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Database

Improper control of interaction frequency

Need

Enforcement of rate limiting to control user interaction frequency, including limits on password change requests

Context

• Usage of Go 1.16 for developing high-performance and efficient applications

• Usage of gin-gonic/gin for building web applications and APIs in Go

• Usage of net/http for handling HTTP requests and responses in a Node.js application

Description

1. Non compliant code

// --- Rate limiting ---
package main

import "github.com/gin-gonic/gin"

func main() {
    r := gin.Default()
    ...

**Rate limiting:** The above code represents a simple backend server using the Gin framework in Go. It has a POST endpoint `/post` where users can send their requests. The vulnerability in this code is that there is no control over the frequency of interactions. That is, a user can send as many requests as they want in a short period of time. This can lead to a Denial of Service (DoS) attack where the server gets overwhelmed with too many requests, causing it to slow down or even crash, thus denying service to legitimate users. This lack of rate limiting can also allow an attacker to brute force their way into the system. For example, if there is a login functionality, an attacker can try as many username/password combinations as they want until they find the right one. In the context of this code, the vulnerability is in the `/post` endpoint where the function handling the requests does not implement any form of rate limiting. **Password change limit:** In the above code, we have a simple API server running using the Gin framework. It exposes an endpoint `/change_password` that accepts POST requests to change a user's password. The `changePassword` function is called whenever a POST request is made to the `/change_password` endpoint. It changes the password for the given username and increments the `passwordChangeCount` for the user. However, there is no limit on how many times a password can be changed in a day. This is a security vulnerability as it allows an attacker to brute force the password change functionality. The `passwordChangeCount` map stores the number of times a user has changed their password but it is never used to limit the number of password changes. This means that an attacker could potentially make unlimited password change requests, which is a serious security risk.

2. Steps

• Implement rate limiting to restrict the number of requests a user can make within a specific time frame.

• Use a middleware to intercept incoming requests and check if the user has exceeded the allowed limit.

• Track the number of requests made by each user and the timestamp of the last request.

• If the user has exceeded the limit, return an appropriate error response, such as HTTP 429 Too Many Requests.

• Choose an appropriate rate limit strategy, such as a fixed number of requests per minute or a sliding window algorithm.

• Consider using a distributed rate limiting solution if your application is deployed across multiple servers.

• Test the rate limiting implementation to ensure it is working as expected and effectively mitigating the vulnerability.

• Implement a password change limit policy to restrict the number of password change requests that can be made in a day.

• Track the number of password change requests made by each user.

• Before allowing a password change request, check if the user has exceeded the password change limit.

• If the user has exceeded the limit, reject the password change request and notify the user.

• Store the password change count for each user in a secure and persistent manner.

• Consider implementing additional security measures such as rate limiting and account lockout to further protect against password abuse.

3. Secure code example

// --- Rate limiting ---
package main

import (
	"github.com/gin-gonic/gin"
	"github.com/didip/tollbooth"
	"github.com/didip/tollbooth_gin"
	"time"...

**Rate limiting:** The above code implements rate limiting in a Go backend application using the Gin framework and the Tollbooth library. The `tollbooth.NewLimiter(1, nil)` function creates a new limiter struct that limits the number of requests to 1 per second. The `SetIPLookups` method is used to specify the headers that should be used to identify the client's IP address. The `SetMessage` and `SetMessageContentType` methods are used to set the error message and content type that will be returned if a client exceeds the rate limit. The `SetTokenBucketExpirationTTL` method is used to set the expiration time for the token bucket, which is used to track the number of requests made by each client. The `tollbooth_gin.LimitHandler(limiter)` function is used as a middleware in the route handler for the POST endpoint. This middleware intercepts incoming requests and checks if the client has exceeded the rate limit. If the limit has been exceeded, it returns an HTTP 429 Too Many Requests response with the error message specified earlier. This implementation effectively mitigates the vulnerability by preventing a client from making too many requests in a short period of time, which could potentially lead to a denial of service (DoS) attack. **Password change limit:** The updated code now includes a password change limit policy. This policy restricts the number of password change requests that can be made in a day by each user. The `passwordChangeCount` map tracks the number of password change requests made by each user. The `passwordChangeDate` map stores the date of the last password change request made by each user. Before allowing a password change request, the code checks if the user has exceeded the password change limit for the current day. This is done by comparing the value in `passwordChangeCount` for the user with the `passwordChangeLimit` constant and checking if the date in `passwordChangeDate` for the user is the current day. If the user has exceeded the limit, the password change request is rejected, and the user is notified with a "Password change limit exceeded" message. The `changePassword` function updates the `passwordChangeCount` and `passwordChangeDate` maps each time a password is successfully changed. This approach ensures that the password change count for each user is stored in a secure and persistent manner. It also provides additional security measures to protect against password abuse.