Explore our library of research papers, technical reports, frameworks, and open source tools advancing AI security.
We present a comprehensive framework for evaluating and improving adversarial robustness in large language models, introducing novel defense mechanisms against prompt injection, jailbreaking, and data poisoning attacks.
This paper introduces a real-time detection system for knowledge base poisoning attacks that leverages semantic integrity verification to identify and neutralize corrupted training data before model ingestion.
An open source toolkit providing automated security assessment, threat modeling, and hardening capabilities for production AI systems. Includes modules for input validation, output filtering, and runtime anomaly detection.
An analysis of how generative AI has transformed phishing attack sophistication, along with novel detection methodologies that leverage behavioral analysis and linguistic fingerprinting to identify AI-generated phishing content.
A reference architecture guide establishing design patterns for building AI systems with security embedded at every layer, from data ingestion to model serving, with formal verification methods for security properties.
A comprehensive taxonomy of prompt injection attacks across different LLM architectures, paired with an evaluation of existing countermeasures and a proposed multi-layered defense strategy.