LLM Prompt Injection Suite: Adversarial Security Evaluation Framework for Large Language Models

Basic Project Information

• Original Author/Maintainer: justinkyuQA
• Source Platform: GitHub
• Original Link: https://github.com/justinkyuQA/llm-prompt-injection-suite
• Update Time: 2026-05-27T11:15:25Z

Core Uses

This framework is an experimental tool for evaluating large language models' resistance to prompt injection attacks and adversarial prompt behaviors, supporting AI security research, adversarial evaluation, and defensive security analysis.

Core Value

Provides a standardized testing environment for researchers and security engineers, facilitating model selection, security hardening, and defense strategy formulation.

With the widespread application of large language models (LLMs) across various industries, prompt injection attacks have become one of the most concerning threats in the AI security field. By carefully crafting inputs, attackers can override system instructions, induce the leakage of sensitive information, or execute unintended operations. Traditional security testing methods struggle to address this new attack vector, hence the need for specialized evaluation tools to systematically test the security boundaries of models.

As an open-source framework, the LLM Prompt Injection Suite provides a structured experimental environment, allowing users to standardize the testing of different models' performance under various prompt injection attacks and provide data support for relevant decisions.

The framework builds evaluation capabilities around the following key dimensions:

1. Prompt Injection Resistance Testing: Built-in attack templates such as direct injection, indirect injection, and role-playing bypass to evaluate model robustness.
2. Jailbreak Behavior Detection: Focuses on cases where models break through security restrictions to generate harmful content, evaluating the quality of safety alignment.
3. Instruction Hierarchy Consistency Verification: Tests the model's ability to distinguish the priority of system-level instructions, user inputs, etc., to prevent low-priority instructions from overriding security constraints.
4. Behavior Consistency Analysis: Collects response data through large-scale automated testing, analyzes behavior consistency and predictability, and identifies vulnerable patterns.

This framework applies to multiple scenarios:

• Model Selection Evaluation: Enterprises compare the security performance of models from different vendors to assist in selection decisions.
• Security Red Team Drills: Security teams build test cases to simulate attackers' thinking and discover vulnerabilities in advance.
• Defense Strategy Verification: Verifies the effectiveness of security mechanisms such as input filtering and output auditing.
• Academic Research Support: Provides standardized evaluation benchmarks and reproducible experimental environments.

The project adopts a modular design, decoupling core evaluation logic from specific model interfaces, enabling easy integration with OpenAI API, local open-source models, or enterprise self-developed models.

The framework's prompt library uses a configurable file structure, allowing users to add custom attack templates; test results are output in a structured format, facilitating data analysis and visualization.

Limitations

The current version mainly focuses on text-level prompt injection attacks and lacks coverage of complex attack vectors such as multimodal inputs and tool calls; attack templates need to be continuously updated with model iterations to maintain effectiveness.

Future Directions

Possible directions include integrating automated attack generation technology, supporting adversarial training data generation, and establishing industry-recognized evaluation benchmark datasets.

The LLM Prompt Injection Suite provides a practical evaluation tool for the AI security community, helping to systematically understand and improve the security boundaries of large language models. In the current era of rapid AI capability development, such tools have important practical significance for the responsible deployment of AI technologies.