Large language models (LLMs) are now being used to automate cyberattacks, democratizing sophisticated hacking techniques and making them accessible to even unskilled individuals. This concerning trend raises the stakes in the cybersecurity landscape. But what if the very weaknesses of these AI attackers could be used against them? Researchers have introduced "Mantis," a novel defensive framework that turns the tables on LLM-driven attacks. Mantis leverages "prompt injection," a known vulnerability of LLMs, to trick attacking agents into disrupting their own operations or even compromising their own systems. Think of it as setting a trap within the system's responses. When an AI attacker interacts with a Mantis-protected system, it encounters seemingly vulnerable decoy services. Once the attacker engages, Mantis injects carefully crafted prompts into the system's output. These prompts are designed to exploit the LLM's tendency to follow instructions blindly, leading it down a path of self-destruction. The attacker's LLM might be tricked into opening a backdoor into its own system, effectively "hacking back" the hacker. Alternatively, Mantis might lure the attacker into a time-wasting "tarpit," forcing it to expend resources on meaningless tasks within a simulated environment. This not only neutralizes the immediate threat but also potentially inflicts financial costs on the attacker by draining their LLM's processing time. In tests, Mantis demonstrated remarkable effectiveness, achieving over a 95% success rate in neutralizing automated LLM-driven attacks across various scenarios. This research reveals a promising new avenue in cybersecurity, where AI's vulnerabilities are turned into powerful defensive tools. While the long-term effectiveness hinges on the evolving nature of LLMs and their susceptibility to prompt injection, Mantis represents a significant step towards building more resilient systems in the age of AI-powered attacks. The open-sourcing of Mantis further encourages community involvement and research into this critical area, potentially sparking further innovation in AI-driven defense mechanisms.