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We go on to propose the first-ever defense against such targeted attacks, by way of personalized password strength meters (PPSMs).These are password strength meters that can warn users when they are picking passwords that are vulnerable to attacks, including targeted ones that take advantage of the user’s previously compromised passwords.
We show how to learn good password similarity models using a compilation of 1.4 billion leaked email, password pairs.
Using our trained models of password similarity, we exhibit the most damaging targeted attack to date.
Simulations indicate that our attack compromises more than 16% of user accounts in less than a thousand guesses, should one of their other passwords be known to the attacker and despite the use of state-of-the art countermeasures.
We show via a case study involving a large university authentication service that the attacks are also effective in practice.
A practical clone search engine relies on a robust vector representation of assembly code.
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However, the existing clone search approaches, which rely on a manual feature engineering process to form a feature vector for an assembly function, fail to consider the relationships between features and identify those unique patterns that can statistically distinguish assembly functions.We then use our model of the FAPI to precisely define central security properties.In an attempt to prove these properties, we uncover partly severe attacks, breaking authentication, authorization, and session integrity properties.We develop mitigations against these attacks and finally are able to formally prove the security of a fixed version of the FAPI.Although financial applications are high-stakes environments, this work is the first to formally analyze and, importantly, verify an Open Banking security profile.We conduct extensive experiments and benchmark the learning model with state-of-the-art static and dynamic clone search approaches.We show that the learned representation is more robust and significantly outperforms existing methods against changes introduced by obfuscation and optimizations.The FAPI is a profile of OAuth 2.0 designed for high-risk scenarios and aiming to be secure against very strong attackers.To achieve this level of security, the FAPI employs a range of mechanisms that have been developed to harden OAuth 2.0, such as Code and Token Binding (including m TLS and OAUTB), JWS Client Assertions, and Proof Key for Code Exchange.Attack Directories, Not Caches: Side Channel Attacks in a Non-Inclusive World Mengjia Yan (University of Illinois at Urbana Champaign), Read Sprabery (University of Illinois at Urbana Champaign), Bhargava Gopireddy (University of Illinois at Urbana Champaign), Christopher Fletcher (University of Illinois at Urbana Champaign), Roy Campbell (University of Illinois at Urbana Champaign), Josep Torrellas (University of Illinois at Urbana Champaign) Although clouds have strong virtual memory isolation guarantees, cache attacks stemming from shared caches have proved to be a large security problem.However, despite the past effectiveness of cache attacks, their viability has recently been called into question on modern systems, due to trends in cache hierarchy design moving away from inclusive cache hierarchies.