Social Engineering Toolkit

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Social Engineering Toolkit (SET) is a free and open-source Toolkit. It is used for Social Engineering attacks, e.g.phishing. This menu driven Toolkit allows you with several submenus to control your attacks tailored to the desired target. It is pre-installed in Kali Linux.

Run Social Engineering Toolkit with "setoolkit" and go through our example to get a first overview of this Toolkit.


As part of this guide, I used Kali (Kali GNU/Linux Rolling 5.10.0-kali3-amd64) as the OS, so it was already preinstalled. I installed Kali on a Virtual machine (VMware® Workstation 15 Pro 15.5.5 build-16285975).


Let's see an example of how to execute a "Twitter Sign in Phishing Web-Attack" using the Social Engineering Toolkit. For this Phishing Attack we need to go through following submenus as shown below.


1st select "Social-Engineering Attacks"

After launching the Social Engineering Toolkit we see the above mentioned menu. Here we can choose between submenus to specify our attack. In our case for "Twitter Sign in Phishing Web-Attack" we have to select "Social-Engineering Attacks".

submenu 2

2nd select "Website Attack Vectors"

For "Twitter Sign in Phishing Web-Attack" we've to choose option 2

submenu 3

3rd select "Credential Harvester Attack Method"

To specify our Attack as a 'Credential Harvester Attack' we've to choose the option 3.

submenu 4

4th select "Web Templates"

To allow Social Engineering Toolkit to import a list of pre-defined web applications that it can utilize within the attack we've to select option 1

Twitter Phishing

5th Enter the IP address for the POST back in Harvester/Tabnabbing and select finally Twitter Template

For practicing purposes I used the localhost (, of course in a real attack you would use a corresponding ip address. And then you've to select the Twitter Template (option 3). After the Template is selected, the choosen website is being cloned. With the next action a website will appear which is similar to the Twitter Sign in website, the victim is prompt to Sign in.


Result: Credentials entered from victim are visible for Attacker

Victims user credentials are visible. At the same time the victims browser is redirected to the original Twitter Sign in website.


  • WFP-1