Need

Implementation of robust security controls for facial recognition

Context

• Usage of Go 1.16 for building high-performance and scalable applications
• Usage of gin-gonic/gin for building web applications in Go

Description

Non compliant code

        package main

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

type User struct {
	Token string `json:"token"`
}

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

	r.POST("/auth", func(c *gin.Context) {
		var user User
		if err := c.ShouldBindJSON(&user); err != nil {
			c.JSON(400, gin.H{"error": err.Error()})
			return
		}

		// Bypassing facial recognition by accepting token only
		if user.Token == "F015" {
			c.JSON(200, gin.H{"message": "Authenticated"})
		} else {
			c.JSON(401, gin.H{"message": "Authentication failed"})
		}
	})

	r.Run() // listen and serve on 0.0.0.0:8080
}
        
        

The above code is a simple Go application using the Gin framework. It defines a single POST endpoint /auth for user authentication.

The vulnerability lies in the authentication logic in the /auth endpoint. It accepts a JSON payload with a token field and checks if the token is equal to "F015". If the token matches, it bypasses any other authentication methods, such as facial recognition, and returns a 200 status code with a message "Authenticated". Otherwise, it returns a 401 status code with a message "Authentication failed".

This is a security vulnerability as it allows an attacker to bypass facial recognition by simply providing the "F015" token. The attacker doesn't need to provide any other authentication information, such as a valid face image, to authenticate as a user. This could potentially allow unauthorized access to the application and its data.

Steps

• Implement a proper facial recognition process to authenticate users instead of relying solely on a token.
• Integrate a facial recognition library or service that can accurately verify the identity of the user.
• Capture and analyze the user's facial features to ensure a match with the stored data.
• Implement additional security measures such as liveness detection to prevent spoofing attacks.
• Consider using multi-factor authentication in combination with facial recognition for stronger security.
• Regularly update and maintain the facial recognition system to address any vulnerabilities or weaknesses.

Compliant code

        package main

import (
	"github.com/gin-gonic/gin"
	"github.com/yourfacialrecognitionlibrary"
)

type User struct {
	Token string `json:"token"`
	FaceID string `json:"faceID"`
}

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

	r.POST("/auth", func(c *gin.Context) {
		var user User
		if err := c.ShouldBindJSON(&user); err != nil {
			c.JSON(400, gin.H{"error": err.Error()})
			return
		}

		// Authenticate using both token and facial recognition
		if user.Token == "F015" && yourfacialrecognitionlibrary.Authenticate(user.FaceID) {
			c.JSON(200, gin.H{"message": "Authenticated"})
		} else {
			c.JSON(401, gin.H{"message": "Authentication failed"})
		}
	})

	r.Run() // listen and serve on 0.0.0.0:8080
}
        
        

The updated code now includes a facial recognition process for user authentication. Instead of solely relying on a token, the application now also requires a FaceID, which is a unique identifier associated with the user's facial data.

The yourfacialrecognitionlibrary.Authenticate(user.FaceID) function is a placeholder for the actual facial recognition authentication process. This function should capture and analyze the user's facial features to ensure a match with the stored data.

This function should also implement additional security measures such as liveness detection to prevent spoofing attacks. Liveness detection is a feature that can detect whether the source of a biometric sample is a live human being or a fake representation.

For stronger security, consider using multi-factor authentication in combination with facial recognition. This could involve something like a password, a token, and facial recognition.

Regularly update and maintain the facial recognition system to address any vulnerabilities or weaknesses. This includes updating the facial recognition library or service and ensuring that the facial data stored in the application is secure.

References

• 210. Security controls bypass or absence - Facial Recognition