Study Exposes Dual Role of AI Language Models: Innovation Drivers & Security Threats

A comprehensive new study published in Frontiers of Engineering Management reveals the dual nature of large language models (LLMs) like GPT, BERT, and T5, which are simultaneously driving innovation and creating significant security and ethical vulnerabilities. Conducted by a research team from Shanghai Jiao Tong University and East China Normal University, the review of 73 key papers systematically documents how these powerful AI tools, while transforming sectors from healthcare to education, are being exploited for phishing attacks, malicious code generation, data leakage, and the spread of misinformation. The research, accessible via DOI 10.1007/s42524-025-4082-6, categorizes threats into misuse-based risks—such as crafting fluent phishing emails—and malicious attacks targeting the models themselves, including data poisoning and jailbreak techniques designed to bypass safety filters.

In response to these growing dangers, the study evaluates current defense strategies, highlighting three main technical approaches: parameter processing to reduce attack surfaces, input preprocessing to detect adversarial prompts, and adversarial training through red-teaming frameworks. Detection technologies like semantic watermarking and tools such as CheckGPT are noted for achieving up to 98–99% accuracy in identifying AI-generated text. However, the authors caution that these defenses are often outpaced by evolving attack methods, underscoring an urgent need for more scalable, cost-effective, and multilingual-adaptive solutions to keep pace with the threats.

Beyond technical fixes, the researchers emphasize that ethical governance is paramount. They argue that risks like social bias, privacy leakage, and model hallucination are societal issues requiring cross-disciplinary oversight, not just engineering problems. The future of secure and ethical LLM development hinges on integrating transparency, verifiable content traceability, and robust ethical review frameworks. If managed responsibly, with industry standards for watermark-based traceability and safer training datasets, LLMs can evolve into reliable tools that support innovation while protecting financial systems, reducing medical misinformation, and maintaining public trust. The full study is available through the journal Frontiers of Engineering Management, which provides a platform for this critical discussion on AI governance.