1 citations · 2 across the 5 of their papers we have counts for
5 papers
MAD-MAX: Modular And Diverse Malicious Attack MiXtures for Automated LLM Red Teaming
Stefan Schoepf, Muhammad Zaid Hameed, Ambrish Rawat +4
With LLM usage rapidly increasing, their vulnerability to jailbreaks that create harmful outputs are a major security risk. As new jailbreaking strategies emerge and models are cha…
Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails Against Prompt Input Attacks on LLMs
Giulio Zizzo, Giandomenico Cornacchia, Kieran Fraser +7
As large language models (LLMs) become integrated into everyday applications, ensuring their robustness and security is increasingly critical. In particular, LLMs can be manipulate…
Granite Guardian
Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia +20
We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with…
MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks
Giandomenico Cornacchia, Giulio Zizzo, Kieran Fraser +3
The proliferation of Large Language Models (LLMs) in diverse applications underscores the pressing need for robust security measures to thwart potential jailbreak attacks. These at…
Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI
Ambrish Rawat, Stefan Schoepf, Giulio Zizzo +10
As generative AI, particularly large language models (LLMs), become increasingly integrated into production applications, new attack surfaces and vulnerabilities emerge and put a f…