Threshold Password-Hardened Encryption Services

Password-hardened encryption (PHE) was introduced by Lai et al. at USENIX 2018 and immediately productized by VirgilSecurity. PHE is a password-based key derivation protocol that involves an oblivious external crypto service for key derivation. The security of PHE protects against offline brute-force attacks, even when the attacker is given the entire database. Furthermore, the crypto service neither learns the derived key nor the password. PHE supports key-rotation meaning that both the server and crypto service can update their keys without involving the user. While PHE significantly strengthens data security, it introduces a single point of failure because key-derivation always requires access to the crypto service. In this work, we address this issue and simultaneously increase security by introducing threshold password-hardened encryption. Our formalization of this primitive revealed shortcomings of the original PHE definition that we also address in this work. Following the spirit of prior works, we give a simple and efficient construction using lightweight tools only. We also implement our construction and evaluate its efficiency. Our experiments confirm the practical efficiency of our scheme and show that it is more efficient than common memory-hard functions, such as scrypt. From a practical perspective this means that threshold PHE can be used as an alternative to scrypt for password protection and key-derivation, offering better security in terms of offline brute force attacks

Social Engineering Exploits in Automotive Software Security: Modeling Human-targeted Attacks with SAM

Security cannot be implemented into a system retrospectively without considerable effort, so security must be takeninto consideration already at the beginning of the system development. The engineering of automotive softwareis by no means an exception to this rule. For addressing automotive security, the AUTOSAR and EAST-ADLstandards for domain-specific system and component modeling provide the central foundation as a start. The EASTADLextension SAM enables fully integrated security modeling for traditional feature-targeted attacks. Due to theCOVID-19 pandemic, the number of cyber-attacks has increased tremendously and of these, about 98 percent arebased on social engineering attacks. These social engineering attacks exploit vulnerabilities in human behaviors,rather than vulnerabilities in a system, to inflict damage. And these social engineering attacks also play a relevantbut nonetheless regularly neglected role for automotive software. The contribution of this paper is a novel modelingconcept for social engineering attacks and their criticality assessment integrated into a general automotive softwaresecurity modeling approach. This makes it possible to relate social engineering exploits with feature-related attacks.To elevate the practical usage, we implemented an integration of this concept into the established, domain-specificmodeling tool MetaEdit+. The tool support enables collaboration between stakeholders, calculates vulnerabilityscores, and enables the specification of security objectives and measures to eliminate vulnerabilities