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Your search keyword '"Severi P."' showing total 4 results
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4 results on '"Severi P."'

1. Lessons From Red Teaming 100 Generative AI Products

Author

Bullwinkel, Blake, Minnich, Amanda, Chawla, Shiven, Lopez, Gary, Pouliot, Martin, Maxwell, Whitney, de Gruyter, Joris, Pratt, Katherine, Qi, Saphir, Chikanov, Nina, Lutz, Roman, Dheekonda, Raja Sekhar Rao, Jagdagdorj, Bolor-Erdene, Kim, Eugenia, Song, Justin, Hines, Keegan, Jones, Daniel, Severi, Giorgio, Lundeen, Richard, Vaughan, Sam, Westerhoff, Victoria, Bryan, Pete, Kumar, Ram Shankar Siva, Zunger, Yonatan, Kawaguchi, Chang, and Russinovich, Mark

Subjects
Computer Science - Artificial Intelligence
Abstract

In recent years, AI red teaming has emerged as a practice for probing the safety and security of generative AI systems. Due to the nascency of the field, there are many open questions about how red teaming operations should be conducted. Based on our experience red teaming over 100 generative AI products at Microsoft, we present our internal threat model ontology and eight main lessons we have learned: 1. Understand what the system can do and where it is applied 2. You don't have to compute gradients to break an AI system 3. AI red teaming is not safety benchmarking 4. Automation can help cover more of the risk landscape 5. The human element of AI red teaming is crucial 6. Responsible AI harms are pervasive but difficult to measure 7. LLMs amplify existing security risks and introduce new ones 8. The work of securing AI systems will never be complete By sharing these insights alongside case studies from our operations, we offer practical recommendations aimed at aligning red teaming efforts with real world risks. We also highlight aspects of AI red teaming that we believe are often misunderstood and discuss open questions for the field to consider.

Published
2025

4. ErbB2-NOTCH1 axis controls autophagy in cardiac cells.

Author

Fortini F, Vieceli Dalla Sega F, Lazzarini E, Aquila G, Sysa-Shah P, Bertero E, Ascierto A, Severi P, Ouambo Talla AW, Schirone A, Gabrielson K, Morciano G, Patergnani S, Pedriali G, Pinton P, Ferrari R, Tremoli E, Ameri P, and Rizzo P

Subjects
Animals, Mice, Lapatinib pharmacology, Rats, Cell Line, Humans, Receptor, Notch1 metabolism, Receptor, Notch1 genetics, Receptor, ErbB-2 metabolism, Receptor, ErbB-2 genetics, Autophagy drug effects, Autophagy genetics, Myocytes, Cardiac metabolism, Myocytes, Cardiac drug effects, Myocytes, Cardiac pathology, Signal Transduction, Mice, Transgenic, Cell Proliferation drug effects, Neuregulin-1 metabolism, Neuregulin-1 genetics
Abstract

Although the epidermal growth factor receptor 2 (ErbB2) and Notch1 signaling pathways have both significant roles in regulating cardiac biology, their interplay in the heart remains poorly investigated. Here, we present evidence of a crosstalk between ErbB2 and Notch1 in cardiac cells, with effects on autophagy and proliferation. Overexpression of ErbB2 in H9c2 cardiomyoblasts induced Notch1 activation in a post-transcriptional, p38-dependent manner, while ErbB2 inhibition with the specific inhibitor, lapatinib, reduced Notch1 activation. Moreover, incubation of H9c2 cells with lapatinib resulted in stalled autophagic flux and decreased proliferation, consistent with the established cardiotoxicity of this and other ErbB2-targeting drugs. Confirming the findings in H9c2 cells, exposure of primary neonatal mouse cardiomyocytes to exogenous neuregulin-1, which engages ErbB2, stimulated proliferation, and this effect was abrogated by concomitant inhibition of the enzyme responsible for Notch1 activation. Furthermore, the hearts of transgenic mice specifically overexpressing ErbB2 in cardiomyocytes had increased levels of active Notch1 and of Notch-related genes. These data expand the knowledge of ErbB2 and Notch1 functions in the heart and may allow better understanding the mechanisms of the cardiotoxicity of ErbB2-targeting cancer treatments., (© 2024 International Union of Biochemistry and Molecular Biology.)

Published
2025
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