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Federico Zappone

Publications and source records attributed to Federico Zappone.

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TENET: Telegram Mini App (in)security

Telegram, with over 450 million daily active users, has introduced Mini Apps---web-based applications running directly within its client. However, this integration introduces notable security risks. As we demonstrate, many Mini Apps store authentication materials---such as session tokens and wallet mnemonic phrases---in plaintext on client devices, exposing users to unauthorized access, impersonation, and financial exploitation. While insecure client-side storage is a known risk in web applications, the Telegram Mini App ecosystem presents a uniquely dangerous combination of factors absent from prior work: no platform-level security review, no storage access restrictions, a financially motivated user base handling live cryptocurrency assets, and a WebView environment that offers weaker protections than standalone browsers. To investigate this threat, we present TENET, a purpose-built auditing tool whose design decisions---pattern selection, entropy thresholds, and charset validation---are grounded in the structural properties of the secrets targeted and empirically validated against a ground-truth dataset. We screened 61 Mini Apps using a stratified, popularity-weighted sampling strategy based on popularity. Of the 37 applications that met our processing criteria and were analyzed, 30 exhibited security flaws, which we classify into three severity tiers: plaintext storage, recoverable encryption, and replayable tokens. Notably, even Telegram's official Wallet exhibits a severe vulnerability that may lead to full account compromise. Following our responsible disclosure, Telegram implemented two new secure-storage APIs, and our post-remediation verification confirmed that its official Wallet no longer exposes the recovery mnemonic in plaintext. Finally, we propose mitigation measures and best practices for both Telegram platform developers and third-party Mini App creators.

cs.CR

Integrating Heuristics and Learning in a Computational Architecture for Cognitive Trading

The successes of Artificial Intelligence in recent years in areas such as image analysis, natural language understanding and strategy games have sparked interest from the world of finance. Specifically, there are high expectations, and ongoing engineering projects, regarding the creation of artificial agents, known as robotic traders, capable of juggling the financial markets with the skill of experienced human traders. Obvious economic implications aside, this is certainly an area of great scientific interest, due to the challenges that such a real context poses to the use of AI techniques. Precisely for this reason, we must be aware that artificial agents capable of operating at such levels are not just round the corner, and that there will be no simple answers, but rather a concurrence of various technologies and methods to the success of the effort. In the course of this article, we review the issues inherent in the design of effective robotic traders as well as the consequently applicable solutions, having in view the general objective of bringing the current state of the art of robo-trading up to the next level of intelligence, which we refer to as Cognitive Trading. Key to our approach is the joining of two methodological and technological directions which, although both deeply rooted in the disciplinary field of artificial intelligence, have so far gone their separate ways: heuristics and learning.

cs.AI