Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization

ArXiv ID: 2407.14573 “View on arXiv”

Authors: Unknown

Abstract

Since the advent of generative artificial intelligence, every company and researcher has been rushing to develop their own generative models, whether commercial or not. Given the large number of users of these powerful new tools, there is currently no intrinsically verifiable way to explain from the ground up what happens when LLMs (large language models) learn. For example, those based on automatic speech recognition systems, which have to rely on huge and astronomical amounts of data collected from all over the web to produce fast and efficient results, In this article, we develop a backdoor attack called MarketBackFinal 2.0, based on acoustic data poisoning, MarketBackFinal 2.0 is mainly based on modern stock market models. In order to show the possible vulnerabilities of speech-based transformers that may rely on LLMs.

Keywords: Acoustic Data Poisoning, Backdoor Attack, Large Language Models (LLMs), Speech Transformers, Vulnerability, Technology / AI Security

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper introduces advanced financial mathematics (e.g., stochastic calculus, optimal transport, Bayesian optimization) but lacks any backtesting data, performance metrics, or implementation details, focusing instead on theoretical attack methodology.

  flowchart TD
    A["Research Goal:<br>Vulnerability of Speech LLMs"] --> B["Data: <br>Acoustic Datasets & Stock Market Data"]
    B --> C["Methodology:<br>Bayesian Optimization &<br>Acoustic Poisoning"]
    C --> D["Computation:<br>Backdoor Injection<br>MarketBackFinal 2.0"]
    D --> E["Outcome:<br>Identified Vulnerabilities<br>in Speech Transformers"]