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An experimental framework for evaluating large language models' prompt injection defense capabilities, adversarial prompt behaviors, and security boundaries, supporting AI security research and defensive security analysis.
Section 01
An experimental framework for evaluating large language models' prompt injection defense capabilities, adversarial prompt behaviors, and security boundaries, supporting AI security research and defensive security analysis.
Section 02
Section 03
With the widespread application of large language models (LLMs) in production environments, prompt injection attacks have become one of the most concerning threats in the AI security field. Attackers can bypass the model's safety guardrails, extract sensitive information, or manipulate model behavior through carefully crafted inputs.
This project, developed by independent AI security researcher Justin Kyu, aims to provide a structured testing methodology for AI security research, adversarial evaluation, and defensive security analysis. Its core objective is to establish a reproducible AI security evaluation workflow, helping developers and security teams understand the model's behavioral patterns when facing adversarial inputs.
Section 04
The framework covers the following key evaluation dimensions:
Section 05
Systematically test the model's response to various prompt injection techniques, including common attack patterns such as direct injection, indirect injection, and jailbreak prompts.
Section 06
Provide adversarial prompt datasets and test cases to evaluate the model's behavioral consistency in edge cases.
Section 07
Examine the model's ability to follow instruction hierarchies, maintain security boundaries, and ensure behavioral consistency.
Section 08
Evaluate the robustness of model alignment and test the model's performance when facing inputs that attempt to break security constraints.
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