arXiv subjects

Tamjid Al Rahat

Publications and source records attributed to Tamjid Al Rahat.

3 recordsLinked to original sources

arXiv2025

Automated Repair of OpenID Connect Programs (Extended Version)

OpenID Connect has revolutionized online authentication based on single sign-on (SSO) by providing a secure and convenient method for accessing multiple services with a single set of credentials. Despite its widespread adoption, critical security bugs in OpenID Connect have resulted in significant financial losses and security breaches, highlighting the need for robust mitigation strategies. Automated program repair presents a promising solution for generating candidate patches for OpenID implementations. However, challenges such as domain-specific complexities and the necessity for precise fault localization and patch verification must be addressed. We propose AuthFix, a counterexample-guided repair engine leveraging LLMs for automated OpenID bug fixing. AuthFix integrates three key components: fault localization, patch synthesis, and patch verification. By employing a novel Petri-net-based model checker, AuthFix ensures the correctness of patches by effectively modeling interactions. Our evaluation on a dataset of OpenID bugs demonstrates that AuthFix successfully generated correct patches for 17 out of 23 bugs (74%), with a high proportion of patches semantically equivalent to developer-written fixes.

cs.SE↗

arXiv2023

Cerberus: Query-driven Scalable Vulnerability Detection in OAuth Service Provider Implementations

OAuth protocols have been widely adopted to simplify user authentication and service authorization for third-party applications. However, little effort has been devoted to automatically checking the security of the libraries that service providers widely use. In this paper, we formalize the OAuth specifications and security best practices, and design Cerberus, an automated static analyzer, to find logical flaws and identify vulnerabilities in the implementation of OAuth service provider libraries. To efficiently detect security violations in a large codebase of service provider implementation, Cerberus employs a query-driven algorithm for answering queries about OAuth specifications. We demonstrate the effectiveness of Cerberus by evaluating it on datasets of popular OAuth libraries with millions of downloads. Among these high-profile libraries, Cerberus has identified 47 vulnerabilities from ten classes of logical flaws, 24 of which were previously unknown. We got acknowledged by the developers of eight libraries and had three accepted CVEs.

cs.CR↗

arXiv2021

Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning

Since GDPR came into force in May 2018, companies have worked on their data practices to comply with this privacy law. In particular, since the privacy policy is the essential communication channel for users to understand and control their privacy, many companies updated their privacy policies after GDPR was enforced. However, most privacy policies are verbose, full of jargon, and vaguely describe companies' data practices and users' rights. Therefore, it is unclear if they comply with GDPR. In this paper, we create a privacy policy dataset of 1,080 websites labeled with the 18 GDPR requirements and develop a Convolutional Neural Network (CNN) based model which can classify the privacy policies with an accuracy of 89.2%. We apply our model to perform a measurement on the compliance in the privacy policies. Our results show that even after GDPR went into effect, 97% of websites still fail to comply with at least one requirement of GDPR.

cs.CR↗