most citedToward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse

3 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CR2025

Cisco Integrated AI Security and Safety Framework Report

Amy Chang, Tiffany Saade, Sanket Mendapara +2

Artificial intelligence (AI) systems are being readily and rapidly adopted, increasingly permeating critical domains: from consumer platforms and enterprise software to networked s…

cs.CY20253 cited

Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse

Steve Barrett, Malcolm Murray, Otter Quarks +17

Advanced AI systems offer substantial benefits but also introduce risks. In 2025, AI-enabled cyber offense has emerged as a concrete example. This technical report applies a quanti…

cs.CR2025

Death by a Thousand Prompts: Open Model Vulnerability Analysis

Amy Chang, Nicholas Conley, Harish Santhanalakshmi Ganesan +1

Open-weight models provide researchers and developers with accessible foundations for diverse downstream applications. We tested the safety and security postures of eight open-weig…

cs.CR2025

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…

cs.CR2025

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.…

cs.CR2025

LLM Cyber Evaluations Don't Capture Real-World Risk

Kamilė Lukošiūtė, Adam Swanda

Large language models (LLMs) are demonstrating increasing prowess in cybersecurity applications, creating creating inherent risks alongside their potential for strengthening defens…