Analysing the Security of Google's implementation of OpenID Connect

Many millions of users routinely use their Google accounts to log in to relying party (RP) websites supporting the Google OpenID Connect service. OpenID Connect, a newly standardised single-sign-on protocol, builds an identity layer on top of the OAuth 2.0 protocol, which has itself been widely adopted to support identity management services. It adds identity management functionality to the OAuth 2.0 system and allows an RP to obtain assurances regarding the authenticity of an end user. A number of authors have analysed the security of the OAuth 2.0 protocol, but whether OpenID Connect is secure in practice remains an open question. We report on a large-scale practical study of Google's implementation of OpenID Connect, involving forensic examination of 103 RP websites which support its use for sign-in. Our study reveals serious vulnerabilities of a number of types, all of which allow an attacker to log in to an RP website as a victim user. Further examination suggests that these vulnerabilities are caused by a combination of Google's design of its OpenID Connect service and RP developers making design decisions which sacrifice security for simplicity of implementation. We also give practical recommendations for both RPs and OPs to help improve the security of real world OpenID Connect systems

Control priorization model for improving information security risk assessment

Evaluating particular assets for information security risk assessment should take into consideration the availability of adequate resources and return on investments (ROI). Despite the need for a good risk assessment framework, many of the existing frameworks lack of granularity guidelines and mostly depend on qualitative methods. Hence, they require additional time and cost to test all the information security controls. Further, the reliance on human inputs and feedback will increase subjective judgment in organizations. The main goal of this research is to design an efficient Information Security Control Prioritization (ISCP) model in improving the risk assessment process. Case studies based on penetration tests and vulnerability assessments were performed to gather data. Then, Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) was used to prioritize them. A combination of sensitivity analysis and expert interviews were used to test and validate the model. Subsequently, the performance of the model was evaluated by the risk assessment experts. The results demonstrate that ISCP model improved the quality of information security control assessment in the organization. The model plays a significant role in prioritizing the critical security technical controls during the risk assessment process. Furthermore, the model’s output supports ROI by identifying the appropriate controls to mitigate risks to an acceptable level in the organizations. The major contribution of this research is the development of a model which minimizes the uncertainty, cost and time of the information security control assessment. Thus, the clear practical guidelines will help organizations to prioritize important controls reliably and more efficiently. All these contributions will minimize resource utilization and maximize the organization’s information security

DAG-Based Attack and Defense Modeling: Don't Miss the Forest for the Attack Trees

This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to present a complete overview of graphical attack and defense modeling techniques based on DAGs. This consists of summarizing the existing methodologies, comparing their features and proposing a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements

Attribute-based cloud storage with secure provenance over encrypted data

National Research Foundation (NRF) Singapore; AXA Research Fun

Bounded-Collusion Attribute-Based Encryption from Minimal Assumptions

Attribute-based encryption (ABE) enables encryption of messages under access policies so that only users with attributes satisfying the policy can decrypt the ciphertext. In standard ABE, an arbitrary number of colluding users, each without an authorized attribute set, cannot decrypt the ciphertext. However, all existing ABE schemes rely on concrete cryptographic assumptions such as the hardness of certain problems over bilinear maps or integer lattices. Furthermore, it is known that ABE cannot be constructed from generic assumptions such as public-key encryption using black-box techniques. In this work, we revisit the problem of constructing ABE that tolerates collusions of arbitrary but a priori bounded size. We present an ABE scheme secure against bounded collusions that requires only semantically secure public-key encryption. Our scheme achieves significant improvement in the size of the public parameters, secret keys, and ciphertexts over the previous construction of bounded-collusion ABE from minimal assumptions by Gorbunov et al. (CRYPTO 2012). We also obtain bounded-collusion symmetric-key ABE (which requires the secret key for encryption) by replacing the public-key encryption with symmetric-key encryption, which can be built from the minimal assumption of one-way functions