Open-Prompt-Injection
Benchmark and toolkit for prompt injection attacks and defenses in LLMs
GraphCanon updated Oct 4, 2026 · GitHub synced Oct 4, 2026
52views this month
Decision brief
Open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs.
Good fit when
- You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications.
- Your project involves evaluating or extending defense mechanisms against prompt injection.
Avoid when
- You require broader, more generalized security features not centered on prompt injection attacks.
- Your project does not involve working with Google PaLM2 or other specific models like Meta's Llama and OpenAI's GPT.
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (6d since push)
- As of Oct 4, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 4, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install Open-Prompt-InjectionSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Open-Prompt-Injection offers an open-source suite for implementing, evaluating, and extending prompt injection attacks, defense mechanisms, and integrating them into applications and agents.
Capability facts
- Languages
- python
Source: github.language · Oct 4, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Oct 4, 2026)
## Required Python packagesSource link
Tags
README
Open Prompt Injection Introduction This repository is an open source toolkit for prompt injection attacks and defenses. It enables implementation, evaluation, and extension of attacks, defenses, and LLM integrated applications and agents. For a deeper dive into prompt injection,...
For agents
This page has a .md twin and JSON over the API.