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As large language models (LLMs) become integrated into business operations, ensuring their security is essential. While LLMs present unique challenges, lessons learned from internal network penetration testing—such as managing trust boundaries, identifying misconfigurations, and testing for privilege escalation—can significantly reinforce the pentesting approach for LLMs. Applying these practices helps detect potential vulnerabilities and protect AI environments more effectively.