arXiv subjects

Mengxiao Wang

Publications and source records attributed to Mengxiao Wang.

7 recordsLinked to original sources

arXiv2026

SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes

Autonomous large language model (LLM) agents are moving rapidly into high-stakes domains, yet existing agentic-AI security studies remain largely domain-agnostic and overlook the distinctive, high-consequence attack surface such settings create. We examine this gap through financial trading agents, a representative case of high-stakes agentic security, where a single compromised agent has direct execution authority over real capital in an adversarial, reflexive market. To this end, we present FARSIGHT (Financial Agent Robustness and Security Investigation and Global Holistic Testing), a framework that performs scheme-level evaluation of financial LLM agents on two axes: robustness under market turbulence (including flash-crash-like scenarios), and security against three attack types: attacks on information sources, attacks on agents, and agent-as-attacker behaviors. Applying FARSIGHT to 15 representative academic schemes, we find that most overlook robustness and realistic adversarial threats: 80% fail at least one core robustness metric and 100% exhibit security vulnerabilities. These two failure modes are inseparable: a small misjudgment can cascade into a market-wide crash on its own, while an adversary can deliberately trigger the same collapse at minimal cost.

cs.CR↗

arXiv2026

GuardianPWA: Enhancing Security Throughout the Progressive Web App Installation Lifecycle

Progressive Web App (PWA) installation is critical for integrating web and mobile app functionalities, offering a seamless user experience. However, ensuring the security of the PWA installation lifecycle is essential for maintaining user trust and privacy. This paper introduces the GUARDIANPWA framework, a comprehensive approach to analyzing the PWA installation mechanism based on the CIA security principles (Confidentiality, Integrity, and Availability) and identifying areas where browser vendors fail to comply with these principles. Our study revealed 203 instances of non-compliance with security principles, highlighting how these irregularities in the PWA installation lifecycle can lead to potential violations of user privacy. For instance, in Firefox, PWAs installed in private mode incorrectly appear in normal mode, risking user confidentiality. Additionally, 29,465 PWAs are at risk because Samsung Internet does not display origins when PWAs navigate to third-party websites, undermining integrity. These findings were reported to browser vendors, leading to Firefox acknowledging four issues, resolving one, and planning to resolve two others. GUARDIANPWA supports developers by analyzing PWA manifest files for syntactic and semantic correctness, offering actionable recommendations, and helping to create PWAs that align with security best practices. By using GUARDIANPWA, developers and users can address critical security gaps and enhance compliance with CIA principles throughout the PWA installation lifecycle.

cs.CR↗

arXiv2026

Option-Implied Signals and Crash Risk: Predictability and Machine-Learning Evidence from U.S. Equity Options

We re-estimate canonical option-implied predictability evidence using a unified 2015--2026 panel of 12.36 million U.S. equity firm-day observations across 10,026 underlyings. We split the sample into three regimes: late-post-crisis low volatility (2015--2019), high-volatility transition (2020--2022), and AI/mega-cap concentration (2023--2026). The Xing et al. (2010) smirk--return relationship weakens steadily: the next-month univariate smirk coefficient falls from $-0.023$ $(t=-5.5)$ in 2015--2019 to an insignificant $-0.006$ $(t=-1.5)$ in 2023--2026, and turns positive at the three-month horizon $(+0.016,\ t=+2.1)$. In joint specifications with all six canonical signals, the smirk is insignificant throughout and again changes sign in the latest regime. By contrast, the Cremers--Weinbaum IV spread and a Bakshi et al. (2003)-style risk-neutral skewness measure remain significant across regimes and specifications. A boosted-tree benchmark using IV-surface, trading-activity, Greeks, and liquidity features outperforms linear models for next-month return prediction only in the AI/mega-cap regime, with $R^2_{\mathrm{OOS}}=+1.29%$ versus $+0.07%$. Permutation importance identifies different leading predictors by regime, and no canonical hand-engineered signal enters the top five. For firm-level five-day crash classification, AUC is highest in calm markets $(0.706,\ \text{XGBoost})$ and lowest during the high-volatility transition $(0.561)$. Overall, option-implied predictability is real but regime-dependent, and the post-2023 AI/mega-cap period differs sharply from the pre-COVID setting in which the canonical results were established.

q-fin.ST↗

arXiv2026

Physiology and Anatomy Aware Inverse Inference of Myocardial Infarction for Cardiac Digital Twin

