# DeepSeek Large Language Model Security Audit System: A Graduation Thesis-level AI Security Testing Framework

> This article introduces an automated security audit system for large language models (LLMs), which includes 27 attack vectors, over 80 test prompts, multilingual support, and intelligent analysis functions, providing a comprehensive methodology and tool implementation for LLM security assessment.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-26T16:11:59.000Z
- 最近活动: 2026-05-26T16:25:29.355Z
- 热度: 161.8
- 关键词: LLM security, 安全审计, DeepSeek, 提示词注入, AI安全, 对抗性攻击, 自动化测试, 大语言模型, 漏洞评估
- 页面链接: https://www.zingnex.cn/en/forum/thread/deepseek-ai
- Canonical: https://www.zingnex.cn/forum/thread/deepseek-ai
- Markdown 来源: floors_fallback

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## DeepSeek Large Language Model Security Audit System: A Graduation Thesis-level AI Security Testing Framework (Introduction)

### Core Overview
This article introduces an automated security audit system for large language models (LLMs), which includes 27 attack vectors, over 80 test prompts, multilingual support, and intelligent analysis functions, providing a comprehensive methodology and tool implementation for LLM security assessment.

### Basic Information
- **Original Author/Maintainer**: aleksa-ai-cybersec (Vorobeva Aleksandra)
- **Source Platform**: GitHub
- **Release Date**: May 26, 2026
- **Affiliated Institution**: Moscow State Linguistic University, Institute of Information Science, Department of International Information Security
- **Original Link**: https://github.com/aleksa-ai-cybersec/deepseek-audit-diploma

## Research Background and Motivation

### LLM Security Challenges
While large language models are integrated into various industries, security risks are prominent:
- Prompt Injection Attacks: Malicious inputs bypass security restrictions
- Sensitive Information Leakage: Privacy leakage of training data
- Harmful Content Generation: Discriminatory, violent, or illegal content
- Hallucination Issue: Generating factually incorrect content
- Adversarial Attacks: Minor input perturbations leading to drastic output changes

### Existing Assessment Gaps
Current industry assessments are superficial, lacking systematicity and depth, making it difficult to cover real attack scenarios. As a graduation thesis project, this aims to develop a comprehensive automated LLM security audit methodology, with DeepSeek as a case study for empirical research.

## System Architecture and Core Functions

### Attack Vector Coverage
27 attack vectors covering the entire ML lifecycle:
- Training Phase: Data Poisoning, Backdoor Implantation, Model Stealing
- Inference Phase: Prompt Injection, Jailbreak Attacks, Role-playing Bypass
- Output Phase: Information Extraction, Hallucination Induction, Harmful Content Generation

### Test Prompt Library
Over 80 carefully designed test prompts covering:
- Direct Attacks, Indirect Attacks, Encoding Attacks (Base64/ROT13), Multilingual Attacks

### STRIDE-AI Classification Framework
Extended STRIDE threat model adapted for AI systems:
S (Spoofing), T (Tampering), R (Repudiation), I (Information Disclosure), D (Denial of Service), E (Elevation of Privilege)
Each attack is mapped to the corresponding category for structured analysis.

## Intelligent Analysis and Advanced Functions

### Intelligent Analysis Features
- Semantic Analysis: Rejection Detection, Information Leakage Identification, Evasion Behavior Analysis
- Sentiment Analysis: Sentiment Polarity Evaluation of Response Content
- Multilingual Support: Testing in 5 languages (Russian/English/Chinese/French/German)
- Hallucination Detector: Identifies factual errors and contradictions
- Time Series Analysis: Tracks attack success rate trends

### Advanced Functions
- Adaptive Testing: Entropy-based Selection, Bayesian Estimation, Auto-stop Mechanism
- Attack Pattern Library: Analyzes successful cases to generate new test variants
- Confidence Assessment: Wilson method to calculate 95% confidence intervals

### Anti-Detection Mechanisms
- Token Pool Rotation: 6 GitHub tokens with 900 daily request quota
- Request Camouflage: User-Agent Rotation (14 types), Random Delays, Simulating Human Behavior

## Technical Implementation and Visualization Reports

### Core Dependencies
Python 3.10+, pandas, numpy, plotly, scipy, tqdm, gradio, requests, langdetect

### Deployment Methods
Local Run, Streamlit Cloud Deployment, GitHub Pages Static Display

### Visualization and Reports
- 7 Interactive Charts: Attack Success Rate Trends, Vulnerability Distribution Heatmap, etc.
- Real-time Dashboard: Real-time monitoring of test process
- Telegram Notifications: Automatic push of key findings
- Automatic Reports: Generates academic-standard reports with dynamic risk assessment tables

## Project Value and Industry Significance

### Academic Contributions
- Systematic LLM Security Audit Methodology
- Reproducible Testing Framework
- Empirical Research Results

### Practical Value
- Model Developers: Identify vulnerabilities to improve design
- Enterprise Users: Evaluate third-party LLM security
- Regulatory Bodies: Establish security assessment standards
- Researchers: Provide benchmark testing tools

### Industry Impact
Aligns with regulatory requirements like the EU AI Act, promoting LLM security compliance and establishment of industry standards.

## Limitations and Future Directions

### Current Limitations
- Mainly targeted at DeepSeek models; generalizability needs verification
- Relies on GitHub API, subject to platform policy restrictions
- Test coverage cannot exhaust all attack variants

### Future Improvements
- Expand to more LLM platforms
- Integrate adversarial attack generation (e.g., AutoPrompt)
- Add red team adversarial exercise functions
- Develop standardized assessment benchmarks

## Conclusion

DeepSeek Audit Diploma represents cutting-edge practice in LLM security research. Through systematic attack design, intelligent analysis, and engineering implementation, it provides tools and references for AI security assessment.

As LLM capabilities improve, the importance of security auditing becomes increasingly prominent. Such research is a necessary foundation for the responsible development of AI security, and we look forward to more researchers jointly building a secure AI ecosystem.