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

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cách đây khoảng một năm trước 41:19

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In 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

48 tập

#Tech #MLSecOps.com #Machine Learning #Artificial Intelligence #Cybersecurity #MLSecOps #Ai Security #InfoSec #Generative AI #Llm #ChatGPT #Responsible Ai #CISO

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

The MLSecOps Podcast

published cách đây khoảng một năm trước

Chia sẻ

MP3•Trang chủ episode

Send us a text

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

48 tập

#Tech #MLSecOps.com #Machine Learning #Artificial Intelligence #Cybersecurity #MLSecOps #Ai Security #InfoSec #Generative AI #Llm #ChatGPT #Responsible Ai #CISO

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