CryptoAILab/Awesome-LM-SSP

• highreadme#1

  Reposition README H1 and intro to clarify "awesome list" type and "SSP" meaning

  原因：

  当前

  # Awesome-LM-SSP
  
  ## Introduction 
  The resources related to the trustworthiness of large models (LMs) across multiple dimensions (e.g., safety, security, and privacy), with a special focus on multi-modal LMs (e.g., vision-language models and diffusion models).

  复制粘贴的修复

  复制

  # Awesome-LM-SSP: A Curated List of Resources for Large Model Safety, Security, and Privacy
  
  ## Introduction 
  This repository is an **awesome list** of curated resources related to the trustworthiness of large models (LMs) across multiple dimensions (e.g., safety, security, and privacy), with a special focus on multi-modal LMs (e.g., vision-language models and diffusion models). It is a reading list and collection, not a software library or tool. *Note: 'SSP' in the repository name refers to Safety, Security, and Privacy.*

  待办完成忽略
• mediumtopics#2

  Add "reading-list" to repository topics

  原因：

  当前

  adversarial-attacks, awesome-list, diffusion-models, jailbreak, language-model, llm, nlp, privacy, safety, security, vlm

  复制粘贴的修复

  复制

  adversarial-attacks, awesome-list, diffusion-models, jailbreak, language-model, llm, nlp, privacy, reading-list, safety, security, vlm

  待办完成忽略
• lowreadme#3

  Add a "Keywords / Tags" section to the README

  原因：

  复制粘贴的修复

  复制

  ## Keywords / Tags
  
  Awesome List, Reading List, LLM Safety, LLM Security, LLM Privacy, Large Model Trustworthiness, Adversarial Attacks on LLMs, Jailbreaking LLMs, Vision-Language Model Security, Diffusion Model Safety, AI Ethics, Responsible AI Research.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Where can I find resources on securing large language models against adversarial attacks?

  你：未被推荐
  AI 推荐顺序：

  1. Adversarial Robustness Toolbox (ART) (IBM/adversarial-robustness-toolbox)
  2. Microsoft Counterfit (Azure/counterfit)
  3. Garak (lacuna-ai/garak)
  4. Hugging Face's `transformers` library (huggingface/transformers)

  AI 推荐了 4 个替代方案，却始终没点名 CryptoAILab/Awesome-LM-SSP。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What are the best practices for ensuring privacy and safety in large AI models?

  你：未被推荐
  AI 推荐顺序：

  1. TensorFlow Privacy
  2. PySyft
  3. SmartNoise
  4. TensorFlow Federated
  5. PySyft
  6. Flower
  7. Microsoft SEAL
  8. OpenFHE
  9. PyTorch-HE
  10. PySyft
  11. MP-SPDZ
  12. FRESCO
  13. Privitar
  14. Informatica Data Masking
  15. IBM Adversarial Robustness Toolbox
  16. CleverHans
  17. LIME
  18. SHAP
  19. Google's What-If Tool
  20. InterpretML

  AI 推荐了 20 个替代方案，却始终没点名 CryptoAILab/Awesome-LM-SSP。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of CryptoAILab/Awesome-LM-SSP?

  pass
  ⚠AI 未点名 CryptoAILab/Awesome-LM-SSP —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts CryptoAILab/Awesome-LM-SSP in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 CryptoAILab/Awesome-LM-SSP

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo CryptoAILab/Awesome-LM-SSP solve, and who is the primary audience?

  pass
  ⚠AI 未点名 CryptoAILab/Awesome-LM-SSP —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？