PromptInject
Modular prompt assembly for robustness analysis of LLMs against adversarial attacks.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is PromptInject?
PromptInject is a framework that assembles prompts in a modular fashion to provide quantitative analysis of the robustness of large language models (LLMs) to adversarial prompt attacks. It was awarded Best Paper at NeurIPS ML Safety Workshop 2022.
Key differentiator
“PromptInject stands out by providing a modular and quantitative approach to assessing LLM robustness against adversarial attacks, making it an essential tool for AI safety research.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Official docs lack detailed guides on advanced prompt assembly techniques
GitHub repository has fewer than 10 contributors and limited activity in issue discussions
Fit analysis
Who is it for?
✓ Best for
Researchers studying adversarial attacks on LLMs who need a modular framework to assemble and test prompts
Teams developing AI safety measures for large language models in sensitive applications
✕ Not a fit for
Developers looking for real-time security monitoring solutions as PromptInject is focused on offline analysis
Projects with limited computational resources due to the potentially intensive nature of prompt evaluation
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with PromptInject
Step-by-step setup guide with code examples and common gotchas.