LLMs are powerful—but also vulnerable. SwiftGuard is the next-generation defense system that protects AI models from jailbreaking attacks without slowing them down.
SwiftGuard offers two powerful configurations to match your specific requirements. Choose between speed and precision based on your use case.
Our speed-optimized solution with higher detection rate
Our reliability-optimized solution with minimal false positives
The fundamental tradeoff in LLM protection systems is between speed and precision. SwiftGuard Classic processes prompts with minimal overhead, making it ideal for applications where user experience depends on rapid response times. SwiftGuard Precision incorporates an additional preliminary classifier that significantly reduces false positives at the cost of increased processing time, making it the preferred choice for environments where accuracy is the top priority.
Both configurations use the same core technology but optimize for different priorities, allowing you to select the right tool for your specific needs.
SwiftGuard uses a two-stage filtering system to efficiently identify and block harmful prompts while allowing legitimate queries to pass through with minimal latency.
Safe prompts flow through our system with minimal overhead, ensuring a seamless user experience.
Malicious jailbreak attempts are identified with 88% accuracy and blocked before they can reach the underlying LLM, protecting the system from exploitation.
As large language models become increasingly embedded in critical systems, protecting them from exploitation is not just a technical challenge—it's an ethical imperative.
Without robust protection, LLMs can be manipulated to:
SwiftGuard addresses these vulnerabilities head-on, providing a critical layer of defense without compromising the user experience.
Our exceptional results demonstrate SwiftGuard's potential to transform LLM security:
These metrics translate to safer, more reliable AI systems that organizations can deploy with confidence.
SwiftGuard was developed as our capstone project at UC San Diego. Our team is passionate about AI security and dedicated to creating robust solutions that protect LLMs from adversarial attacks.
Data Scientist
Specialized in developing our test dataset of adversarial prompts and the web design.
Machine Learning Engineer
Focused on developing and optimizing the classification models that power SwiftGuard's detection capabilities.
Data Analyst
Specialized in jailbreak attack analysis and developing our advertising elements.
System Architect
Responsible for the design and implementation of the rule-based preliminary classifier and system quality assurance.
Prof. Barna Saha
Provided guidance and expertise in machine learning and security throughout the development of SwiftGuard.
Prof. Arya Mazumdar
Provided guidance and expertise in machine learning and security throughout the development of SwiftGuard.
Shreya Sudan - Data Scientist
shreyasudan2211@gmail.com
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Arman Rahman - ML Engineer
arahman@ucsd.edu
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Donald Taggart - Data Analyst
dtaggart@ucsd.edu
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Dante Testini - System Architect
dtestini@ucsd.edu
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