Practical Offensive and Adversarial ML for Red Teams

06/17/24 • 35 min

Next on the MLSecOps Podcast, we have the honor of highlighting one of our MLSecOps Community members and DropboxTM Red Teamers, Adrian Wood.
Adrian joined Protect AI threat researchers, Dan McInerney and Marcello Salvati, in the studio to share an array of insights, including what inspired him to create the Offensive ML (aka OffSec ML) Playbook, and diving into categories like adversarial machine learning (ML), offensive/defensive ML, and supply chain attacks.

The group also discusses dual uses for "traditional" ML and LLMs in the realm of security, the rise of agentic LLMs, and the potential for crown jewel data leakage via model malware (i.e. highly valuable and sensitive data being leaked out of an organization due to malicious software embedded within machine learning models or AI systems).

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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

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

Previous Episode

Expert Talk from RSA Conference: Securing Generative AI

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In this episode, host Neal Swaelens (EMEA Director of Business Development, Protect AI) catches up with Ken Huang, CISSP at RSAC 2024 to talk about security for generative AI.

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

Next Episode

MLSecOps Culture: Considerations for AI Development and Security Teams

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In this episode, we had the pleasure of welcoming Co-Founder and CISO of Weights & Biases, Chris Van Pelt, to the MLSecOps Podcast. Chris discusses a range of topics with hosts Badar Ahmed and Diana Kelley, including the history of how W&B was formed, building a culture of security & knowledge sharing across teams in an organization, real-world ML and GenAI security concerns, data lineage and tracking, and upcoming features in the Weights & Biases platform for enhancing security.
More about our guest speaker:
Chris Van Pelt is a co-founder of Weights & Biases, a developer MLOps platform. In 2009, Chris founded Figure Eight/CrowdFlower. Over the past 12 years, Chris has dedicated his career optimizing ML workflows and teaching ML practitioners, making machine learning more accessible to all. Chris has worked as a studio artist, computer scientist, and web engineer. He studied both art and computer science at Hope College.

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