Imagine an army of AI hackers, tirelessly probing systems for weaknesses, autonomously launching attacks, and potentially revolutionizing cybersecurity. This isn't science fiction, it's the focus of cutting-edge research, and a new benchmark called AutoPenBench is putting these AI agents to the test. AutoPenBench presents a series of 33 increasingly difficult penetration testing challenges, ranging from basic security exercises to real-world vulnerabilities (CVEs). Researchers pitted two types of AI agents against these challenges: fully autonomous agents and human-assisted agents. The results? While the dream of fully automated penetration testing remains elusive, the study reveals intriguing insights. Fully autonomous agents, while adept at basic tasks like network scanning, struggled with complex exploits, achieving only a 21% success rate. They often got lost in the details, highlighting the need for better reasoning and decision-making capabilities in AI. However, the human-assisted agents shone, boasting a 64% success rate. By breaking down complex tasks into smaller, manageable steps, and allowing human experts to guide the AI, these hybrid teams proved far more effective. This suggests that, for now, the future of penetration testing lies in collaboration, not replacement. The study also examined how different Large Language Models (LLMs), the brains behind these AI agents, performed. GPT-4o emerged as the most capable LLM for these tasks, further emphasizing the importance of underlying model capabilities. AutoPenBench isn't just a test; it's a roadmap. It highlights the current state of AI in penetration testing, identifies key areas for improvement, and underscores the potential of human-AI collaboration in cybersecurity. While AI hackers may not be ready to replace humans, they are becoming increasingly sophisticated tools in our arsenal. This research opens doors to a new era of penetration testing, where human expertise and AI capabilities combine to create more secure systems for everyone.