LLMs Suck at Cyber Intel—Unless You Hack Them Right

Every second, 6,000 posts flood X.com—some valuable, most just noise. Hidden in the chaos are leaked credentials, malware sightings, and attack indicators. The challenge? Spotting real threats before they get lost in the noise. This session dives into how AI can turn social media chaos into actionable cyber intel. We’ll track key cybersecurity insiders, extract Indicators of Compromise (IOCs), and put machine learning to the test. But raw AI isn’t enough—off-the-shelf models choke on misinformation and context. The real power comes from hacking LLMs to make them sharper, faster, and more threat-aware. Through real-world data and hands-on analysis, we’ll break down what works, what fails, and how security teams can tweak AI to stay ahead of attackers. No fluff—just hard-hitting lessons on AI’s role in threat intelligence. If you want to see AI in action, with all its strengths and weaknesses laid bare, buckle up. This talk is for you. The demonstration will detail the technical architecture, including Langchain's role in orchestrating these components, the integration pipeline with X, and the transformation of raw content into structured feeds containing IPs, domains, and other user-defined indicators. These real-time threat intelligence feeds are then incorporated into the security protection pipeline.

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