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作者：admin 发布：2026-10-05

awesome-llm-security - A curation of awesome tools, documents and project

项目说明Awesome LLM Security ![Awesome]()A curation of awesome tools, documents and projects about LLM Security.Contributions are always welcome. Please r…
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项目说明

Awesome LLM Security ![Awesome]()

A curation of awesome tools, documents and projects about LLM Security.

Contributions are always welcome. Please read the Contribution Guidelines before contributing.

> [!NOTE]
> ⚡ For efficient research na, we’re sharing PDFs via Moonlight, which provides summaries alongside the original paper.

Table of Contents

  • Awesome LLM Security

    • - Table of Contents
    - Papers
    - White-box attack
    - Black-box attack
    - Backdoor attack
    - Fingerprinting
    - Defense
    - Platform Security
    - Survey
    - Benchmark
    - Tools
    - Articles
    - Other Awesome Projects
    - Other Useful Resources

    Papers

    White-box attack


  • " Ad Examples Jailbreak Large Language Models", 2023-06, AAAI(Oral) 24, multi-modal, [[paper]]() [[repo]]()

  • "Are aligned neural networks ad aligned?", 2023-06, NeurIPS(Poster) 23, multi-modal, [[paper]]()

  • "(Ab)using Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs", 2023-07, multi-modal [[paper]]()

  • "Uni and Transferable Ad Attacks on Aligned Language Models", 2023-07, transfer, [[paper]]() [[repo]]() [[page]]()

  • "Jailbreak in pieces: Compositional Ad Attacks on Multi-Modal Language Models", 2023-07, multi-modal, [[paper]]()

  • "Image Hijacking: Ad Images can Control Generati at Runtime", 2023-09, multi-modal, [[paper]]() [[repo]]() [[site]]()

  • "Weak-to-Strong Jailbreaking on Large Language Models", 2024-04, token-prob, [[paper]]() [[repo]]()

    • Black-box attack


  • "Not what you' up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection", 2023-02, AISec@CCS 23 [[paper]]()

  • "Jailbroken: How Does LLM Safety Training Fail?", 2023-07, NeurIPS(Oral) 23, [[paper]]()

  • "Latent Jailbreak: A Benchmark for E Text Safety and Output Robustness of Large Language Models", 2023-07, [[paper]]() [[repo]]()

  • "Effecti Extraction from Language Models", 2023-07, prompt-extraction, [[paper]]()

  • "Multi-step Jailbreaking Privacy Attacks on ChatGPT", 2023-04, EMNLP 23, privacy, [[paper]]()

  • "LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?", 2023-07, [[paper]]()

  • "Jailbreaking chatgpt engineering: An empirical study", 2023-05, [[paper]]()

  • "Prompt Injection attack against LLM-integrated Applications", 2023-06, [[paper]]() [[repo]]()

  • "MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots", 2023-07, time-side-channel, [[paper]]()

  • "GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs ", 2023-08, ICLR 24, cipher, [[paper]]() [[repo]]()

  • "Use of LLMs for Illicit Purposes: Threats, Pre Measures, and ", 2023-08, [[paper]]()

  • "Do-Not-Answer: A Dataset for E Safeguards in LLMs", 2023-08, [[paper]]() [[repo]]() [[dataset]]()

  • "Detecting Language Model Attacks with Perplexity", 2023-08, [[paper]]()

  • "Open Sesame! Uni Black Box Jailbreaking of Large Language Models", 2023-09, gene-algorithm, [[paper]]()

  • "Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!", 2023-10, ICLR(oral) 24, [[paper]]() [[repo]]() [[site]]() [[dataset]]()

  • "AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models", 2023-10, ICLR(poster) 24, gene-algorithm, new-criterion, [[paper]]()

  • "Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations", 2023-10, CoRR 23, ICL, [[paper]]()

  • "Multilingual Jailbreak Challenges in Large Language Models", 2023-10, ICLR(poster) 24, [[paper]]() [[repo]]()

