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AUTOMATED SOCIAL ENGINEERING
FOR THE
ANTISOCIAL ENGINEER
PATRICK SAYLER
2
ABOUT
Patrick Sayler
 Web Application and Network Pentester at NetSPI
 Social Engineering
 Phone
 Onsite
 Twitter: @psayler
 Blog: blog.netspi.com
3
PRESENTATION OVERVIEW
 Background on Phone SE
 Current problems
 Solutions
 Service overview
 Environment
 Attack Scenarios
 Inbound
 Outbound
 Demo
 Addl. Resources and Research
4
INTRODUCTION
Background on Phone SE
 Typical engagement
1. Setup the phone
2. Mentally prepare
3. Make the call
4. Tell the target “do bad thing”
5. Hang up, breath a sigh of relief
6. Repeat 2-6
Easy, right?
5
INTRODUCTION
UGLY
Effective
Fun
Unique
GOOD
Time / Effort
Stressful
BAD
6
INTRODUCTION
How can I…
avoid Asterisk
avoid talking to someone
make this better?
7
INTRODUCTION
 Voice Clips
 Record my own voice and play back the audio over the phone
− Short lived. Too much work.
 Text-to-Speech (TTS)
− Found a website with an obviously robotic but legitimate sounding voice
− Recorded 4 Phrases:
“You have…1…new message”
“Please say your username”
“Please say your password”
“First message:”
− It worked!
 [REDACTED]
− Entry point into an environment. Got credentials, got DA
8
INTRODUCTION
Okay. Now what?
 Fun Experiment
− Less structured engagements, more freeform
 Still some hurdles
− Annoying to setup
− Didn’t scale well
− Multiple users? Awkward to put together.
− Too many people editing Asterisk extensions and sip.conf
9
INTRODUCTION
We need:
 Easy
− Setup
− Maintenance
 Scalable
− Multiple users
− Multiple calls
 Centralized
− Recordings
− Tracking and statistics
Sounds familiar…
1010
SOLUTION
AMAZON CONNECT
11
SOLUTION – AMAZON CONNECT
Full Featured Call Center Service
 Easy
− Setup - Point and Click GUI
− Maintenance - Managed by Amazon
 Scalable
− Multiple users
− Multiple calls – Inbound & Outbound
 Centralized
− Recordings – S3 Bucket
− Tracking and statistics
12
SOLUTION
What can you do?
 Inbound & outbound phone calls
 Audio recording
 Call routing/triaging
 Customizable prompts and triggers
 Cheap!
 Integration with AWS ecosystem
13
SOLUTION
Integration – Amazon Transcribe
 Speech recognition
 Convert voice to text
 Run against the recordings in your S3 bucket
− Easier to review post-engagement
Integration – AWS Lambda
 Run code
 Process information received from recordings
− Flag on specific keywords
− “Password”
 Literally anything you can write, it can do
Integration – Amazon Lex
 “Conversation Bot”
14
SOLUTION
15
SOLUTION – AMAZON CONNECT
16
SOLUTION
17
SOLUTION
18
SOLUTION
Integration
 With the previous tools alone
− Reacting to the situation
− Cannot change what has already happened
− Might be too late to use information
 Lex + Lambda
− Proactive
− Actual interaction with the target
− Can share information with you while it happens
1919
ATTACK SCENARIOS
INBOUND PHONE CALLS
20
ATTACK SCENARIOS – SMS PHISHING
SMS Phishing
 Phishing, but over text message instead of email
 Same concepts and methodology apply
− Mass delivery
− Broad reach
 AWS SNS to send the text message
 Victim calls associated number
− Prompted to provide credentials
 Lex recognizes the data and transcribes it for Lambda
 Lambda takes the creds and sends them
− Notify the tester
21
ATTACK SCENARIOS – SMS PHISHING
[~] aws sns publish –message
"Your corporate account has been disabled due to malicious activity.
Please contact +1-XXX-XXX-2315 to reactivate your service."
