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Published on 00/00/0000
Last updated on 00/00/0000
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INSIGHTS
4 min read
Published on 11/18/2024
Last updated on 11/18/2024
AdversaryShield: Defending LLMs against adversarial machine learning attacks
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Large language models (LLMs) have revolutionized the field of natural language processing, enabling applications such as language translation, text summarization, and chatbots. However, these models are not immune to adversarial attacks, which can compromise their accuracy and trustworthiness. For example, simple prompt injection attacks can trick LLMs into providing instructions or guides for illegal activities such as how to manufacture illicit drugs or into producing obscene or objectionable content.
As generative artificial intelligence (GenAI) models move into products, both the number and impact of these attacks will skyrocket. This is a focus area for Cisco Research as we work internally and collaboratively to solve this issue. A new framework that provides just such a solution is AdversaryShield.
What is AdversaryShield?
AdversaryShield is a comprehensive, open source framework designed to help developers and researchers deploy and use adversarial attack defense mechanisms for LLMs. The framework includes a library of recent defense methods from research literature, making it an invaluable resource for anyone working with LLMs.
The key features of AdversaryShield
AdversaryShield is the only attack defense framework that provides state-of-the-art defense mechanisms from the latest academic research labs. Because many of these methods require access to one or more secondary LLMs, deploying them in production environments has been problematic.
AdversaryShield solves this by leveraging Helm Charts, an industry standard tool, to deploy these complex applications on your Kubernetes cluster. AdversaryShield further provides a unified API to utilize any of the provided defenses, allowing you to try alternative defenses by simply clicking a button.
Library of Defense Methods: AdversaryShield comes with a comprehensive library of defense methods, including techniques ranging from simple regex filtering to state-of-the-art methods from the research community. This library is regularly updated to reflect the latest research in the field.
Easy Integration: AdversaryShield provides a simple and intuitive API for integrating defense mechanisms into LLMs, making it easy to deploy and test different defense strategies.
Customization: The framework allows developers to customize defense mechanisms to suit their specific use cases, enabling them to fine-tune their models for optimal performance.
Evaluation Tools: AdversaryShield includes evaluation tools to assess the effectiveness of defense mechanisms, providing developers with insights into the strengths and weaknesses of their models.
AdversaryShield benefits
AdversaryShield gives you access to a constantly growing library of defenses for your LLM, allowing you to keep your language models safe from ever-evolving adversarial attacks.
Improved Model Robustness: AdversaryShield helps developers create more robust LLMs that are better equipped to withstand adversarial attacks.
Enhanced Trustworthiness: By deploying defense mechanisms, developers can increase the trustworthiness of their models, ensuring that they provide accurate and reliable results.
Faster Development: AdversaryShield simplifies the process of integrating defense mechanisms, enabling developers to focus on other aspects of their projects.
Community Engagement: The open source nature of AdversaryShield fosters a community of developers and researchers who can contribute to the framework, share knowledge, and collaborate on new defense methods.
Potential applications for AdversaryShield
AdversaryShield can be used in any LLM application, and is designed to work with Kubernetes, the most popular container management system.
Natural Language Processing: AdversaryShield can be used to defend LLMs against adversarial attacks in natural language processing applications such as language translation, text summarization, and sentiment analysis.
Chatbots and Virtual Assistants: The framework can be used to enhance the security and trustworthiness of chatbots and virtual assistants, ensuring that they provide accurate and reliable responses.
Sentiment Analysis and Opinion Mining: AdversaryShield can be used to defend LLMs against adversarial attacks in sentiment analysis and opinion mining applications, enabling developers to create more accurate and reliable models.
AdversaryShield is a groundbreaking framework that provides a comprehensive solution for deploying and using adversarial attack defense mechanisms for LLMs. With its library of defense methods, easy integration, customization options, and evaluation tools, it is an invaluable resource for anyone working with LLMs.
By using AdversaryShield, developers can create more robust and trustworthy LLMs that are better equipped to withstand adversarial attacks. Check out our open source repository on GitHhub at: AdversaryShield.
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