include a LIMIT clause that restricts the result set to the top N users. The exact method for achieving this will depend on the database system used by the Heartbound application, but the general principle remains the same. For example, in SQL, the query might look something like this:

SELECT user_id, username, experience_points, credits, voice_time, level
FROM users
ORDER BY experience_points DESC
LIMIT 100;

This query retrieves the user ID, username, experience points, credits, voice time, and level for the top 100 users, ordered by experience points in descending order. By executing this query on the backend, the server only needs to process and transmit data for 100 users, rather than thousands, resulting in a significant performance improvement.

Paginating the results further enhances performance by allowing the client to request data in smaller chunks. This is particularly useful if the leaderboard needs to display more than just the top 100 users, or if the total number of users is very large. Pagination involves adding parameters to the API request that specify the page number and page size. For example, a request to /api/users/leaderboard?page=1&limit=50 might return the first 50 users, while a request to /api/users/leaderboard?page=2&limit=50 might return the next 50 users. This allows the client to retrieve data in a more controlled and efficient manner, reducing the initial load time and improving the overall user experience.

Backend filtering and pagination work synergistically to optimize data retrieval and transmission. By limiting the amount of data retrieved from the database and allowing the client to request data in smaller chunks, these techniques significantly reduce the load on the server, minimize network bandwidth consumption, and improve the responsiveness of the leaderboard feature. This contributes to a more scalable and user-friendly application.

Security Implications of Not Filtering on the Backend

Filtering on the backend, rather than the frontend, is a critical security practice. While it might seem convenient to use query parameters like ?limit=100 to restrict the number of users returned, this approach is inherently insecure. If the filtering is done solely on the frontend, a malicious user could simply remove the ?limit parameter from the URL to bypass the restriction and access the full dataset. This exposes all user information, negating any attempt to protect sensitive data.

Backend filtering ensures that the server only retrieves and transmits the data that is explicitly requested and authorized. This prevents unauthorized access to sensitive information and mitigates the risk of data breaches. By enforcing filtering on the backend, the application maintains a secure and controlled data flow, protecting user privacy and data integrity. The principle of least privilege dictates that systems should only grant the minimum level of access necessary to perform a given function. In this context, the server should only retrieve the data required to populate the leaderboard, and no more. Backend filtering enforces this principle, ensuring that sensitive data is not unnecessarily exposed.

Conclusion: Enhancing Security and Performance

Addressing the user data exposure and performance issues on the Heartbound leaderboard is crucial for maintaining both the security and usability of the application. By implementing a Leaderboard DTO and enforcing backend filtering and pagination, the application can significantly reduce the risk of data breaches and improve loading times. These changes not only enhance the user experience but also demonstrate a commitment to data security and privacy.

The implementation of these solutions requires a concerted effort from both backend and frontend developers. The backend team must create the DTO, modify the API endpoint to use the DTO, and implement backend filtering and pagination. The frontend team may need to adjust the leaderboard display to accommodate the paginated data. However, the benefits of these changes far outweigh the effort required. A secure and performant leaderboard fosters trust among users and contributes to the overall success of the application. Investing in these improvements is an investment in the long-term health and viability of the Heartbound platform.

In summary, the proposed solutions offer a comprehensive approach to resolving the identified issues. By limiting the amount of data exposed and optimizing data retrieval, the Heartbound application can provide a more secure, efficient, and user-friendly experience for its users.