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1. Cost-Asymmetric Memory Hard Password Hashing

   Bai, Wenjie ; Blocki, J ; Ameri, Mohammad Hassan ( September 2022 , Security and Cryptography for Networks. SCN 2022)

   Galdi, C ; Jarecki, S. (Ed.)

   In the past decade billions of user passwords have been exposed to the dangerous threat of offline password cracking attacks. An offline attacker who has stolen the cryptographic hash of a user’s password can check as many password guesses as s/he likes limited only by the resources that s/he is willing to invest to crack the password. Pepper and key-stretching are two techniques that have been proposed to deter an offline attacker by increasing guessing costs. Pepper ensures that the cost of rejecting an incorrect password guess is higher than the (expected) cost of verifying a correct password guess. This is useful because most of the offline attacker’s guesses will be incorrect. Unfortunately, as we observe the traditional peppering defense seems to be incompatible with modern memory hard key-stretching algorithms such as Argon2 or Scrypt. We introduce an alternative to pepper which we call Cost-Asymmetric Memory Hard Password Authentication which benefits from the same cost-asymmetry as the classical peppering defense i.e., the cost of rejecting an incorrect password guess is larger than the expected cost to authenticate a correct password guess. When configured properly we prove that our mechanism can only reduce the percentage of user passwords that are cracked by a rational offline attacker whose goal is to maximize (expected) profit i.e., the total value of cracked passwords minus the total guessing costs. We evaluate the effectiveness of our mechanism on empirical password datasets against a rational offline attacker. Our empirical analysis shows that our mechanism can significantly reduce the percentage of user passwords that are cracked by a rational attacker by up to 10%. 

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2. DAHash: Distribution Aware Tuning of Password Hashing Costs

   https://doi.org/10.1007/978-3-662-64331-0_20  

   Bai, Wenjie ; Blocki, Jeremiah. ( January 2021 , International Conference on Financial Cryptography and Data Security (FC 2021))

   Borisov, N. (Ed.)

   An attacker who breaks into an authentication server and steals all of the cryptographic password hashes is able to mount an offline-brute force attack against each user’s password. Offline brute-force attacks against passwords are increasingly commonplace and the danger is amplified by the well documented human tendency to select low-entropy password and/or reuse these passwords across multiple accounts. Moderately hard password hashing functions are often deployed to help protect passwords against offline attacks by increasing the attacker’s guessing cost. However, there is a limit to how “hard” one can make the password hash function as authentication servers are resource constrained and must avoid introducing substantial authentication delay. Observing that there is a wide gap in the strength of passwords selected by different users we introduce DAHash (Distribution Aware Password Hashing) a novel mechanism which reduces the number of passwords that an attacker will crack. Our key insight is that a resource-constrained authentication server can dynamically tune the hardness parameters of a password hash function based on the (estimated) strength of the user’s password. We introduce a Stackelberg game to model the interaction between a defender (authentication server) and an offline attacker. Our model allows the defender to optimize the parameters of DAHash e.g., specify how much effort is spent in hashing weak/moderate/high strength passwords. We use several large scale password frequency datasets to empirically evaluate the effectiveness of our differentiated cost password hashing mechanism. We find that the defender who uses our mechanism can reduce the fraction of passwords that would be cracked by a rational offline attacker by up to 15%. 

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3. Password Strength Signaling: A Counter-Intuitive Defense Against Password Cracking

   https://doi.org/10.1007/978-3-030-90370-1_18  

   Bai, Wenjie ; Blocki, Jeremiah ; Harsha, Ben. ( October 2021 , Decision and Game Theory for Security: 12th International Conference, GameSec 2021)

