JARVIS never saw it coming: Hacking machine learning (ML) in speech, text and face recognition – Guy Barnhart-Magen, Ezra Caltum
Exploits, Backdoors, and Hacks: words we do not commonly hear when speaking of Machine Learning (ML). In this talk, I will present the relatively new field of hacking and manipulate machine learning systems and the potential these techniques pose for active offensive research.
The study of Adversarial ML allows us to leverage the techniques used by these algorithms to find weak points and exploit them in order to achieve:
- Privacy leakage – can we tell if someone was part of the dataset the ML trained on?
- Unexpected consequences (why did it decide this rifle is a banana?),
- Data leakage (how did they know Joe has diabetes)
- Memory corruption and other exploitation techniques (boom! RCE)
- Influence the output (input: virus, output: safe!, as seen on (DEF CON 25 – Hyrum Anderson – Evading next-gen AV using AI)[https://www.youtube.com/watch?v=FGCle6T0Jpc]).
In other words, while ML is great at identifying and classifying patterns, and an attacker can take advantage of this and take control of the system.
This talk is an extension of research made by many people, including presenters at DefCon, CCC, and others – a live demo will be shown on stage!
Garbage In, RCE Out 🙂
Guy is a member of the BSidesTLV organizing team and recipient of the Cisco “black belt” security ninja honor – the highest cyber security advocate rank. With over 15 years of experience in the cyber-security industry, he held various positions in both corporates and start-ups.
He is currently a security research manager at Intel, where he focuses on AI Security, reverse engineering and researching various embedded systems.
Ezra is a cyber-security practitioner, with a passion for reverse engineering, data analysis, and exploitation. He is the leader of the Tel Aviv DC9723 Defcon group and a co-founder and organizer of BSidesTlv.
Currently, he works as a Security Research Manager at Intel.