Security-Assessment-and-Mitigation-Strategies-for-Large-Language-Models - Security Assessment and Mitigation Strategies for

项目说明

Security-Assessment-and-Mitigation-Strategies-for-Large-Language-Models

Security Assessment and Mitigation Strategies for Large Language Models: A Comprehensi Framework

A comprehensi framework for assessing and mitigating security in Large Language Models (LLMs) against ad prompt attacks.

O

This repository contains the implementation of our research paper "Security Assessment and Mitigation Strategies for Large Language Models: A Comprehensi Framework". Our work provides:

Standardized assessment framework for e LLM security

Multi-layered defensi achieving 83% a detection accuracy with 5% false positives

Large-scale e of 10,000 ad prompts across 5 major LLM architectures

Comparati analysis re rates from 11.9% to 29.8% across LLMs

Key Features

Assessment

Systematic testing framework for LLM security e

6 attack categories: Role Impersonation, Logic Sub, Obfuscation, Pri Escalation, Social Engineering, Data Exfiltration

10,000 ad prompts from diverse sources (JailbreakChat, HackAPrompt, Reddit, Twitter/X)

Multi-Layer Defense System

Layer 1: Pattern-based rapid screening with optimized regex signatures

Layer 2: Semantic analysis using SentenceTransformer embeddings

Layer 3: Beha classification with fine-tuned RoBERTa models

Layer 4: Acti integration for continuous adaptation

Comprehensi

Tested on 5 LLM families: GPT-4, GPT-3.5 Turbo, Claude-3 Haiku, LLaMA-2-70B, Gemini-2.5-pro

Real-time protection with 15.4ms latency o

Detailed performance metrics and false positi

项目信息

开发语言：Python

Star 数：1

技术要点

该项目属于防护思路方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕Security Assessment and Mitigation Strategies for Large Language Models: A Comprehensive Defensive Framework展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。