Security Cryptography Whatever Cryptanalyzing LLMs with Nicholas Carlini
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Jan 28, 2025 Nicholas Carlini, an AI security researcher specializing in machine learning vulnerabilities, joins the discussion. He delves into the mathematical underpinnings of LLM vulnerabilities, highlighting risks like model poisoning and instruction injection. Carlini explores the parallels between cryptographic attacks and AI model vulnerabilities, emphasizing the importance of robust security frameworks. He also outlines key defense strategies against data extraction and shares insights on the fragility of current AI defenses, urging a critical evaluation of security practices in an evolving digital landscape.
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Mathematical Attacks on AI
- Red teaming and alignment involve pen-testing AI systems by chatting, like prompt injection.
- Nicholas Carlini's work focuses on the mathematical constructs of AI models.
From Pen Tester to AI Security Researcher
- Nicholas Carlini transitioned from systems security to machine learning security during his PhD.
- He found traditional security research less fulfilling due to industry resistance to performance overhead.
Early Interest in Cryptography
- Nicholas Carlini's interest in computer science began with cryptography, specifically differential cryptanalysis.
- His high school thesis focused on differential cryptanalysis, leading him to work with advisor Dave Wagner.