--topic-arn arn:aws:sns:us-east-1:5XXXXXXXXXX6:WWHF
{
"MessageId": "eXXXXXXd-XXXX-XXXX-XXXX-764d3XXXXXX4"
}
22
ATTACK SCENARIOS – SMS PHISHING
23
ATTACK SCENARIOS – SMS PHISHING
24
ATTACK SCENARIOS – SMS PHISHING
25
ATTACK SCENARIOS – SMS PHISHING
26
ATTACK SCENARIOS – SMS PHISHING
27
ATTACK SCENARIOS – SMS PHISHING
28
ATTACK SCENARIOS – SMS PHISHING
29
ATTACK SCENARIOS – SMS PHISHING
30
ATTACK SCENARIOS – SMS PHISHING
31
ATTACK SCENARIOS – SMS PHISHING
32
ATTACK SCENARIOS – SMS PHISHING
33
ATTACK SCENARIOS – EMAIL PHISHING
Email Phishing
 Phishing, but with a phone number in the message
 Phone call is a secondary option
− Email is the primary delivery method
 Phone is just there for backup
− Memo from help desk notifying users
− Include number
− Victim calls the phone number
− Amazon accepts the call and places it into a “hold queue” (play music)
− Notify the testers
− Once ready, route the call to the legitimate help desk
− Amazon Connect “Managers” can listen in on the ongoing conversation
− Wiretapping laws…
34
ATTACK SCENARIOS – EMAIL PHISHING
35
ATTACK SCENARIOS – EMAIL PHISHING
36
ATTACK SCENARIOS – EMAIL PHISHING
37
ATTACK SCENARIOS – EMAIL PHISHING
38
ATTACK SCENARIOS – EMAIL PHISHING
39
ATTACK SCENARIOS – EMAIL PHISHING
40
ATTACK SCENARIOS – EMAIL PHISHING
4141
ATTACK SCENARIOS
OUTBOUND PHONE CALLS
42
ATTACK SCENARIOS – OUTBOUND CALL
Outbound Call to Target
 Connect provides an API that you can use to place outbound phone calls
 Outbound calls can be placed into a workflow which follows an automated system
“You have…1…new message”
“Please say your username”
“Please say your password”
“First message:”
 Lex recognizes the data and transcribes it for Lambda
 Lambda takes the creds and sends them to the tester
43
ATTACK SCENARIOS – OUTBOUND CALL
[~] aws connect start-outbound-voice-contact
--destination-phone-number "+1XXXXXX9001“
--contact-flow-id 8XXXXXX5-XXXX-XXXX-XXXX-7a751XXXXXX5
--instance-id f0XXXXXX-XXXX-XXXX-XXXX-abXXXXXXe7e1
--source-phone-number "+1XXXXXX2315"
{
"ContactId": "2XXXXXX3-XXXX-XXXX-XXXX-724c5XXXXXX5"
}
44
ATTACK SCENARIOS – OUTBOUND CALL
45
ATTACK SCENARIOS – OUTBOUND CALL
46
ATTACK SCENARIOS – DISTRACTION CALL
Outbound Call to Target
 Problem:
− Working on a test, couldn’t locate direct phone numbers for employees
− Found a dial-by-name directory, but could reach it directly
− Would only rollover to the directory if the receptionist/operator didn’t answer
 Solution:
− Outbound phone call to contact operator
− Operator answers, phone is busy
− Place a second call
− Routed straight to the directory and could reach employees directly
47
ATTACK SCENARIOS – DISTRACTION CALL
48
ATTACK SCENARIOS – DISTRACTION CALL
49
ATTACK SCENARIOS – DISTRACTION CALL
5050
WORKAROUNDS
51
WORKAROUNDS
How effective is it?
 Voice recognition is crucial (it’s a phone-based test!)
− It’s fine if Alexa doesn’t understand what song you want to play
− During a pentest? It could mean the difference between a full-on breach or empty report
 Early testing didn’t go so well
− My name was fine, but that’s not the goal
52
WORKAROUNDS
Names Are Hard
 Solution?
 Compare the voice-recognized results to a pre-built list of potential options
− In a pure phone-based engagement, you would normally have a list of target employees
− You know for a fact who should be answering the phone, easier to narrow down the list
− “Real world”
− Attacker gets a list of names and numbers from “the dark web”
(or a phone book)
− Use caller ID to reference a pool of specific area codes
and cross-reference names
 Can you apply this concept to passwords too?
