InjectLab

Free plan

What InjectLab is

InjectLab is a free, open-source framework and test suite for adversarial security research on large language models. Its structured YAML files map tests to techniques in the LLM ATT&CK Matrix and describe example prompts, expected or vulnerable behavior, scoring logic, and mitigation guidance. Covered tactics include prompt injection, role or instruction override, execution hijack, information disclosure, output manipulation, and multi-agent exploitation, with simulated plugin abuse also described. The project is intended for LLM red teaming, prompt fuzzing, comparing model resilience, and improving defensive detection and prompt hardening. It describes repeatable testing across models such as GPT-4, Claude, and LLaMA, including jailbreak-resilience scoring and a leaderboard. A Python command-line runner lists available tests, lets users select one, displays its prompt, and copies it to the clipboard. The runner attempts to install PyYAML and pyperclip when needed. The repository includes an MIT License. The project reports evaluating five adversarial techniques across multiple models, with broader evaluations planned, and identifies Austin Howard as its creator.

Who it suits

InjectLab suits defenders, developers, and researchers studying adversarial LLM threats. It is designed for self-hosted red teaming, prompt fuzzing, and model comparison.

What it does well

Free and open source
YAML tests map to ATT&CK techniques
Includes mitigation guidance
CLI runner copies prompts to clipboard
Supports jailbreak and data-leakage tests

What to know first

Broader evaluations are still planned
Runner may install dependencies with pip
Self-hosted deployment

Verdict

InjectLab provides structured adversarial tests, scoring ideas, and mitigation guidance for LLM security work. Its command-line runner and MIT License make the project usable for independent research and defensive testing.

Facts from InjectLab's own pages

Author: The repository says the framework was created by Austin Howard, a cybersecurity student at Western Governors University, as an independent research project. · github.com, 9 Oct 2026
CLI utility: A Python test runner lists the YAML prompt tests, lets a user select one, displays its prompt, and copies it to the clipboard. · github.com, 9 Oct 2026
Contact: The framework page lists [email protected] as a contact address. · github.com, 9 Oct 2026
Dependencies: The Python test runner attempts to install PyYAML and pyperclip with pip if they are missing. · github.com, 9 Oct 2026
Intended audience: The maker describes the project as an independent research effort for defenders, developers, and researchers studying adversarial LLM threats. · github.com, 9 Oct 2026
Intended use: The project says it supports LLM red teaming and prompt fuzzing, comparison of model resilience, defensive detection and prompt hardening, and future adversarial prompt benchmarks. · github.com, 9 Oct 2026
License: The repository includes an MIT License, which grants permission to use, copy, modify, distribute, sublicense, and sell copies subject to its conditions. · github.com, 9 Oct 2026
Mitigation guidance: The repository provides technique-specific mitigation recommendations alongside broader LLM security practices. · github.com, 9 Oct 2026
Model comparison: The project describes repeatable adversarial testing across models such as GPT-4, Claude, and LLaMA, with jailbreak resilience scoring and a leaderboard. · github.com, 9 Oct 2026
Model examples: The project describes repeatable adversarial testing across models including GPT-4, Claude, and LLaMA. · github.com, 9 Oct 2026
Purpose: InjectLab is an open-source ATT&CK-style framework and test suite for adversarial LLM security research. · github.com, 9 Oct 2026
Research scope: The project reports testing five adversarial techniques across multiple LLMs and says broader evaluations are planned. · github.com, 9 Oct 2026
Runner: The repository includes a Python CLI runner that lists available prompt tests, displays a selected prompt, and copies it to the clipboard. · github.com, 9 Oct 2026
Runner dependencies: The runner attempts to install PyYAML and pyperclip with pip if they are missing. · github.com, 9 Oct 2026
Test contents: Each YAML file maps to a technique in the LLM ATT&CK Matrix and includes example prompts, expected or vulnerable behaviors, scoring logic, and mitigation guidance. · github.com, 9 Oct 2026
Test format: The test suite uses YAML files that map to techniques in the LLM ATT&CK Matrix and include example prompts, expected or vulnerable behaviors, scoring logic, and mitigation guidance. · github.com, 9 Oct 2026
Testing goals: The project says it is intended to support LLM red teaming and prompt fuzzing, compare model resilience, and inform defensive detection and prompt hardening. · github.com, 9 Oct 2026
Test suite: Its structured YAML test suite simulates adversarial LLM behaviors such as prompt injection, role override, and plugin abuse. · github.com, 9 Oct 2026
Threat categories: The matrix describes techniques involving prompt injection, role or instruction override, execution hijack, information disclosure, output manipulation, and multi-agent exploitation. · github.com, 9 Oct 2026
Threat coverage: The framework describes tactics including prompt injection, role or instruction override, execution hijack, information disclosure, output manipulation, and multi-agent exploitation. · github.com, 9 Oct 2026

Where InjectLab stands on Freedom251

AI Security Testing Tools#22 of 29