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FAQ: Understanding the Security and Ethical Risks of Large Language Models
TL;DR
Companies can gain security advantages by implementing LLM defenses like watermark detection and adversarial training to prevent phishing and data breaches.
The study reviewed 73 papers, finding LLMs enable risks like phishing and misinformation, with defenses including adversarial training and watermark-based detection requiring improvement.
Ethical LLM development with transparency and oversight can reduce misinformation and bias, making AI tools safer for education and healthcare.
Researchers found LLMs can generate phishing emails with near-native fluency, while watermark detection identifies AI text with 98-99% accuracy.
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The study systematically reviews ethical and security risks associated with large language models (LLMs), identifying threats like phishing, malicious code generation, privacy breaches, and misinformation, while also evaluating current defense strategies.
LLMs empower innovation by generating fluent, human-like text that automates tasks and accelerates workflows in sectors like education and healthcare, but this same capability enables cyber-attacks, model manipulation, misinformation, and biased outputs that can threaten data security and public trust.
The study categorizes threats into misuse-based risks (e.g., phishing emails, malware scripting, false information production) and malicious attacks targeting models at data/model levels (e.g., model inversion, poisoning) and user interaction levels (e.g., prompt injection, jailbreaking).
Defense strategies include adversarial training, input preprocessing, watermark-based detection, parameter processing, and model alignment, though they remain insufficient against evolving attack techniques and require scalable, low-cost solutions.
A research team from Shanghai Jiao Tong University and East China Normal University published the review in Frontiers of Engineering Management (2025), with the DOI: 10.1007/s42524-025-4082-6.
The authors emphasize that technical safeguards must coexist with ethical governance, arguing that issues like hallucination, bias, and misinformation are social-level risks requiring systematic regulation and defense mechanisms to ensure trust and responsible deployment.
The review mentions semantic watermarking and CheckGPT as detection technologies that can identify model-generated text with up to 98–99% accuracy.
The findings highlight that the future of LLMs relies on coordinated security design, ethical oversight, and technical safeguards to mitigate risks, with further research needed to improve model governance and strengthen defenses against evolving threats.
Curated from 24-7 Press Release