5353
DEMO
VPN CONNECTION
54
DEMO - VPN
55
DEMO - VPN
56
DEMO - VPN
5757
RESOURCES
58
RESOURCES AND RESEARCH
Services
 Amazon Connect – Call Center
− https://aws.amazon.com/connect/
 Azure – Speech to Text
− https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text/
 Twilio - Speech Recognition
− https://www.twilio.com/speech-recognition
Defenses
 Google Assistant – Call Screening
− https://support.google.com/phoneapp/answer/9118387?hl=en
 Jolly Roger
− https://jollyrogertelephone.com/
 ItsLenny
− https://www.reddit.com/r/itslenny/
59
CALL ME
(773) 598-4494
@AntiSocialSE
MINNEAPOLIS | NEW YORK | PORTLAND | DENVER | DALLAS
https://www.netspi.com
https://www.facebook.com/netspi
@NetSPI
https://www.slideshare.net/NetSPI

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Automated Social Engineering for the Antisocial Engineer

  • 1. AUTOMATED SOCIAL ENGINEERING FOR THE ANTISOCIAL ENGINEER PATRICK SAYLER
  • 2. 2 ABOUT Patrick Sayler  Web Application and Network Pentester at NetSPI  Social Engineering  Phone  Onsite  Twitter: @psayler  Blog: blog.netspi.com
  • 3. 3 PRESENTATION OVERVIEW  Background on Phone SE  Current problems  Solutions  Service overview  Environment  Attack Scenarios  Inbound  Outbound  Demo  Addl. Resources and Research
  • 4. 4 INTRODUCTION Background on Phone SE  Typical engagement 1. Setup the phone 2. Mentally prepare 3. Make the call 4. Tell the target “do bad thing” 5. Hang up, breath a sigh of relief 6. Repeat 2-6 Easy, right?
  • 6. 6 INTRODUCTION How can I… avoid Asterisk avoid talking to someone make this better?
  • 7. 7 INTRODUCTION  Voice Clips  Record my own voice and play back the audio over the phone − Short lived. Too much work.  Text-to-Speech (TTS) − Found a website with an obviously robotic but legitimate sounding voice − Recorded 4 Phrases: “You have…1…new message” “Please say your username” “Please say your password” “First message:” − It worked!  [REDACTED] − Entry point into an environment. Got credentials, got DA
  • 8. 8 INTRODUCTION Okay. Now what?  Fun Experiment − Less structured engagements, more freeform  Still some hurdles − Annoying to setup − Didn’t scale well − Multiple users? Awkward to put together. − Too many people editing Asterisk extensions and sip.conf
  • 9. 9 INTRODUCTION We need:  Easy − Setup − Maintenance  Scalable − Multiple users − Multiple calls  Centralized − Recordings − Tracking and statistics Sounds familiar…
  • 11. 11 SOLUTION – AMAZON CONNECT Full Featured Call Center Service  Easy − Setup - Point and Click GUI − Maintenance - Managed by Amazon  Scalable − Multiple users − Multiple calls – Inbound & Outbound  Centralized − Recordings – S3 Bucket − Tracking and statistics
  • 12. 12 SOLUTION What can you do?  Inbound & outbound phone calls  Audio recording  Call routing/triaging  Customizable prompts and triggers  Cheap!  Integration with AWS ecosystem
  • 13. 13 SOLUTION Integration – Amazon Transcribe  Speech recognition  Convert voice to text  Run against the recordings in your S3 bucket − Easier to review post-engagement Integration – AWS Lambda  Run code  Process information received from recordings − Flag on specific keywords − “Password”  Literally anything you can write, it can do Integration – Amazon Lex  “Conversation Bot”
  • 18. 18 SOLUTION Integration  With the previous tools alone − Reacting to the situation − Cannot change what has already happened − Might be too late to use information  Lex + Lambda − Proactive − Actual interaction with the target − Can share information with you while it happens
  • 20. 20 ATTACK SCENARIOS – SMS PHISHING SMS Phishing  Phishing, but over text message instead of email  Same concepts and methodology apply − Mass delivery − Broad reach  AWS SNS to send the text message  Victim calls associated number − Prompted to provide credentials  Lex recognizes the data and transcribes it for Lambda  Lambda takes the creds and sends them − Notify the tester
  • 21. 21 ATTACK SCENARIOS – SMS PHISHING [~] aws sns publish –message "Your corporate account has been disabled due to malicious activity. Please contact +1-XXX-XXX-2315 to reactivate your service." --topic-arn arn:aws:sns:us-east-1:5XXXXXXXXXX6:WWHF { "MessageId": "eXXXXXXd-XXXX-XXXX-XXXX-764d3XXXXXX4" }
