This paper explores how the emergence of large-scale language models (LLMs) like ChatGPT has revolutionized the field of natural language processing (NLP), while simultaneously introducing new security vulnerabilities. We categorize threats into several key areas: prompt injection and jailbreaking, adversarial attacks (including input perturbation and data poisoning), information warfare by malicious actors, phishing emails and malware generation, and the risks of autonomous LLM agents. We further discuss emerging risks of autonomous LLM agents, including goal mismatch, emerging deception, self-preservation instincts, and the potential of LLMs to develop and pursue covert and inconsistent goals (known as planning). We summarize recent academic and industry research from 2022 to 2025, exemplifying each threat, analyzing proposed defenses and their limitations, and identifying unresolved challenges in securing LLM-based applications. Finally, we emphasize the importance of developing robust, multi-layered security strategies to ensure LLMs are both secure and beneficial.
