  As Large Language Models (LLMs) increasingly become key components in various
AI applications, understanding their security vulnerabilities and the
effectiveness of defense mechanisms is crucial. This survey examines the
security challenges of LLMs, focusing on two main areas: Prompt Hacking and
Adversarial Attacks, each with specific types of threats. Under Prompt Hacking,
we explore Prompt Injection and Jailbreaking Attacks, discussing how they work,
their potential impacts, and ways to mitigate them. Similarly, we analyze
Adversarial Attacks, breaking them down into Data Poisoning Attacks and
Backdoor Attacks. This structured examination helps us understand the
relationships between these vulnerabilities and the defense strategies that can
be implemented. The survey highlights these security challenges and discusses
robust defensive frameworks to protect LLMs against these threats. By detailing
these security issues, the survey contributes to the broader discussion on
creating resilient AI systems that can resist sophisticated attacks.
