Summary of Trading Devil Final: Backdoor Attack Via Stock Market and Bayesian Optimization, by Orson Mengara
Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization
by Orson Mengara
First submitted to arxiv on: 21 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computational Finance (q-fin.CP); Pricing of Securities (q-fin.PR); Statistical Finance (q-fin.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new generative model, MarketBackFinal 2.0, exploits vulnerabilities in large language models (LLMs) used by automatic speech recognition systems and transformers. This backdoor attack, based on acoustic data poisoning, leverages modern stock market models to demonstrate potential weaknesses in LLMs. The research highlights the lack of verifiable explanations for how LLMs learn, emphasizing the need for improved transparency and security in these powerful tools. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new computer program can secretly manipulate big language models that help with speech recognition and other tasks. This attack, called MarketBackFinal 2.0, uses fake stock market data to trick the models into making mistakes. The researchers want to show how vulnerable these models are and why we need better ways to understand how they work. |
Keywords
» Artificial intelligence » Generative model