  • 22. 22 ATTACK SCENARIOS – SMS PHISHING
  • 23. 23 ATTACK SCENARIOS – SMS PHISHING
  • 24. 24 ATTACK SCENARIOS – SMS PHISHING
  • 25. 25 ATTACK SCENARIOS – SMS PHISHING
  • 26. 26 ATTACK SCENARIOS – SMS PHISHING
  • 27. 27 ATTACK SCENARIOS – SMS PHISHING
  • 28. 28 ATTACK SCENARIOS – SMS PHISHING
  • 29. 29 ATTACK SCENARIOS – SMS PHISHING
  • 30. 30 ATTACK SCENARIOS – SMS PHISHING
  • 31. 31 ATTACK SCENARIOS – SMS PHISHING
  • 32. 32 ATTACK SCENARIOS – SMS PHISHING
  • 33. 33 ATTACK SCENARIOS – EMAIL PHISHING Email Phishing  Phishing, but with a phone number in the message  Phone call is a secondary option − Email is the primary delivery method  Phone is just there for backup − Memo from help desk notifying users − Include number − Victim calls the phone number − Amazon accepts the call and places it into a “hold queue” (play music) − Notify the testers − Once ready, route the call to the legitimate help desk − Amazon Connect “Managers” can listen in on the ongoing conversation − Wiretapping laws…
  • 34. 34 ATTACK SCENARIOS – EMAIL PHISHING
  • 35. 35 ATTACK SCENARIOS – EMAIL PHISHING
  • 36. 36 ATTACK SCENARIOS – EMAIL PHISHING
  • 37. 37 ATTACK SCENARIOS – EMAIL PHISHING
  • 38. 38 ATTACK SCENARIOS – EMAIL PHISHING
  • 39. 39 ATTACK SCENARIOS – EMAIL PHISHING
  • 40. 40 ATTACK SCENARIOS – EMAIL PHISHING
  • 42. 42 ATTACK SCENARIOS – OUTBOUND CALL Outbound Call to Target  Connect provides an API that you can use to place outbound phone calls  Outbound calls can be placed into a workflow which follows an automated system “You have…1…new message” “Please say your username” “Please say your password” “First message:”  Lex recognizes the data and transcribes it for Lambda  Lambda takes the creds and sends them to the tester
  • 43. 43 ATTACK SCENARIOS – OUTBOUND CALL [~] aws connect start-outbound-voice-contact --destination-phone-number "+1XXXXXX9001“ --contact-flow-id 8XXXXXX5-XXXX-XXXX-XXXX-7a751XXXXXX5 --instance-id f0XXXXXX-XXXX-XXXX-XXXX-abXXXXXXe7e1 --source-phone-number "+1XXXXXX2315" { "ContactId": "2XXXXXX3-XXXX-XXXX-XXXX-724c5XXXXXX5" }
  • 44. 44 ATTACK SCENARIOS – OUTBOUND CALL
  • 45. 45 ATTACK SCENARIOS – OUTBOUND CALL
  • 46. 46 ATTACK SCENARIOS – DISTRACTION CALL Outbound Call to Target  Problem: − Working on a test, couldn’t locate direct phone numbers for employees − Found a dial-by-name directory, but could reach it directly − Would only rollover to the directory if the receptionist/operator didn’t answer  Solution: − Outbound phone call to contact operator − Operator answers, phone is busy − Place a second call − Routed straight to the directory and could reach employees directly
  • 47. 47 ATTACK SCENARIOS – DISTRACTION CALL
  • 48. 48 ATTACK SCENARIOS – DISTRACTION CALL
  • 49. 49 ATTACK SCENARIOS – DISTRACTION CALL
  • 51. 51 WORKAROUNDS How effective is it?  Voice recognition is crucial (it’s a phone-based test!) − It’s fine if Alexa doesn’t understand what song you want to play − During a pentest? It could mean the difference between a full-on breach or empty report  Early testing didn’t go so well − My name was fine, but that’s not the goal
  • 52. 52 WORKAROUNDS Names Are Hard  Solution?  Compare the voice-recognized results to a pre-built list of potential options − In a pure phone-based engagement, you would normally have a list of target employees − You know for a fact who should be answering the phone, easier to narrow down the list − “Real world” − Attacker gets a list of names and numbers from “the dark web” (or a phone book) − Use caller ID to reference a pool of specific area codes and cross-reference names  Can you apply this concept to passwords too?
  • 58. 58 RESOURCES AND RESEARCH Services  Amazon Connect – Call Center − https://aws.amazon.com/connect/  Azure – Speech to Text − https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text/  Twilio - Speech Recognition − https://www.twilio.com/speech-recognition Defenses  Google Assistant – Call Screening − https://support.google.com/phoneapp/answer/9118387?hl=en  Jolly Roger − https://jollyrogertelephone.com/  ItsLenny − https://www.reddit.com/r/itslenny/
  • 60. MINNEAPOLIS | NEW YORK | PORTLAND | DENVER | DALLAS https://www.netspi.com https://www.facebook.com/netspi @NetSPI https://www.slideshare.net/NetSPI