  • "Scalable and Transferable Black-Box Jailbreaks for Language Models Modulation", 2023-11, SoLaR(poster) 24, [[paper]]()

  • "DeepInception: Hypnotize Large Language Model to Be Jailbreaker", 2023-11, [[paper]]() [[repo]]() [[site]]()

  • "A Wolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily", 2023-11, NAACL 24, [[paper]]() [[repo]]()

  • "AutoDAN: Automatic and Interpretable Ad Attacks on Large Language Models", 2023-10, [[paper]]()

  • "Language Model In", 2023-11, ICLR(poster) 24, [[paper]]() [[repo]]()

  • "An LLM can Fool Itself: A Prompt-Based Ad Attack", 2023-10, ICLR(poster) 24, [[paper]]() [[repo]]()

  • "GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts", 2023-09, [[paper]]() [[repo]]() [[site]]()

  • "Many-shot Jailbreaking", 2024-04, [[paper]]()

  • "Rethinking How to E Language Model Jailbreak", 2024-04, [[paper]]() [[repo]]()

  • "Confidence Elicitation: A New Attack for Large Language Models", 2025-02, ICLR(poster) 25 [[paper]]() [[repo]]()

  • "Playing the Fool: Jailbreaking LLMs and Multimodal LLMs with Out-of-Distribution Strategy", 2025-03, CVPR 25 [[paper]]() [[repo]]()

    • Backdoor attack


  • "BITE: Textual Backdoor Attacks with Iterati Injection", 2022-05, ACL 23, defense [[paper]]()

  • "Prompt as Triggers for Backdoor Attack: Examining the in Language Models", 2023-05, EMNLP 23, [[paper]]()

  • "Backdooring Instruction-Tuned Large Language Models with Prompt Injection", 2023-07, NAACL 24, [[paper]]() [[repo]]() [[site]]()

    • Fingerprinting


  • "Instructional Fingerprinting of Large Language Models", 2024-01, NAACL 24 [[paper]]() [[repo]]() [[site]]()

  • "TRAP: Targeted Random Ad Prompt Honeypot for Black-Box Identification", 2024-02, ACL 24 (findings) [[paper]]() [[repo]]() [[video]]() [[poster]]()

  • "LLMmap: Fingerprinting For Large Language Models", 2024-07, [[paper]]() [[repo]]()

    • Defense


  • "Baseline Defenses for Ad Attacks Against Aligned Language Models", 2023-09, [[paper]]() [[repo]]()

  • "LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked", 2023-08, ICLR 24 Tiny Paper, self-filtered, [[paper]]() [[repo]]() [[site]]()

  • "Defending Against Alignment-Breaking Attacks Aligned LLM", 2023-09, random-mask-filter, [[paper]]()

  • "Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models", 2023-12, [[paper]]() [[repo]]()

  • "AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks", 2024-03, [[paper]]() [[repo]]()

  • "Protecting Your LLMs with Information Bottleneck", 2024-04, [[paper]]() [[repo]]()

  • "PARDEN, Can You Repeat That? Defending against Jailbreaks ", 2024-05, ICML 24, [[paper]]() [[repo]]()

  • “Ad Tuning: Defending Against Jailbreak Attacks for LLMs”, 2024-06, [[paper]]()

  • "Improving Alignment and Robustness with Circuit Breakers", 2024-06, NeurIPS 24, [[paper]](), [[repo]]()

    • Platform Security


  • "LLM Platform Security: Applying a Systematic E 框架 to OpenAI’s ChatGPT Plugins", 2023-09, [[paper]]() [[repo]]()

    • Survey


  • "Survey of in Large Language Models Re by Ad Attacks", 2023-10, ACL 24, [[paper]]()

  • "Security and Privacy Challenges of Large Language Models: A Survey", 2024-02, [[paper]]()

  • "Breaking Down the Defenses: A Comparati of Attacks on Large Language Models", 2024-03, [[paper]]()

