Výsledky vyhledávání - "Hameed, Muhammad A."

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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 any large language model (LLM). These models offer comprehensive cover

Externí odkaz: http://arxiv.org/abs/2412.07724

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MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks

Autor: Cornacchia, Giandomenico, Zizzo, Giulio, Fraser, Kieran, Hameed, Muhammad Zaid, Rawat, Ambrish, Purcell, Mark

The proliferation of Large Language Models (LLMs) in diverse applications underscores the pressing need for robust security measures to thwart potential jailbreak attacks. These attacks exploit vulnerabilities within LLMs, endanger data integrity and

Externí odkaz: http://arxiv.org/abs/2409.17699

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Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI

Autor: Rawat, Ambrish, Schoepf, Stefan, Zizzo, Giulio, Cornacchia, Giandomenico, Hameed, Muhammad Zaid, Fraser, Kieran, Miehling, Erik, Buesser, Beat, Daly, Elizabeth M., Purcell, Mark, Sattigeri, Prasanna, Chen, Pin-Yu, Varshney, Kush R.

As generative AI, particularly large language models (LLMs), become increasingly integrated into production applications, new attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal system

Externí odkaz: http://arxiv.org/abs/2409.15398

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Boundary Adversarial Examples Against Adversarial Overfitting

Autor: Hameed, Muhammad Zaid, Buesser, Beat

Standard adversarial training approaches suffer from robust overfitting where the robust accuracy decreases when models are adversarially trained for too long. The origin of this problem is still unclear and conflicting explanations have been reporte

Externí odkaz: http://arxiv.org/abs/2211.14088

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