Risk Assessment and Security Analysis of Large Language Models

As large language models (LLMs) expose systemic security challenges in high risk applications, including privacy leaks, bias amplification, and malicious abuse, there is an urgent need for a dynamic risk assessment and collaborative defence framework that covers their entire life cycle. This paper focuses on the security problems of large language models (LLMs) in critical application scenarios, such as the possibility of disclosure of user data, the deliberate input of harmful instructions, or the models bias. To solve these problems, we describe the design of a system for dynamic risk assessment and a hierarchical defence system that allows different levels of protection to cooperate. This paper presents a risk assessment system capable of evaluating both static and dynamic indicators simultaneously. It uses entropy weighting to calculate essential data, such as the frequency of sensitive words, whether the API call is typical, the realtime risk entropy value is significant, and the degree of context deviation. The experimental results show that the system is capable of identifying concealed attacks, such as role escape, and can perform rapid risk evaluation. The paper uses a hybrid model called BERT-CRF (Bidirectional Encoder Representation from Transformers) at the input layer to identify and filter malicious commands. The model layer uses dynamic adversarial training and differential privacy noise injection technology together. The output layer also has a neural watermarking system that can track the source of the content. In practice, the quality of this method, especially important in terms of customer service in the financial industry.

💡 Research Summary

The paper addresses the emerging security challenges of large language models (LLMs) when deployed in high‑risk domains such as finance, healthcare, and law. It categorises the threats into three primary dimensions: data‑privacy leakage, bias propagation, and malicious abuse. Each threat can manifest during training (e.g., over‑expenditure of privacy budgets leading to gradient leakage), inference (prompt injection, reverse‑engineered API attacks), and deployment (open‑interface exploitation).

To mitigate these risks, the authors propose a comprehensive framework that combines a dynamic risk‑assessment engine with a hierarchical defence architecture. The risk‑assessment component fuses static indicators (sensitive‑word density, privacy‑budget values) and dynamic indicators (real‑time risk entropy, abnormal API call rates, context deviation) using an Entropy Weighted Fusion Evaluation (EWFE) method. Entropy weighting automatically determines the importance of each metric, eliminating the subjectivity of traditional static weighting schemes. The system integrates a Prometheus‑based real‑time monitoring API and the NSFOCUS Risk Matrix v1 to assign risk levels (T1‑T4) on the fly, achieving detection latencies under 50 ms for novel attack patterns such as role‑escape and zero‑sample jailbreaks.

The defence stack is organised into three layers. The input layer employs a hybrid BERT‑CRF model that jointly detects sensitive tokens and malicious commands; BERT provides deep contextual understanding while CRF preserves token‑level sequence dependencies. The model layer applies dynamic adversarial training to harden the system against evolving jailbreak and prompt‑injection attacks, and simultaneously injects differential‑privacy noise to bound the leakage of personal data during inference. The output layer incorporates a neural watermarking scheme that embeds traceable signatures into generated text, enabling post‑hoc provenance tracking—an essential capability for regulated sectors.

Experimental validation was performed on NVIDIA A100 GPU clusters using a mixture of public datasets, GPT‑4‑synthetic data, and anonymised industry data. The framework successfully identified concealed attacks such as role‑escape, achieving a three‑fold speed improvement over prior solutions and reducing malicious code execution success from 23 % to 1 %. The added defence overhead scales sub‑linearly with model size; a lightweight BERT variant (≤100 M parameters) added less than 5 ms of input latency, and overall latency fluctuations remained within 10 % under high‑concurrency loads. Importantly, text quality metrics (perplexity, BLEU) remained statistically unchanged, demonstrating that security hardening does not compromise generation fluency.

In summary, the study delivers a viable, end‑to‑end technical solution for LLM security governance: a real‑time, entropy‑driven risk assessment, a multi‑layered defence mechanism that blends static filtering, dynamic adversarial robustness, and privacy‑preserving noise, and a watermark‑based traceability layer. The authors argue that this closed‑loop system—assessment, early warning, optimisation—offers a practical path for enterprises to meet both operational security requirements and regulatory compliance. Future work is outlined to extend the approach to multimodal models, federated learning scenarios, and more resilient watermark‑forgery resistance.