#

llm-attacks

Here are 5 public repositories matching this topic...

DmitrL-dev / AISecurity

AI Security Platform: Defense (61 Rust engines + Micro-Model Swarm) + Offense (39K+ payloads)

Updated Mar 2, 2026
Python

0x-Professor / VeilArmor

Veil Armor is an enterprise-grade security framework for Large Language Models (LLMs) that provides multi-layered protection against prompt injections, jailbreaks, PII leakage, and sophisticated attack vectors.

jailbreak llm-security llm-guard llm-guardrails prompt-injection-llm-security model-security llm-security-compliance-prompt-injection llm-attacks veil-armor

Updated Feb 6, 2026
Python

AI-Security-Research-Group / LLM-Attacks

Comprehensive taxonomy of AI security vulnerabilities, LLM adversarial attacks, prompt injection techniques, and machine learning security research. Covers 71+ attack vectors including model poisoning, agentic AI exploits, and privacy breaches.

machine-learning ai-security ai-privacy open-source-security adversarial-ml prompt-injection ai-red-teaming model-poisoning llm-attacks ai-vulnerabilities security-taxonomy

Updated Sep 19, 2025

clolomagico123 / ai-security-lab

🤖 Test and secure AI systems with advanced techniques for Large Language Models, including jailbreaks and automated vulnerability scanners.

incident-response dfir ctf offensive-security blue-team adversarial-attacks ai-security cybersecurity-education hands-on-labs adversarial-ml llm ai-red-team prompt-injection langchain-alternative agentic-ai-security llm-attacks

Updated Mar 2, 2026

adarsh-rai-secure / llm-prompt-injection-rag-attacks

Testing how LLM guardrails fail across prompt attacks, context overflow, and RAG poisoning.

ai-safety secml llm-security prompt-injection-llm-security llm-attacks rag-attack-surface

Updated Jan 18, 2026
Jupyter Notebook

Improve this page

Add a description, image, and links to the llm-attacks topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the llm-attacks topic, visit your repo's landing page and select "manage topics."