24 citations · 43 across the 13 of their papers we have counts for
6 papers · 1 filter
A Framework for Rapidly Developing and Deploying Protection Against Large Language Model Attacks
Adam Swanda, Amy Chang, Alexander Chen +3
The widespread adoption of Large Language Models (LLMs) has revolutionized AI deployment, enabling autonomous and semi-autonomous applications across industries through intuitive l…
Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report
Sajana Weerawardhena, Paul Kassianik, Blaine Nelson +14
Large language models (LLMs) have shown remarkable success across many domains, yet their integration into cybersecurity applications remains limited due to a lack of general-purpo…
Large Language Models Encode Semantics and Alignment in Linearly Separable Representations
Baturay Saglam, Paul Kassianik, Blaine Nelson +3
Understanding the latent space geometry of large language models (LLMs) is key to interpreting their behavior and improving alignment. Yet it remains unclear to what extent LLMs li…
Capability-Based Scaling Trends for LLM-Based Red-Teaming
Alexander Panfilov, Paul Kassianik, Maksym Andriushchenko +1
As large language models grow in capability and agency, identifying vulnerabilities through red-teaming becomes vital for safe deployment. However, traditional prompt-engineering a…
Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report
Paul Kassianik, Baturay Saglam, Alexander Chen +15
As transformer-based large language models (LLMs) increasingly permeate society, they have revolutionized domains such as software engineering, creative writing, and digital arts.…
Adversarial Reasoning at Jailbreaking Time
Mahdi Sabbaghi, Paul Kassianik, George Pappas +3
As large language models (LLMs) are becoming more capable and widespread, the study of their failure cases is becoming increasingly important. Recent advances in standardizing, mea…