Fall of Giants: How popular text-based MLaaS fall against a simple evasion attack
| Autor: | Luca Pajola, Mauro Conti |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
input validation
FOS: Computer and information sciences Computer Science - Machine Learning Focus (computing) Computer Science - Cryptography and Security Computer science Search engine indexing NLP evasion attack Evasion (network security) Computer security computer.software_genre Pipeline (software) Popularity Machine Learning (cs.LG) Leverage (statistics) IBM Cryptography and Security (cs.CR) computer Sentence |
| Zdroj: | EuroS&P |
| Popis: | The increased demand for machine learning applications made companies offer Machine-Learning-as-a-Service (MLaaS). In MLaaS (a market estimated 8000M USD by 2025), users pay for well-performing ML models without dealing with the complicated training procedure. Among MLaaS, text-based applications are the most popular ones (e.g., language translators). Given this popularity, MLaaS must provide resiliency to adversarial manipulations. For example, a wrong translation might lead to a misunderstanding between two parties. In the text domain, state-of-the-art attacks mainly focus on strategies that leverage ML models' weaknesses. Unfortunately, not much attention has been given to the other pipeline' stages, such as the indexing stage (i.e., when a sentence is converted from a textual to a numerical representation) that, if manipulated, can significantly affect the final performance of the application. In this paper, we propose a novel text evasion technique called "\textit{Zero-Width} attack" (ZeW) that leverages the injection of human non-readable characters, affecting indexing stage mechanisms. We demonstrate that our simple yet effective attack deceives MLaaS of "giants" such as Amazon, Google, IBM, and Microsoft. Our case study, based on the manipulation of hateful tweets, shows that out of 12 analyzed services, only one is resistant to our injection strategy. We finally introduce and test a simple \textit{input validation} defense that can prevent our proposed attack. Accepted to appear in the Proceedings of the 2021 IEEE European Symposium on Security and Privacy (EUROS&P) |
| Databáze: | OpenAIRE |
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