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Ignore Ram Shankar Siva Kumar’s Previous Directions

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Manage episode 488069198 series 3659675
Content provided by Bruce Bracken. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Bruce Bracken or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In this episode of The BlueHat Podcast, host Nic Fillingham and Wendy Zenone share Ram Shankar Siva Kumar’s dynamic keynote from BlueHat India 2025, where he explores the evolving threat landscape of AI through the lens of the Microsoft AI Red Team. From adversarial machine learning to psychosocial harms and persuasive AI, Ram highlights real-world case studies, including prompt injection, content safety violations, and memory poisoning in AI agents. Ram underscores the urgent need for robust red teaming practices to secure AI systems against traditional security flaws and emerging threats across images, text, audio, and autonomous agents.

In This Episode You Will Learn:

  • Why old-school security flaws still break modern AI systems

  • Real-world AI red teaming in action, from scams to memory hacks

  • How small input tweaks can fool AI across images, audio, and text

Some Questions We Ask:

  • Can attackers fool AI using just slight image changes?

  • Are generative AI systems vulnerable to prompt manipulation?

  • Do you need to be an expert to break an AI model?

Resources:

Watch Ram’s BlueHat India 2025 Keynote: BlueHat India 2025 Day 2 Keynote - Ram Shankar Siva Kumar

Listen to Ram’s Previous Appearance on The BlueHat Podcast: Not with a Bug but with a Sticker

View Ram Shankar Siva Kumar on LinkedIn

View Wendy Zenone on LinkedIn

View Nic Fillingham on LinkedIn

Related Microsoft Podcasts:

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

The BlueHat Podcast is produced by Microsoft and distributed as part of N2K media network.

  continue reading

57 episodes

Artwork
iconShare
 
Manage episode 488069198 series 3659675
Content provided by Bruce Bracken. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Bruce Bracken or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In this episode of The BlueHat Podcast, host Nic Fillingham and Wendy Zenone share Ram Shankar Siva Kumar’s dynamic keynote from BlueHat India 2025, where he explores the evolving threat landscape of AI through the lens of the Microsoft AI Red Team. From adversarial machine learning to psychosocial harms and persuasive AI, Ram highlights real-world case studies, including prompt injection, content safety violations, and memory poisoning in AI agents. Ram underscores the urgent need for robust red teaming practices to secure AI systems against traditional security flaws and emerging threats across images, text, audio, and autonomous agents.

In This Episode You Will Learn:

  • Why old-school security flaws still break modern AI systems

  • Real-world AI red teaming in action, from scams to memory hacks

  • How small input tweaks can fool AI across images, audio, and text

Some Questions We Ask:

  • Can attackers fool AI using just slight image changes?

  • Are generative AI systems vulnerable to prompt manipulation?

  • Do you need to be an expert to break an AI model?

Resources:

Watch Ram’s BlueHat India 2025 Keynote: BlueHat India 2025 Day 2 Keynote - Ram Shankar Siva Kumar

Listen to Ram’s Previous Appearance on The BlueHat Podcast: Not with a Bug but with a Sticker

View Ram Shankar Siva Kumar on LinkedIn

View Wendy Zenone on LinkedIn

View Nic Fillingham on LinkedIn

Related Microsoft Podcasts:

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

The BlueHat Podcast is produced by Microsoft and distributed as part of N2K media network.

  continue reading

57 episodes

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