Cracking-Resistant Password Vaults using Natural Language Encoders

Password vaults are increasingly popular applications that store multiple passwords encrypted under a single master password that the user memorizes. A password vault can greatly reduce the burden on a user of remembering passwords, but introduces a single point of failure. An attacker that obtains a user’s encrypted vault can mount offline brute-force attacks and, if successful, compromise all of the passwords in the vault. In this paper, we investigate the construction of encrypted vaults that resist such offline cracking attacks and force attackers instead to mount online attacks. Our contributions are as follows. We present an attack and supporting analysis showing that a previous design for cracking-resistant vaults—the only one of which we are aware—actually degrades security relative to conventional password-based approaches. We then introduce a new type of secure encoding scheme that we call a natural language encoder (NLE). An NLE permits the construction of vaults which, when decrypted with the wrong master password, produce plausible-looking decoy passwords. We show how to build NLEs using existing tools from natural language processing, such as n-gram models and probabilistic context-free grammars, and evaluate their ability to generate plausible decoys. Finally, we present, implement, and evaluate a full, NLE-based cracking-resistant vault system called NoCrack

Honey Chatting: A novel instant messaging system robust to eavesdropping over communication

There have been many efforts to strengthen security of Instant Messaging (IM) system. One of the typical technologies is the conventional message encryption using a secret or private key. However, the key is fundamentally vulnerable to a bruteforce attack, causing to acquire the original message. In this respect, a countermeasure was suggested as the way to generating plausible-looking but fake plaintexts, which is called Honey Encryption (HE). In this paper, we present a HE-based statistical scheme and design a Honey Chatting application, which is robust to eavesdropping. Besides, we verify the effectiveness of the Honey Chatting by comparing the entropy of decrypted messages through experiments

Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data

We develop the first universal password model -- a password model that, once pre-trained, can automatically adapt to any password distribution. To achieve this result, the model does not need to access any plaintext passwords from the target set. Instead, it exploits users' auxiliary information, such as email addresses, as a proxy signal to predict the underlying target password distribution. The model uses deep learning to capture the correlation between the auxiliary data of a group of users (e.g., users of a web application) and their passwords. It then exploits those patterns to create a tailored password model for the target community at inference time. No further training steps, targeted data collection, or prior knowledge of the community's password distribution is required. Besides defining a new state-of-the-art for password strength estimation, our model enables any end-user (e.g., system administrators) to autonomously generate tailored password models for their systems without the often unworkable requirement of collecting suitable training data and fitting the underlying password model. Ultimately, our framework enables the democratization of well-calibrated password models to the community, addressing a major challenge in the deployment of password security solutions on a large scale.Comment: v0.0

Honey Encryption for Language

Honey Encryption (HE), introduced by Juels and Ristenpart (Eurocrypt 2014), is an encryption paradigm designed to produce ciphertexts yielding plausible-looking but bogus plaintexts upon decryption with wrong keys. Thus brute-force attackers need to use additional information to determine whether they indeed found the correct key. At the end of their paper, Juels and Ristenpart leave as an open question the adaptation of honey encryption to natural language messages. A recent paper by Chatterjee et al. takes a mild attempt at the challenge and constructs a natural language honey encryption scheme relying on simple models for passwords. In this position paper we explain why this approach cannot be extended to reasonable-size human-written documents e.g. e-mails. We propose an alternative approach and evaluate its security

On the Gold Standard for Security of Universal Steganography

While symmetric-key steganography is quite well understood both in the information-theoretic and in the computational setting, many fundamental questions about its public-key counterpart resist persistent attempts to solve them. The computational model for public-key steganography was proposed by von Ahn and Hopper in EUROCRYPT 2004. At TCC 2005, Backes and Cachin gave the first universal public-key stegosystem - i.e. one that works on all channels - achieving security against replayable chosen-covertext attacks (SS-RCCA) and asked whether security against non-replayable chosen-covertext attacks (SS-CCA) is achievable. Later, Hopper (ICALP 2005) provided such a stegosystem for every efficiently sampleable channel, but did not achieve universality. He posed the question whether universality and SS-CCA-security can be achieved simultaneously. No progress on this question has been achieved since more than a decade. In our work we solve Hopper's problem in a somehow complete manner: As our main positive result we design an SS-CCA-secure stegosystem that works for every memoryless channel. On the other hand, we prove that this result is the best possible in the context of universal steganography. We provide a family of 0-memoryless channels - where the already sent documents have only marginal influence on the current distribution - and prove that no SS-CCA-secure steganography for this family exists in the standard non-look-ahead model.Comment: EUROCRYPT 2018, llncs styl

Fooling an Unbounded Adversary with a Short Key, Repeatedly: The Honey Encryption Perspective

This article is motivated by the classical results from Shannon that put the simple and elegant one-time pad away from practice: key length has to be as large as message length and the same key could not be used more than once. In particular, we consider encryption algorithm to be defined relative to specific message distributions in order to trade for unconditional security. Such a notion named honey encryption (HE) was originally proposed for achieving best possible security for password based encryption where secrete key may have very small amount of entropy. Exploring message distributions as in HE indeed helps circumvent the classical restrictions on secret keys.We give a new and very simple honey encryption scheme satisfying the unconditional semantic security (for the targeted message distribution) in the standard model (all previous constructions are in the random oracle model, even for message recovery security only). Our new construction can be paired with an extremely simple yet "tighter" analysis, while all previous analyses (even for message recovery security only) were fairly complicated and require stronger assumptions. We also show a concrete instantiation further enables the secret key to be used for encrypting multiple messages