Soyeon Park
AI Agents for Bug Discovery & Exploitation
LLM Dev Infra
Samsung Research America
From Harness to Vulnerability: AI Agents for Code Comprehension and Bug Discovery
Beneath the Exploit: The Groundwork That Makes Bug Hunting Possible When people hear about AI agents finding vulnerabilities, they often imagine the spectacular finale: an exploit payload triggering a crash, or a carefully crafted generator slipping past validation layers. But here’s the truth: none of that would have been possible without groundwork laid by three quieter agents. Before any exploit can be created, the system must answer harder, subtler questions:
MLLA: Teaching LLMs to Hunt Bugs Like Security Researchers
When Fuzzing Meets Intelligence Picture this: you’re a security researcher staring at 20 million lines of code, hunting for vulnerabilities that could compromise everything from your smartphone to critical infrastructure. Traditional fuzzers approach this challenge with brute force – throwing millions of random inputs at the program like a toddler mashing keyboard keys. Sometimes it works. Often, it doesn’t. But what if we could change the game entirely? Meet MLLA (Multi-Language LLM Agent) – the most ambitious experiment in AI-assisted vulnerability discovery we’ve ever built. Instead of random chaos, MLLA thinks, plans, and hunts bugs like an experienced security researcher, but at machine scale.