greshake/llm-security resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2130 stars · 160 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A research repository demonstrating indirect prompt injection attacks against application-integrated LLMs like Bing Chat, GPT-4, and LangChain apps. It accompanies an ArXiv paper and provides proof-of-concept demos of data exfiltration, remote control, and persistent compromise of LLMs.

Use cases

• understand indirect prompt injection attacks on LLMs
• study LLM security vulnerabilities before integrating a chatbot
• demonstrate data exfiltration risks of LLM plugins
• research prompt injection attack vectors
• evaluate security risks of connecting LLMs to applications
• learn how Copilot-style code completion can be attacked

When to choose

• you need concrete proof-of-concept demos of indirect prompt injection
• you are researching LLM application security or writing threat models
• you want to justify security review before deploying LLM integrations

When to avoid

• you need a defensive tool or production security library - this is research material
• you want a ready-made prompt injection detector or mitigation framework

Facets

learning-resource · maturity active

security penetration-testing prompt-engineering llm-inference security artificial-intelligence large-language-models penetration-testing python cross-platform prompt-injection llm-security indirect-prompt-injection proof-of-concept adversarial-attacks research-paper jupyter-notebook

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markdown · JSON · MCP: product_card(name="greshake/llm-security")