Search arXivSearch

arXiv · 2307.09317

Measuring the Leakage and Exploitability of Authentication Secrets in Super-apps: The WeChat Case

Abstract

We conduct a large-scale measurement of developers' insecure practices leading to mini-app to super-app authentication bypass, among which hard-coding developer secrets for such authentication is a major contributor. We also analyze the exploitability and security consequences of developer secret leakage in mini-apps by examining individual super-app server-side APIs. We develop an analysis framework for measuring such secret leakage, and primarily analyze 110,993 WeChat mini-apps, and 10,000 Baidu mini-apps (two of the most prominent super-app platforms), along with a few more datasets to test the evolution of developer practices and platform security enforcement over time. We found a large number of WeChat mini-apps (36,425, 32.8%) and a few Baidu mini-apps (112) leak their developer secrets, which can cause severe security and privacy problems for the users and developers of mini-apps. A network attacker who does not even have an account on the super-app platform, can effectively take down a mini-app, send malicious and phishing links to users, and access sensitive information of the mini-app developer and its users. We responsibly disclosed our findings and also put forward potential directions that could be considered to alleviate/eliminate the root causes of developers hard-coding the app secrets in the mini-app's front-end code.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Supraja Baskaran, Lianying Zhao, Mohammad Mannan, Amr Youssef. 2023-07-18. Measuring the Leakage and Exploitability of Authentication Secrets in Super-apps: The WeChat Case. https://arxiv.org/abs/2307.09317

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Starfish: Rebalancing Multi-Party Off-Chain Payment Channels

Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their efficiency and applicability under realistic network topologies. Cycle-based approaches only enable rebalancing among nodes that form cycles, while non-cycle-based approaches incur high on-chain operation costs. In this study, we propose Starfish, a rebalancing approach designed for the locally star-like structures formed by high-degree routing hubs and their incident channels in real-world PCNs. By leveraging a hub-centric structure, Starfish reduces the on-chain overhead of rebalancing from quadratic to linear in the number of channels, achieving high rebalancing efficiency. We formally prove the security of Starfish and conduct comparative experiments against existing rebalancing techniques to demonstrate its effectiveness.

cs.CR

Proof-of-Authorship for Diffusion-based AI Generated Content

Recent advancements in AI-generated content (AIGC) have introduced new challenges in intellectual property protection and the authentication of generated objects. We focus on scenarios in which an author seeks to assert authorship of an object generated using latent diffusion models (LDMs), in the presence of adversaries who attempt to falsely claim authorship of objects they did not create. While proof-of-ownership has been studied in the context of multimedia content through techniques such as time-stamping and watermarking, these approaches face notable limitations. In contrast to traditional content creation sources (e.g., cameras), the LDM generation process offers greater control to the author. Specifically, the random seed used during generation can be deliberately chosen. By binding the seed to the author's identity using cryptographic functions, the author can assert to be the creator of the object. We refer to this stronger guarantee as proof-of-authorship, since only the creator of the object can legitimately claim the object. This contrasts with proof-of-ownership via time-stamping or watermarking, where any entity could potentially claim ownership of an object by being the first to timestamp or embed the watermark. We propose a proof-of-authorship framework involving a probabilistic adjudicator who quantifies the probability that a claim is false. Furthermore, unlike prior approaches, the proposed framework does not involve any secret. We explore various attack scenarios and analyze design choices using Stable Diffusion 2.1 and XL as representative case studies.

cs.CR

Impact Analysis of Speech Representation Learning Models for Acoustic Side-Channel Attack

Acoustic side-channel attacks (ASCA) on keyboards have gained increasing attention, yet impact of speech representation learning models in ASCA remains unexplored. Addressing this, we introduce KEYAC, a dataset designed to analyze representation generalization for ASCA under both standard and VoIP codec settings. On KEYAC, we evaluate six representation learning models under zero-shot and partial fine-tuning settings using fully connected and convolutional networks. Results show that while partial fine-tuning improves performance, models struggle to generalize across VoIP codecs. We hypothesize this limitation stems from inadequate modeling of nonlinear feature interactions in conventional fine-tuning architectures. To address this, we employ Kolmogorov-Arnold Networks (KAN) for fine-tuning. Empirical results show that KAN-based fine-tuning consistently outperforms the baselines and establishes a new state-of-the-art on KEYAC.

cs.CR