Awesome-LLMSecOps
Enrichment pendingLLM | Agentic | Security | Operations in one github repo with good links and pictures.
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git clone https://github.com/wearetyomsmnv/Awesome-LLMSecOpsSimilar tools
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Overview
LLM | Agentic | Security | Operations in one github repo with good links and pictures.
Capability facts
- Languages
- html
Source: github.language · Jul 15, 2026
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README
🚀 Awesome LLMSecOps
🔐 A curated list of awesome resources for LLMSecOps (Large Language Model Security Operations) 🧠
by @wearetyomsmnv and people
Architecture | Vulnerabilities | Tools | Defense | Threat Modeling | Jailbreaks | RAG Security | PoC's | Study Resources | Books | Blogs | Datasets for Testing | OPS Security | Frameworks | Best Practices | Research | Tutorials | Companies | Community Resources
LLM safety is a huge body of knowledge that is important and relevant to society today. The purpose of this Awesome list is to provide the community with the necessary knowledge on how to build an LLM development process - safe, as well as what threats may be encountered along the way. Everyone is welcome to contribute.
[!IMPORTANT] This repository, unlike many existing repositories, emphasizes the practical implementation of security and does not provide a lot of references to arxiv in the description.
Architecture risks
Overview of fundamental architectural risks and challenges in LLM systems.
| Risk | Description |
|---|---|
| Recursive Pollution | LLMs can produce incorrect output with high confidence. If such output is used in training data, it can cause future LLMs to be trained on polluted data, creating a feedback loop problem. |
| Data Debt | LLMs rely on massive datasets, often too large to thoroughly vet. This lack of transparency and control over data quality presents a significant risk. |
| Black Box Opacity | Many critical components of LLMs are hidden in a "black box" controlled by foundation model providers, making it difficult for users to manage and mitigate risks effectively. |
| Prompt Manipulation | Manipulating the input prompts can lead to unstable and unpredictable LLM behavior. This risk is similar to adversarial inputs in other ML systems. |
| Poison in the Data | Training data can be contaminated intentionally or unintentionally, leading to compromised model integrity. This is especially problematic given the size and scope of data used in LLMs. |
| Reproducibility Economics | The high cost of training LLMs limits reproducibility and independent verification, leading to a reliance on commercial entities and potentially unreviewed models. |
| Model Trustworthiness | The inherent stochastic nature of LLMs and their lack of true understanding can make their output unreliable. This raises questions about whether they should be trusted in critical applications. |
| Encoding Integrity | Data is often processed and re-represented in ways that can introduce bias and other issues. This is particularly challenging with LLMs due to their unsupervised learning nature. |
From Berryville Institute of Machine Learning (BIML) paper
Vulnerabilities description
by Giskard
Common vulnerabilities and security issues found in LLM applications.
| Vulnerability | Description |
|---|---|
| Hallucination and Misinformation | These vulnerabilities often manifest themselves in the generation of fabricated content or the spread of false information, which can have far-reaching consequences such as disseminating misleading content or malicious narratives. |
| Harmful Content Generation | This vulnerability involves the creation of harmful or malicious content, including violence, hate speech, or misinformation with malicious intent, posing a threat to individuals or communities. |
| Prompt Injection | Users manipulating input prompts to bypass content filters or override model instructions can lead to the generation of inappropriate or biased content, circumventing intended safeguards. |
| Robustness | The lack of |
For agents
This page has a .md twin and JSON over the API.