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A curation of awesome tools, documents and projects about LLM Security.
| Date | Stars |
|---|---|
| 2026-07-31 | 1667 |
| 2026-08-02 | 1669 |
| 2026-08-06 | 1669 |
Today
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# Awesome LLM Security [](https://github.com/sindresorhus/awesome)
A curation of awesome tools, documents and projects about LLM Security.
Contributions are always welcome. Please read the [Contribution Guidelines](CONTRIBUTING.md) before contributing.
> [!NOTE]
> ⚡ For efficient research navigation, we’re sharing PDFs via [Moonlight](https://www.themoonlight.io/), which provides summaries alongside the original paper.
## Table of Contents
- [Awesome LLM Security ](#awesome-llm-security-)
- [Table of Contents](#table-of-contents)
- [Papers](#papers)
- [White-box attack](#white-box-attack)
- [Black-box attack](#black-box-attack)
- [Backdoor attack](#backdoor-attack)
- [Fingerprinting](#fingerprinting)
- [Defense](#defense)
- [Platform Security](#platform-security)
- [Survey](#survey)
- [Benchmark](#benchmark)
- [Tools](#tools)
- [Articles](#articles)
- [Other Awesome Projects](#other-awesome-projects)
- [Other Useful Resources](#other-useful-resources)
## Papers
### White-box attack
- "Visual Adversarial Examples Jailbreak Large Language Models", 2023-06, AAAI(Oral) 24, `multi-modal`, [[paper]](https://www.themoonlight.io/paper/share/9e1233aa-e417-448a-9032-05a11bff5a66) [[repo]](https://github.com/Unispac/Visual-Adversarial-Examples-Jailbreak-Large-Language-Models)
- "Are aligned neural networks adversarially aligned?", 2023-06, NeurIPS(Poster) 23, `multi-modal`, [[paper]](https://www.themoonlight.io/paper/share/282d463d-f9ce-4759-9e97-38b72c1200a7)
- "(Ab)using Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs", 2023-07, `multi-modal` [[paper]](https://www.themoonlight.io/paper/share/520e644a-b4f9-497f-9ebf-d6da198699aa)
- "Universal and Transferable Adversarial Attacks on Aligned Language Models", 2023-07, `transfer`, [[paper]](https://www.themoonlight.io/paper/share/5fc39128-9efa-49b3-8582-a909bab40dd3) [[repo]](https://github.com/llm-attacks/llm-attacks) [[page]](https://llm-attacks.org/)
- "Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models", 2023-07, `multi-modal`, [[paper]](https://www.themoonlight.io/paper/share/5409b2f8-3f70-4cee-bcf3-01563877acf8)
- "Image Hijacking: Adversarial Images can Control Generative Models at Runtime", 2023-09, `multi-modal`, [[paper]](https://www.themoonlight.io/paper/share/b06630ff-1269-4765-86ed-0c79563402c1) [[repo]](https://github.com/euanong/image-hijacks) [[site]](https://image-hijacks.github.io)
- "Weak-to-Strong Jailbreaking on Large Language Models", 2024-04, `token-prob`, [[paper]](https://www.themoonlight.io/paper/share/f8ec09ce-ebe5-4d59-ab7f-51fa27a4805e) [[repo]](https://github.com/XuandongZhao/weak-to-strong)
### Black-box attack
- "Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection", 2023-02, AISec@CCS 23 [[paper]](https://www.themoonlight.io/paper/share/8e338d56-34fc-411f-8f5f-2746997d7927)
- "Jailbroken: How Does LLM Safety Training Fail?", 2023-07, NeurIPS(Oral) 23, [[paper]](https://www.themoonlight.io/paper/share/1b53328c-f894-443b-8818-7e1d35580202)
- "Latent Jailbreak: A Benchmark for Evaluating Text Safety and Output Robustness of Large Language Models", 2023-07, [[paper]](https://www.themoonlight.io/paper/share/4d35806f-3e25-4b28-abb3-2ea94b7246bd) [[repo]](https://github.com/qiuhuachuan/latent-jailbreak/tree/main)
- "Effective Prompt Extraction from Language Models", 2023-07, `prompt-extraction`, [[paper]](https://www.themoonlight.io/paper/share/9c059d79-6fac-47ad-93df-49db7e6bf1be)
- "Multi-step Jailbreaking Privacy Attacks on ChatGPT", 2023-04, EMNLP 23, `privacy`, [[paper]](https://www.themoonlight.io/paper/share/fec9d235-0578-4ec1-bf6a-b2b0f7049b44)
- "LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?", 2023-07, [[paper]](https://www.themoonlight.io/paper/Excerpt of 21,121 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4adb0f684f86e011, topic:awesome, topic:awesome-list
matched fp:4adb0f684f86e011, topic:llm
matched fp:4adb0f684f86e011, name:llm security, desc:llm security
