arXiv subjects

Michael Specter

Publications and source records attributed to Michael Specter.

7 recordsLinked to original sources

arXiv2026

Don't Trust the Super-App: A Case Study of Russia's Max

Super-apps, an emerging mobile architecture, host third-party mini-apps inside a single app, allowing users to access diverse services. A decade of security research on the super-app ecosystem has all assumed super-apps to be a trusted intermediary. We argue this implicit trust is difficult to justify: China's WeChat is already shown to passively track its user's activity across mini-apps at extraordinary scale; Russia's MAX's parent company is reported to be deeply entangled with the state prosecution of online speech; and Iran's Bale was reported to be functioning in the world's longest internet shutdown due to its state-backed support. In this paper, we show how malicious super-apps have undeniable capabilities to silently undermine the security and privacy of mini-apps and users without leaving any trace. Using MAX as an example, we show how it can capture mini-app UI, read and write mini-app local storage, inject arbitrary JavaScript into a mini-app's runtime, mediate mini-app network traffic, and control authentication context in ways that can enable silent user impersonation. Sadly, these capabilities manifest themselves in any super-app because of the architectural privileges granted to them by design. We argue that mobile OS and app store interventions are urgently needed to close this architectural blind spot before it is further exploited.

cs.CR↗

arXiv2026

Uncovering Relationships between Android Developers, User Privacy, and Developer Willingness to Reduce Fingerprinting Risks

The major mobile platforms, Android and iOS, have introduced changes that restrict user tracking to improve user privacy, yet apps continue to covertly track users via device fingerprinting. We study the opportunity to improve this dynamic with a case study on mobile fingerprinting that evaluates developers' perceptions of how well platforms protect user privacy and how developers perceive platform privacy interventions. Specifically, we study developers' willingness to make changes to protect users from fingerprinting and how developers consider trade-offs between user privacy and developer effort. We do this via a survey of 246 Android developers, presented with a hypothetical Android change that protects users from fingerprinting at the cost of additional developer effort. We find developers overwhelmingly (89%) support this change, even when they anticipate significant effort, yet prefer the change be optional versus required. Surprisingly, developers who use fingerprinting are six times more likely to support the change, despite being most impacted by it. We also find developers are most concerned about compliance and enforcement. In addition, our results show that while most rank iOS above Android for protecting user privacy, this distinction significantly reduces among developers very familiar with fingerprinting. Thus there is an important opportunity for platforms and developers to collaboratively build privacy protections, and we present actionable ways platforms can facilitate this.

cs.CR↗

arXiv2025

Security and Privacy Analysis of Tile's Location Tracking Protocol

We conduct the first comprehensive security analysis of Tile, the second most popular crowd-sourced location-tracking service behind Apple's AirTags. We identify several exploitable vulnerabilities and design flaws, disproving many of the platform's claimed security and privacy guarantees: Tile's servers can persistently learn the location of all users and tags, unprivileged adversaries can track users through Bluetooth advertisements emitted by Tile's devices, and Tile's anti-theft mode is easily subverted. Despite its wide deployment -- millions of users, devices, and purpose-built hardware tags -- Tile provides no formal description of its protocol or threat model. Worse, Tile intentionally weakens its antistalking features to support an antitheft use-case and relies on a novel "accountability" mechanism to punish those abusing the system to stalk victims. We examine Tile's accountability mechanism, a unique feature of independent interest; no other provider attempts to guarantee accountability. While an ideal accountability mechanism may disincentivize abuse in crowd-sourced location tracking protocols, we show that Tile's implementation is subvertible and introduces new exploitable vulnerabilities. We conclude with a discussion on the need for new, formal definitions of accountability in this setting.

cs.CR↗

arXiv2023

Can large language models democratize access to dual-use biotechnology?

Large language models (LLMs) such as those embedded in 'chatbots' are accelerating and democratizing research by providing comprehensible information and expertise from many different fields. However, these models may also confer easy access to dual-use technologies capable of inflicting great harm. To evaluate this risk, the 'Safeguarding the Future' course at MIT tasked non-scientist students with investigating whether LLM chatbots could be prompted to assist non-experts in causing a pandemic. In one hour, the chatbots suggested four potential pandemic pathogens, explained how they can be generated from synthetic DNA using reverse genetics, supplied the names of DNA synthesis companies unlikely to screen orders, identified detailed protocols and how to troubleshoot them, and recommended that anyone lacking the skills to perform reverse genetics engage a core facility or contract research organization. Collectively, these results suggest that LLMs will make pandemic-class agents widely accessible as soon as they are credibly identified, even to people with little or no laboratory training. Promising nonproliferation measures include pre-release evaluations of LLMs by third parties, curating training datasets to remove harmful concepts, and verifiably screening all DNA generated by synthesis providers or used by contract research organizations and robotic cloud laboratories to engineer organisms or viruses.

cs.CY↗

arXiv2020

SonicPACT: An Ultrasonic Ranging Method for the Private Automated Contact Tracing (PACT) Protocol

Throughout the course of the COVID-19 pandemic, several countries have developed and released contact tracing and exposure notification smartphone applications (apps) to help slow the spread of the disease. To support such apps, Apple and Google have released Exposure Notification Application Programming Interfaces (APIs) to infer device (user) proximity using Bluetooth Low Energy (BLE) beacons. The Private Automated Contact Tracing (PACT) team has shown that accurately estimating the distance between devices using only BLE radio signals is challenging. This paper describes the design and implementation of the SonicPACT protocol to use near-ultrasonic signals on commodity iOS and Android smartphones to estimate distances using time-of-flight measurements. The protocol allows Android and iOS devices to interoperate, augmenting and improving the current exposure notification APIs. Our initial experimental results are promising, suggesting that SonicPACT should be considered for implementation by Apple and Google.

cs.NI↗

arXiv2019

KeyForge: Mitigating Email Breaches with Forward-Forgeable Signatures

Email breaches are commonplace, and they expose a wealth of personal, business, and political data that may have devastating consequences. The current email system allows any attacker who gains access to your email to prove the authenticity of the stolen messages to third parties -- a property arising from a necessary anti-spam / anti-spoofing protocol called DKIM. This exacerbates the problem of email breaches by greatly increasing the potential for attackers to damage the users' reputation, blackmail them, or sell the stolen information to third parties. In this paper, we introduce "non-attributable email", which guarantees that a wide class of adversaries are unable to convince any third party of the authenticity of stolen emails. We formally define non-attributability, and present two practical system proposals -- KeyForge and TimeForge -- that provably achieve non-attributability while maintaining the important protection against spam and spoofing that is currently provided by DKIM. Moreover, we implement KeyForge and demonstrate that that scheme is practical, achieving competitive verification and signing speed while also requiring 42% less bandwidth per email than RSA2048.

cs.CR↗

arXiv2018

Explaining Explanations: An Overview of Interpretability of Machine Learning

There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we provide our definition of explainability and show how it can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we conclude with suggested future research directions for explanatory artificial intelligence.

cs.AI↗