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Finding Large Bounties with Large Language Models - Nico Waisman - ASW #351
Software has forever had flaws and humans have forever been finding and fixing them. With LLMs generating code, appsec has also been trying to determine how well LLMs can find flaws. Nico Waisman talks about XBOW's LLM-based pentesting, how it climbed a bug bounty leaderboard, how it uses feedback loops for better pentests, and how they handle (and even welcome!) hallucinations.
In the news, using LLMs to find flaws, directory traversal in an MCP, another resource for learning cloud and AI security, spreadsheets and appsec, and more!
Visit https://www.securityweekly.com/asw for all the latest episodes!
Show Notes: https://securityweekly.com/asw-351
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By Mike Shema
4.9
1212 ratings
Software has forever had flaws and humans have forever been finding and fixing them. With LLMs generating code, appsec has also been trying to determine how well LLMs can find flaws. Nico Waisman talks about XBOW's LLM-based pentesting, how it climbed a bug bounty leaderboard, how it uses feedback loops for better pentests, and how they handle (and even welcome!) hallucinations.
In the news, using LLMs to find flaws, directory traversal in an MCP, another resource for learning cloud and AI security, spreadsheets and appsec, and more!
Visit https://www.securityweekly.com/asw for all the latest episodes!
Show Notes: https://securityweekly.com/asw-351
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