Addressing User Data Exposure And Performance On Leaderboard

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In this article, we address critical issues related to user data exposure and performance bottlenecks within the leaderboard feature of our application. This came to light when calling the endpoint GET https://heartbound-d00f1b55e2ad.herokuapp.com/api/users/leaderboard, revealing that the response includes comprehensive user information, posing significant privacy and performance concerns.

Problem Description

The current implementation of the leaderboard endpoint returns an extensive amount of user data, including sensitive details like IDs, usernames, avatars, display names, pronouns, about sections, banner colors, banner URLs, roles, credits, levels, experience points, and various usage statistics. This excessive data exposure is a major privacy vulnerability, as it allows unauthorized access to user information that should not be publicly available. The full response structure is as follows:

[
    {
        "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 etc
   }
]

Performance Impact

Performance is significantly impacted because the endpoint returns data for all registered users (over 1000 in this case), even though the leaderboard only displays the top 100. This results in a substantial payload size (the response is 59000 lines long), leading to slow loading times (approximately 2 seconds). Such delays negatively affect the user experience and can lead to frustration.

Security Vulnerability

The most critical issue is the exposure of sensitive user data. By retrieving all user information, the application creates a significant security vulnerability. Malicious actors could exploit this vulnerability to gather user data, potentially leading to privacy breaches, identity theft, and other security incidents. The comprehensive nature of the data returned—including details like usernames, avatars, and usage statistics—presents a high risk.

Proposed Solutions

To address these issues, we propose the following solutions, focusing on both data security and performance optimization:

1. Implement a Leaderboard-Specific Data Transfer Object (DTO)

To mitigate the risk of exposing sensitive user information, it is crucial to create a dedicated Data Transfer Object (DTO) specifically for the leaderboard. A DTO is a design pattern used to transfer data between subsystems of an application. In this context, a LeaderboardDTO should be created to encapsulate only the data necessary for displaying the leaderboard. This includes:

  • User ID: A unique identifier for each user, necessary for linking leaderboard entries to user profiles.
  • Username: The user's display name on the leaderboard.
  • Experience Points: The user's current experience points, a key metric for ranking on the leaderboard.
  • Credits: The number of credits a user has, which can be another metric for ranking.
  • Voice Time: The amount of time a user has spent in voice channels, often used as a measure of engagement.
  • Level: The user's current level in the application.

By creating a LeaderboardDTO, the endpoint will only return these essential fields, preventing the exposure of sensitive information such as email addresses, personal descriptions, and other private data. This approach significantly reduces the risk of data breaches and enhances user privacy.

2. Limit the Number of Returned Users

Currently, the leaderboard endpoint retrieves data for all registered users, which is highly inefficient and unnecessary since the leaderboard only displays a limited number of top users (e.g., 100). To improve performance, the endpoint should be modified to return only the top 100 users. This can be achieved by implementing a limit on the number of results returned by the database query. Limiting the result set will significantly reduce the data payload, leading to faster response times and improved application performance.

Backend Filtering Implementation

It is imperative to implement this filtering on the backend rather than the frontend. Frontend filtering is less secure because users can bypass the filter by manipulating the URL or making direct API calls without the filter applied. For example, a user could remove the ?limit=100 parameter from the URL to retrieve data for all users. By implementing the limit on the backend, the application ensures that only the necessary data is fetched from the database and sent to the client, regardless of any client-side manipulations. This approach provides a robust and secure solution to the performance and security issues.

3. Implement Backend Filtering and Pagination

To further optimize performance and security, it is recommended to implement backend filtering and pagination. Backend filtering ensures that only the necessary data is retrieved from the database based on the query parameters. Pagination, on the other hand, breaks the data into smaller, more manageable chunks. This approach is particularly useful when dealing with large datasets.

Backend Filtering

As mentioned earlier, backend filtering involves applying filters directly to the database query. In the context of the leaderboard, this means filtering the users based on the desired criteria (e.g., top 100 users by experience points). This can be achieved using SQL queries or ORM (Object-Relational Mapping) tools that allow specifying conditions and limits on the data retrieval. For example, in SQL, the query might look like this:

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

This query retrieves only the top 100 users based on their experience points, ensuring that the application does not fetch unnecessary data.

Pagination

Pagination involves dividing the dataset into pages and allowing users to navigate through these pages. This is useful when displaying a large number of records, such as in a leaderboard with thousands of users. Pagination can be implemented by adding parameters to the API endpoint that specify the page number and the number of records per page. For example:

  • GET /api/users/leaderboard?page=1&limit=100 (returns the first 100 users)
  • GET /api/users/leaderboard?page=2&limit=100 (returns the next 100 users)

By implementing pagination, the application can efficiently handle large datasets and provide a smooth user experience, even when dealing with thousands of users.

Secure Coding Practices

In addition to the above solutions, it is essential to follow secure coding practices to prevent future vulnerabilities. Some best practices include:

  1. Input Validation: Always validate user inputs to prevent injection attacks and ensure data integrity.
  2. Output Encoding: Encode data before displaying it to prevent cross-site scripting (XSS) attacks.
  3. Least Privilege Principle: Grant only the necessary permissions to users and applications.
  4. Regular Security Audits: Conduct regular security audits to identify and address potential vulnerabilities.
  5. Stay Updated: Keep all software and libraries up-to-date with the latest security patches.

Implementation Steps

To implement the proposed solutions, follow these steps:

  1. Create a LeaderboardDTO: Define a new DTO that includes only the necessary fields for the leaderboard (user ID, username, experience points, credits, voice time, and level).
  2. Modify the Endpoint: Update the leaderboard endpoint to return LeaderboardDTO objects instead of full user objects.
  3. Implement Backend Filtering: Modify the database query to retrieve only the top 100 users.
  4. Implement Pagination (Optional): Add pagination support to the endpoint to handle large datasets efficiently.
  5. Test Thoroughly: Test the changes thoroughly to ensure that the leaderboard functions correctly and that no sensitive data is exposed.
  6. Deploy Changes: Deploy the changes to the production environment.

Conclusion

Addressing user data exposure and performance issues on the leaderboard is critical for maintaining user privacy and providing a smooth user experience. By implementing a LeaderboardDTO, limiting the number of returned users, and applying backend filtering and pagination, we can significantly enhance the security and performance of the application. Following secure coding practices and conducting regular security audits will help prevent future vulnerabilities and ensure the long-term security and reliability of the system.