Improper resource allocation
Need
Efficient resource allocation and management
Context
- Usage of Go 1.16 for developing scalable and efficient applications
- Usage of gin-gonic/gin for building web applications in Go
- Usage of net/http for creating HTTP servers in Node.js
- Usage of time for time-related operations and calculations
Description
Non compliant code
package main
import (
"github.com/gin-gonic/gin"
"net/http"
"time"
)
func main() {
r := gin.Default()
r.GET("/longprocess", func(c *gin.Context) {
time.Sleep(10 * time.Second) // Simulating a long process
c.String(http.StatusOK, "Done")
})
r.Run() // listen and serve on 0.0.0.0:8080 (for windows "localhost:8080")
}
This is a simple Go application that uses the Gin framework to set up a web server. The server has only one route /longprocess, which simulates a long process by sleeping for 10 seconds.
The vulnerability here is improper resource allocation. Specifically, the problem is that the server can be easily overwhelmed by multiple simultaneous requests to the /longprocess endpoint. Each request to this endpoint causes the server to sleep for 10 seconds, which means that the server is allocating resources (i.e., a worker thread and associated memory) to handle the request for this entire duration. If an attacker were to flood the server with requests to this endpoint, it could consume all available resources and cause the server to become unresponsive or crash.
This problem is exacerbated by the fact that the server does not limit the number of simultaneous requests it will handle. As a result, an attacker can easily perform a Denial of Service (DoS) attack by sending a large number of simultaneous requests to the /longprocess endpoint.
Furthermore, the server does not implement any form of rate limiting or request throttling. This means that an attacker can send as many requests as they want as quickly as they want, further increasing the potential for resource exhaustion.
In summary, this code is vulnerable to a DoS attack due to its improper allocation of resources when handling requests to the /longprocess endpoint. This vulnerability could be mitigated by implementing rate limiting, request throttling, or by optimizing the long process to reduce the amount of resources it consumes.
Steps
- Identify the resource allocation issue in the code.
- Analyze the code to determine the root cause of the inefficient resource allocation.
- Optimize the code by implementing efficient algorithms or programming practices.
- Consider using asynchronous processing or parallelization techniques to improve resource allocation.
- Implement proper input validation and restrictions to prevent excessive resource consumption by external sources.
- Test the application thoroughly to ensure that the resource allocation issue has been resolved.
- Monitor the application in production to identify any potential resource allocation issues and address them promptly.
Compliant code
package main
import (
"github.com/gin-gonic/gin"
"net/http"
"time"
)
func main() {
r := gin.Default()
r.GET("/longprocess", func(c *gin.Context) {
go func() {
time.Sleep(10 * time.Second) // Simulating a long process
c.String(http.StatusOK, "Done")
}()
})
r.Run() // listen and serve on 0.0.0.0:8080 (for windows "localhost:8080")
}
The original code was vulnerable due to improper resource allocation. The server was forced to wait for 10 seconds due to the time.Sleep(10 * time.Second) line, which simulates a long process. This could lead to a Denial of Service (DoS) attack if multiple requests are sent to the /longprocess endpoint, as the server would be forced to wait for each request to finish before processing the next one.
The fixed code mitigates this vulnerability by using a goroutine, which is a lightweight thread managed by the Go runtime. The go keyword before the function call causes the function to be executed in a new goroutine. This means that the server can continue processing other requests while the long process is running, thus improving resource allocation.
However, it's important to note that this fix might not be suitable for all use cases. For example, if the long process involves writing to a database or other shared resource, running it in a separate goroutine could lead to race conditions. In such cases, other solutions such as using a queue or a worker pool might be more appropriate.
Additionally, the fixed code does not implement any input validation or restrictions to prevent excessive resource consumption by external sources. Depending on the nature of the long process and the application's requirements, it might be necessary to implement such measures to further improve resource allocation and prevent DoS attacks.