   We introduce password strength signaling as a potential defense against password cracking. Recent breaches have exposed billions of user passwords to the dangerous threat of offline password cracking attacks. An offline attacker can quickly check millions (or sometimes billions/trillions) of password guesses by comparing a candidate password’s hash value with a stolen hash from a breached authentication server. The attacker is limited only by the resources he is willing to invest. We explore the feasibility of applying ideas from Bayesian Persuasion to password authentication. Our key idea is to have the authentication server store a (noisy) signal about the strength of each user password for an offline attacker to find. Surprisingly, we show that the noise distribution for the signal can often be tuned so that a rational (profit-maximizing) attacker will crack fewer passwords. The signaling scheme exploits the fact that password cracking is not a zero-sum game i.e., it is possible for an attacker to increase their profit in a way that also reduces the number of cracked passwords. Thus, a well-defined signaling strategy will encourage the attacker to reduce his guessing costs by cracking fewer passwords. We use an evolutionary algorithm to compute the optimal signaling scheme for the defender. We evaluate our mechanism on several password datasets and show that it can reduce the total number of cracked passwords by up to 12% (resp. 5%) of all users in defending against offline (resp. online) attacks. While the results of our empirical analysis are positive we stress that we view the current solution as a proof-of-concept as there are important societal concerns that would need to be considered before adopting our password strength signaling solution. 

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4. Just In Time Hashing

   https://doi.org/10.1109/EuroSP.2018.00033  

   Harsha, Benjamin ; Blocki, Jeremiah ( April 2018 , 2018 IEEE European Symposium on Security and Privacy (EuroS&P))

   In the past few years billions of user passwords have been exposed to the threat of offline cracking attempts. Such brute-force cracking attempts are increasingly dangerous as password cracking hardware continues to improve and as users continue to select low entropy passwords. Key-stretching techniques such as hash iteration and memory hard functions can help to mitigate the risk, but increased key-stretching effort necessarily increases authentication delay so this defense is fundamentally constrained by usability concerns. We introduce Just in Time Hashing (JIT), a client side key-stretching algorithm to protect user passwords against offline brute-force cracking attempts without increasing delay for the user. The basic idea is to exploit idle time while the user is typing in their password to perform extra key-stretching. As soon as the user types in the first character(s) of their password our algorithm immediately begins filling memory with hash values derived from the character(s) that the user has typed thus far. We conduct a user study to guide the development of JIT e.g. by determining how much extra key-stretching could be performed during idle cycles or how many consecutive deletions JIT may need to handle. Our security analysis demonstrates that JIT can substantially increase guessing costs over traditional key-stretching algorithms with equivalent (or less) authentication delay. Specifically an empirical evaluation using existing password datasets demonstrates that JIT increases guessing costs by nearly an order of magnitude in comparison to standard key-stretching techniques with comparable delay. We provide a proof-of-concept implementation of a Just in Time Hashing algorithm by modifying Argon2. 

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5. Towards a Rigorous Statistical Analysis of Empirical Password Datasets

   Blocki, Jeremiah ; Liu, Peiyuan ( January 2023 , 2023 IEEE Symposium on Security and Privacy (SP))

   A central challenge in password security is to characterize the attacker's guessing curve i.e., what is the probability that the attacker will crack a random user's password within the first G guesses. A key challenge is that the guessing curve depends on the attacker's guessing strategy and the distribution of user passwords both of which are unknown to us. In this work we aim to follow Kerckhoffs's principal and analyze the performance of an optimal attacker who knows the password distribution. Let \lambda_G denote the probability that such an attacker can crack a random user's password within G guesses. We develop several statistically rigorous techniques to upper and lower bound \lambda_G given N independent samples from the unknown password distribution P. We show that our upper/lower bounds on \lambda_G hold with high confidence and we apply our techniques to analyze eight large password datasets. Our empirical analysis shows that even state-of-the-art password cracking models are often significantly less guess efficient than an attacker who can optimize its attack based on its (partial) knowledge of the password distribution. We also apply our statistical tools to re-examine different models of the password distribution i.e., the empirical password distribution and Zipf's Law. We find that the empirical distribution closely matches our upper/lower bounds on \lambda_G when the guessing number G is not too large i.e., G << N. However, for larger values of G our empirical analysis rigorously demonstrates that the empirical distribution (resp. Zipf's Law) overestimates the attacker's success rate. We apply our statistical techniques to upper/lower bound the effectiveness of password throttling mechanisms (key-stretching) which are used to reduce the number of attacker guesses G. Finally, if we are willing to make an additional assumption about the way users respond to password restrictions, we can use our statistical techniques to evaluate the effectiveness of various password composition policies which restrict the passwords that users may select. 

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