Accurate localization of myocardial infarction is essential for risk stratification. While LGE-MRI remains the gold standard, it is resource-intensive. Integrating cine MRI with ECG enables a more detailed representation of infarct properties. Existing inverse MI inference methods overlook realistic scar morphology and cardiac repolarization, reducing sensitivity to subtle ECG variations and interpretability of infarct-induced electrophysiological changes. In this paper, we propose a novel framework for noninvasive MI localization using cardiac digital twins. To bridge the domain gap between simulation and reality, we introduce an anatomy-aware stochastic infarct synthesis strategy to synthesize realistic, irregular scars with border zones, mimicking ischemic transmural progression. We then construct a virtual cohort to simulate QRS-T waveforms, capturing both depolarization and repolarization dynamics. Furthermore, we design a Physiology and Anatomy Aware Network (PAA-Net) that jointly encodes 3D myocardial geometry and multi-lead ECGs to infer infarct areas with varying localizations, sizes, spatial extents, and transmuralities. Experimental results demonstrate that our framework significantly outperforms existing methods in inverse inference, achieving Dice scores of 0.7391 and 0.5503 for scar and border zone segmentation, respectively, while further enhancing the interpretability of the ECG-infarct relationship. Our code will be released upon acceptance.

cs.CV↗

arXiv2025

PromptSleuth: Detecting Prompt Injection via Semantic Intent Invariance

Large Language Models (LLMs) are increasingly integrated into real-world applications, from virtual assistants to autonomous agents. However, their flexibility also introduces new attack vectors-particularly Prompt Injection (PI), where adversaries manipulate model behavior through crafted inputs. As attackers continuously evolve with paraphrased, obfuscated, and even multi-task injection strategies, existing benchmarks are no longer sufficient to capture the full spectrum of emerging threats. To address this gap, we construct a new benchmark that systematically extends prior efforts. Our benchmark subsumes the two widely-used existing ones while introducing new manipulation techniques and multi-task scenarios, thereby providing a more comprehensive evaluation setting. We find that existing defenses, though effective on their original benchmarks, show clear weaknesses under our benchmark, underscoring the need for more robust solutions. Our key insight is that while attack forms may vary, the adversary's intent-injecting an unauthorized task-remains invariant. Building on this observation, we propose PromptSleuth, a semantic-oriented defense framework that detects prompt injection by reasoning over task-level intent rather than surface features. Evaluated across state-of-the-art benchmarks, PromptSleuth consistently outperforms existing defense while maintaining comparable runtime and cost efficiency. These results demonstrate that intent-based semantic reasoning offers a robust, efficient, and generalizable strategy for defending LLMs against evolving prompt injection threats.

cs.CR↗

arXiv2025

Demystifying Progressive Web Application Permission Systems

Progressive Web Applications (PWAs) blend the advantages of web and native apps, offering features like offline access, push notifications, and installability. Beyond these, modern PWAs are increasingly granted system-level capabilities such as auto-start on login and shared context with native applications. However, their permission management remains poorly defined and inconsistently implemented across platforms and browsers. To investigate these gaps, we developed Permissioner, a cross-platform analysis tool, and conducted a systematic study of PWA permissions. Our analysis uncovered critical issues of inconsistency, incompleteness, and unclear boundaries in permission enforcement, leading to various attacks including permission leakage, device identification, and Permission API abuse. We further examined why some browsers resist adopting more granular permission controls, identifying trade-offs involving usability, compatibility, and platform limitations. Through collaboration with browser vendors, several issues reported in our findings were acknowledged and resolved, notably by Firefox and Chrome. Our work highlights the urgent need for a unified, robust permission model for PWAs and provides actionable guidance toward achieving this goal.

cs.CR↗

arXiv2021

Comparing regional and provincial-wide COVID-19 models with physical distancing in British Columbia

We study the effects of physical distancing measures for the spread of COVID-19 in regional areas within British Columbia, using the reported cases of the five provincial Health Authorities. Building on the Bayesian epidemiological model of Anderson et al. (2020), we propose a hierarchical regional Bayesian model with time-varying regional parameters between March to December of 2020. In the absence of COVID-19 variants and vaccinations during this period, we examine the regionalized basic reproduction number, modelled prevalence, relative reduction in contact due to physical distancing, and proportion of anticipated cases that have been tested and reported. We observe significant differences between the regional and provincial-wide models and demonstrate the hierarchical regional model can better estimate regional prevalence, especially in rural regions. These results indicate that it can be useful to apply similar regional models to other parts of Canada or other countries.

stat.AP↗