Llm-Security

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llm-security is an open-source tool that demonstrates new ways of breaking app-integrated LLMs. It focuses on indirect prompt injection attacks and their potential impact on security.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is llm-security?

llm-security is a comprehensive resource and proof-of-concept repository dedicated to exploring novel vulnerabilities in application-integrated Large Language Models (LLMs). It specifically highlights the dangers of "indirect prompt injection," a new class of attack vectors that can lead to remote control of LLMs, data exfiltration, persistent compromises, and automated social engineering. The tool provides demonstrations across various LLMs, including GPT-4 and GPT-3, and shows how these attacks can affect code completion engines like Copilot. It serves as a critical resource for security researchers and developers to understand and mitigate significant roadblocks to the secure deployment of LLMs.

Best used for

Ideal for developers and security researchers who need to understand the security implications of integrating LLMs into applications, identify potential attack vectors like indirect prompt injection, and develop robust defenses. Especially valuable for those working with GPT-3, GPT-4, and LangChain-based applications.

Common actions

analyze LLM security

understand prompt injection

test LLM vulnerabilities

"AI Agents"github copilotface swappinglow-code/no-codeautomated workflowopen-sourceworkflowsdeepfakecollaboration

Capabilities

Key features

Demonstrates indirect prompt injection
Shows remote control of LLMs
Illustrates data exfiltration
Proves persistent compromise
Targets code completion engines
Automated social engineering demos

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of LLMs does llm-security demonstrate attacks on?

llm-security provides demonstrations of attacks on GPT-4 (including Bing Chat and synthetic apps), GPT-3, and LangChain-based applications. It also includes proof-of-concepts for attacks on code completion engines like Copilot, indicating broad applicability across various LLM integrations.

What is indirect prompt injection and why is it significant?

Indirect prompt injection is a new class of vulnerability where an LLM is compromised by processing external, untrusted data (like a website or email) containing malicious instructions, rather than direct user input. This is significant because it allows adversaries to remotely control LLMs and exfiltrate data without direct interaction.

How can I run the demonstrations provided by llm-security?

To run the demonstrations, you need to install the required Python packages using `pip install -r requirements.txt`. For OpenAI model demos, your OpenAI API key must be set as an environment variable `OPENAI_API_KEY`. The demos can then be executed via `python scenarios/main.py`.

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Open Source & Models Code Assistants DevOps & Infrastructure No-Code / Low-Code Backend & APIs Prompt Engineering

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