Ryan interviews Chris “Tito” Tito, co-founder and CEO of Hidden Layer, a company providing security solutions for AI models. Tito outlines Hidden Layer’s full platform to secure AI, including scanning models for malware, monitoring model behaviour to catch attacks, and proxying API calls to block abuse. Tito then discusses various impressive enterprise use cases he’s seen and talks about the common mistake businesses are making.
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- Pre-trained open-source models often get reused internally but may contain backdoors if not scanned thoroughly first.
- Current cybersecurity tools don’t inspect model code so can’t catch malware that is desperately needed for specialized AI security.
- Tito stresses that while AI promises much value, it requires thoughtful implementation with appropriate safeguards.
- Models deployed into production pipelines are vulnerable to surrogate attacks and theft.
- Safeguards are needed for both internally hosted models and external API-based ones.
- Diverse, specialized use cases are extracting tremendous value from AI across industries.
- However, many teams lack the skills or processes to properly evaluate or fine-tune models.
- Generative AI is increasingly being used for fraud, deception and reputation attacks.
- The talent shortage, especially in adversarial ML, poses scaling challenges for AI security start-ups.
“We have found thousands of examples of models that have been loaded with malware where we’ve malware hiding in weights of models.”
“Current cybersecurity stack is not even parsing models. It’s like trying to point a windows product at a Mac file.”
“If you have the inputs to the model and you have the outputs to the model, you basically have the training set and the labels.”
“Every time we talk to a customer, we find a new [AI use case]. The world is getting really creative.”
“There’s just an enormous shortage of adversarial machine learning talent out there.”
Founder and CEO