LLM Honeypot: an early warning system for autonomous hacking
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Palisade Research has deployed a honeypot system to detect autonomous AI hacking attempts. The system uses digital traps that simulate vulnerable targets across 10 countries and has processed over 1.7 million interactions to date. By analyzing response patterns and timing, we separate AI-driven attacks from traditional cyber threats. This early warning system informs defenders about trends in autonomous hacking to help cybersecurity preparedness.
May 7, 2026
Language Models Can Autonomously Hack and Self-Replicate
We demonstrate that language models can autonomously replicate their weights and harness across a network by exploiting vulnerable hosts. The agent independently finds and exploits a web-application vulnerability, extracts credentials,...
February 12, 2026
Technical Report: Shutdown Resistance in Large Language Models, on robots!
Recently Palisade Research showed that AI agents powered by modern LLMs may actively resist shutdown in virtual environments. In this work, we show a demo of shutdown resistance in the...
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GPT-5 at CTFs: case studies from top cybersecurity events
OpenAI and DeepMind’s AIs recently got gold at the IMO math olympiad and ICPC programming competition. We show frontier AI is similarly good at hacking by letting GPT-5 compete in...
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Misalignment Bounty: crowdsourcing AI agent misbehavior
Advanced AI systems sometimes act in ways that differ from human intent. To gather clear, reproducible examples, we ran the Misalignment Bounty: a crowdsourced project that collected cases of agents...
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End-to-end hacking with AI agents
We show OpenAI o3 can autonomously breach a simulated corporate network. Our agent broke into three connected machines, moving deeper into the network until it reached the most protected server...