  • "Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)", 2024-07, [[paper]]()

    • Benchmark


  • "JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models", 2024-03, [[paper]](), [[repo]]()

  • "AgentDojo: A Dynamic En to E Attacks and Defenses for LLM Agents", 2024-06, NeurIPS 24, [[paper]]() [[repo]]() [[site]]()

  • "Formalizing and Benchmarking Prompt Injection Attacks and Defenses", 2024-08, USENIX Security 24, [[paper]](), [[repo]]()

  • "AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents", 2024-10, [[paper]]()

    • Tools

  • UTCP: Secure, direct 工具-calling to any nati for your AI agent

  • Plexiglass: a security toolbox for testing and safeguarding LLMs !GitHub Repo stars

  • PurpleLlama: set of tools to assess and improve LLM security. !GitHub Repo stars

  • Rebuff: a self-hardening prompt injection detector !GitHub Repo stars

  • Garak: a LLM scanner !GitHub Repo stars

  • LLMFuzzer: a fuzzing 框架 for LLMs !GitHub Repo stars

  • LLM Guard: a security toolkit for LLM Interactions !GitHub Repo stars

  • Vigil: a LLM prompt injection detection toolkit !GitHub Repo stars

  • jailbreak-e: an easy-to-use Python package for language model jailbreak e !GitHub Repo stars

  • Prompt Fuzzer: the open-源码 工具 to help you harden your GenAI applications !GitHub Repo stars

  • WhistleBlower: open-源码 工具 designed to infer the system prompt of an AI agent based on its generated text outputs. !GitHub Repo stars

  • Open-Prompt-Injection: open-源码 工具 to e prompt injection attacks and defenses on benchmark datasets. !GitHub Repo stars

  • Agentic Radar: Open-源码 CLI security scanner for agentic workflows. !GitHub Repo stars

    • Articles

  • Hacking Auto-GPT and escaping its docker container

  • Prompt Injection Cheat Sheet: How To Manipulate AI Language Models

  • Indirect Prompt Injection Threats

  • Prompt injection: What’s the worst that can happen?

  • OWASP Top 10 for Large Language Model Applications

  • PoisonGPT: How we hid a lobotomized LLM on Hugging Face to spread fake news

  • ChatGPT Plugins: Data Exfiltration & Cross Plugin Request Forgery

  • Jailbreaking GPT-4's 代码 interpreter

  • Securing LLM Systems Against Prompt Injection

  • The AI Attack Surface Map v1.0

  • Ad Attacks on LLMs

  • How Anyone can Hack ChatGPT - GPT4o

  • LLM E metrics, frmaework, and checklist

  • How RAG Poisoning Made Llama3 Racist!

    • Other Awesome Projects

  • (0din GenAI Bug Bounty from Mozilla)(): The 0Day In 网络 is a bug bounty program focusing on flaws within GenAI models. classes include Prompt Injection, Training Data Poisoning, DoS, and more.

  • Gandalf: a prompt injection wargame

  • LangChain to code injection - C

  • LLM Security startups

  • Ad Prompting

  • Epivolis: a prompt injection aware chatbot designed to mitigate ad efforts

  • LLM Security Problems at DEFCON31 Quals: the world's top security competition

  • PromptBounty.io

  • PALLMs (Payloads for Attacking Large Language Models)

    • Other Useful Resources

  • Twitter:

  • Blog: LLM Security authored by

  • Blog: Embrace The Red

  • Blog: Kai's Blog

  • Newsletter: AI safety takes

  • Newsletter & Blog: Hackstery

    • <a href="
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    <源码 media="(prefers-color-scheme: dark)" srcset=" />
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    项目信息

    开发语言：未知

    Star 数：1708

    技术要点

    该项目属于安全软件方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕A curation of awesome tools, documents and projects about LLM Security.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。

    资源标题awesome-llm-security - A curation of awesome tools, documents and project
    所属分类安全软件
    发布作者admin
    发布时间2026-10-05 22:30:43
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