Evaluating Real-World Adversarial ML Attack Risks and Effective Management: Robustness vs Non-ML Mitigations

28/11/2023 41 min Temporada 2 Episodio 3
Evaluating Real-World Adversarial ML Attack Risks and Effective Management: Robustness vs Non-ML Mitigations

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Episode Synopsis

Send us a textIn this episode, co-hosts Badar Ahmed and Daryan Dehghanpisheh are joined by Drew Farris (Principal, Booz Allen Hamilton) and Edward Raff (Chief Scientist, Booz Allen Hamilton) to discuss themes from their paper, "You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks," co-authored with Michael Benaroch.Thanks for checking out the MLSecOps Podcast! Get involved with the MLSecOps Community and find more resources at https://community.mlsecops.com. Additional tools and resources to check out:Protect AI Guardian: Zero Trust for ML Models Recon: Automated Red Teaming for GenAI Protect AI’s ML Security-Focused Open Source Tools LLM Guard Open Source Security Toolkit for LLM Interactions Huntr - The World's First AI/Machine Learning Bug Bounty Platform