BIPIA: Benchmark for Indirect Prompt Injection Attacks
BIPIA (Benchmarking Indirect Prompt Injection Attacks) is an innovative benchmark designed to evaluate the robustness of Large Language Models (LLMs) and their defenses against indirect prompt injection attacks. The project provides essential tools and datasets necessary for researchers to systematically assess and enhance the security of LLM implementations.
Key Features:
- Comprehensive Evaluation: Evaluates 25 existing LLMs against indirect prompt injection attacks.
- Defense Proposals: Introduces several defense strategies for both black-box and white-box scenarios.
- Robust Dataset: Incorporates a diverse dataset covering various tasks like Web QA, Email QA, Table QA, Summarization, and Code QA.
- Installation Instructions: Easy setup for different operating systems with clear dependencies required for implementation.
- Example Code: Provides examples to demonstrate how the benchmark can be effectively utilized and tested.
Benefits:
- Research Facilitation: Aims to inspire future research on securing LLMs against prompt injection attacks.
- Reproducibility: Code and datasets are made available to ensure that results can be reproduced and built upon in the research community.
- Responsible AI Testing: Promotes safe and fair operation of AI technologies, ensuring that robustness is evaluated across different groups.
In summary, BIPIA advances the field of AI security by introducing structured methodologies for evaluating and defending against the vulnerability of large language models to prompt injection attacks.
