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Explore Bill Gates’ AI Manifesto, Stolen LLMs & AI Honeypots feat. Eli Woodward with 15,575 views, 439 likes, and 0 comments. Experience the impact of this video content that has captured audience attention.

What Happens When AI Becomes Both the Attacker and the Thing We’re Trying to Detect? What does real-world AI abuse look like—and how will cybersecurity teams detect threats when AI agents can take actions on their own? In this episode of Detection Dispatch, host Alex sits down with Eli Woodward to explore the rapidly evolving intersection of AI security, detection engineering, threat hunting, AI honeypots, LLM attacks, and autonomous AI agents. Eli shares how a Christmas AWS honeypot experiment evolved into a global network of AI honeypots, capturing millions of prompts and providing a unique view into what attackers are actually doing with exposed AI infrastructure. The conversation dives into LLM jacking, stolen compute, malicious prompts, AI agents, attack behavior, telemetry, and AI detection challenges—including one of the hardest questions for defenders: Was an action performed by a human, an AI agent, or an attacker controlling the system? As AI moves beyond answering questions and starts taking real-world actions, traditional approaches to detection engineering and threat detection may need to evolve. 🔎 IN THIS EPISODE: • How AI honeypots reveal real-world AI abuse and attacker behavior • What attackers are doing with exposed AI infrastructure and stolen compute • LLM jacking, malicious prompts, and emerging AI attack techniques • How defenders can distinguish human-driven from agent-driven activity • Why timing, sequencing, and context could become critical AI detection signals • What changes when AI evolves from knowledge worker to autonomous actor • The growing connection between AI security engineering and detection engineering • Why AI systems need grounding, evaluation, testing, and guardrails • What happens when AI agents begin performing detection and security work themselves • Where humans may still have an advantage: judgment, taste, curation, and knowing when AI is wrong 🔗 RESOURCES AI Honeypots: https://ai-honeypots.com/ Bill Gates’ AI Manifesto: https://www.gatesnotes.com/a-turbulent-ai-era-and-critical-choices-to-make 📚 The conversation also connects to The AI Security Engineering Skills Map and Alex’s recent work with Dan Nguyen-Huu exploring why detection engineering and AI security engineering are increasingly becoming the same discipline. 🎙️ ABOUT DETECTION DISPATCH Detection Dispatch (Alex’s Version) is an independent detection engineering and threat hunting podcast focused on the people, technologies, and active projects pushing the boundaries of cybersecurity. From AI security and threat detection to detection engineering, threat hunting, telemetry, and autonomous agents, Detection Dispatch explores what defenders need to understand as the security landscape changes. 👍 If you found this episode valuable, like, subscribe, and share Detection Dispatch for more conversations about cybersecurity, AI security, detection engineering, and threat hunting. #AISecurity #Cybersecurity #DetectionEngineering #ThreatHunting #AIHoneypots #LLMSecurity #AIAgents #ThreatDetection #AIThreats #DetectionDispatch

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