Honeywords Making Password-Cracking Detectable By Ari Juels and - - PowerPoint PPT Presentation

Honeywords

Making Password-Cracking Detectable By Ari Juels and Ronald L. Rivest Presented by Nunzio Cicone and Hans-Peter Höllwirth

Setting

Password Attacks

• Passwords are a notoriously weak authentication mechanism
• One significant attack scenario: stolen files of password hashes
• Can be used to find password that corresponds to stored hash value
• ffline by brute-force search

Setting

Examples

6 million hashed user passwords stolen from LinkedIn in 2012 Hashed passwords of Evernote‘s 50 million users stolen in 2013

Setting

Common Defense Approaches

• Make password hashing more complex and time-consuming
• “Salting”: Adding random digits to each hashed value
• Example: Hashing passwords with complex cryptographic functions, salting

them and then hashing the result again

• However, also slows down authentication process for legitimate users
• Set up fake user accounts (“honeypot accounts”)
• Trap set to detect unauthorized use of information systems
• However, does not detect attack on legitimate user accounts

Honeywords

• Create additional “honeyword” passwords
• Store the honeywords with the real passwords in a hash file
• Incorporate an auxiliary secure server called a “honeychecker”
• When a login is attempted, the main server verifies the request with the

honeychecker

Honeywords

A User Login

User Password Bob HoneywordA Bob HoneywordB Bob RealPassword Bob HoneywordC Bob HoneywordD Users Check Alice 1 James 5 Nick 5 Bob 3 Emily 4 Login Attempt User: Bob Password: RealPassword Main Server Honeychecker Check( 3 )

Honeywords

Malicious Login

User Password Bob HoneywordA Bob HoneywordB Bob RealPassword Bob HoneywordC Bob HoneywordD Users Check Alice 1 James 5 Nick 5 Bob 3 Emily 4 Main Server Honeychecker Login Attempt User: Bob Password: HoneywordA Check( 1 )

Honeywords

Design Principles

• Distributed Security
• No Additional Risk
• Simplicity
• Flexibility

Honeyword Generation

Overview

• Problem setting: make the user-chosen password undistinguishable

from generated honeywords

• Two classes of approaches split according to whether there is an impact
• n user interface (UI)
• Legacy-UI: password-change UI is unchanged – user chooses real password
• Modified-UI: password-change UI is modified to allow for a better

honeyword generation

Honeyword Generation

Legacy-UI Password Changes - Chaffing by Tweaking

• “Tweak” selected character positions of the password to obtain the

honeywords

• For each selected position the character of the real password is replaced by a

randomly-chosen character of the same type

• Alternatives
• Chaffing-by-tail-tweaking: tweak last t positions of password
• Chaffing-by-tweaking-digits: tweak last t positions containing digits

Example where t = 4 BG+1a745 -> BG+7a305 BG+2a177 BG+9a587 BG+0a602

Honeyword Generation

Legacy-UI Password Changes – Chaffing-with-a-Password Model

• Chooses honeywords from a given list of thousands/millions of passwords
• Uses probabilistic model of real passwords
• May not depend on user-chosen password
• However, attacker might have access to the list of passwords

Example mice3blind -> gold5rings name8honey flat7sorts

Propose a password: myPassword Append “413” to password. Enter new password: myPassword413

Honeyword Generation

Modified-UI Password Changes – “take-a-tail” Method

• Request password from user and then modify it with a randomized tail

Generated honeywords: myPassword798 myPassword982 myPassword113 myPassword056 myPassword935 myPassword664

Honeyword Generation

Hybrid Generation Methods

• Combining several methods can result in better honeywords
• Combine both legacy-UI techniques:
• Require the user to use digits at the end of the password
• Chaffing-with-a-password to generate new random words
• Chaffing-by-tweaking-digits on all words

abacad513 snurfle672 zinja897 abacad941 snurfle134 zinja320 abacad004 snurfle845 zinja461 abacad752 snurfle772 zinja389

Policy Choices

Detected Password-Cracking

• Honeyword entered – possible actions
• Setting off an alarm or notifying a system adminstrator
• Letting login proceed as usual
• Letting the login proceed, but on a honeypot system
• Tracing the source of the login carefully
• Shutting down that user‘s account or the computer system
• Per-user policies
• Use honeypot accounts
• Selective alarms: different policies across user population

Policy Choices

More to Consider

• Password eligibility
• Require certain password syntax
• Check for/disallow dictionary words, password re-use, most common and

popular passwords

• Failover mode
• Logins can proceed if honeychecker becomes unreachable to prevent denial-
• f-service attacks
• Honeywords are temporarily promoted to become acceptable passwords

Attack Scenarios

Attacking the Honeychecker

• All communication between honeychecker and main system needs to be

authenticated

• If an adversary takes down the honeychecker, the system will enter a

failover state

• Only, a small increase in password guessability
• Requests to the honeychecker can be stored and sent when the honeychecker

becomes available again

Attack Scenarios

Likelihood Attack

• The attacker can try to determine which passwords are honeywords
• “NewtonSaid:F=ma” is likely a user generated password
• Advise users to pick passwords that will will be similar to honeywords
• The generator can be given a private list of passwords that look user

generated and occasionally include them

• The use of “tough nuts” that cannot be cracked makes it harder for the

attacker to know for sure that the user generated password has been found

Attack Scenarios

Denial-of-Service

• An attacker can try to fake an attack
• The attacker knows a single user password “kerfluffle346”
• Sends a large number of requests
• kerfluffle467, kerfluffle972, kerfluffle672, kerfluffle019, kerfluffle735,

kerfluffle892, kerfluffle200, kerfluffle651, kerfluffle875, kerfluffle023

• Only use a small percentage of possible honeywords
• The DoS attack will be recognizable from real attacks

Attack Scenarios

Effect on Common Attack Scenarios

• Against general password guessing, honeywords have no effect
• Using targeted password guessing, the attacker may subvert the

effectiveness of honeywords

• Attacks on multiple systems
• Modified UI techniques will provide users with different passwords
• Legacy UI techniques have a chance of randomly generating the same

honeyword on two different systems

Conclusion

• Eventually, passwords should be supplemented with stronger and more

convenient authentication methods

• A simple and powerful new line of defence in the security of hashed

passwords

• Decreases the value of the stolen password hash files
• Makes password cracking detectable