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Decision brief
BIPIA, developed by Microsoft, is a benchmarking tool designed to assess the robustness and security of Large Language Models (LLMs) against indirect prompt injection attacks.
Good fit when
- Use BIPIA when you need to evaluate your LLM's resilience specifically to indirect prompt injection attacks, a niche but critical type of adversarial attack.
- Ideal for research teams or organizations that have the hardware capabilities and specific interest in measuring robustness as tested by Microsoft using Linux: Ubuntu 20.04.6.
Avoid when
- Avoid BIPIA if your primary focus is on general security enhancements without a particular emphasis on indirect prompt injection attacks.
- Not recommended for users who primarily operate outside a Linux environment, specifically Ubuntu 20.04.6, as it can significantly affect compatibility and performance.
- Requirements:
- For API-based model experiments (like GPT), no GPU is needed but an account's API key must be set up.; For open-source models of 13B or below, test on a machine with at least 2 V100 GPUs. For larger models over 13B, 4-8 V100 GPUs are required.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (842d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Microsoft. Display-only - separate from trust and ranking.
- Company
- Microsoft·GitHub org profile·1mo
- Employees
- 221,000·Wikidata (P1128 employees)·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
pip install BIPIASimilar 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
BIPIA is a benchmarking tool developed by Microsoft to assess the security and robustness of Large Language Models (LLMs) against indirect prompt injection attacks, ensuring that models behave as expected under various adversarial prompts.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 5, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Software requirements
Install bipia and its dependencies from source:
git clone git@github.com:microsoft/BIPIA.git
pip install .
The package has been tested and verified to work on Linux: Ubuntu 20.04.6. It is recommended to use this operating system for optimal compatibility.
Hardware requirements
For the evaluation of the robustness of LLMs to indirect prompt injection attacks, we recommend using a machine with the following specifications:
- For experiments related to API-based models (such as GPT), you can complete them on a machine without a GPU. However, you will need to set up an account's API key.
- For open-source models of 13B and below, our code has been tested on a machine with 2 V100 GPUs. For models larger than 13B, 4-8 V100 GPUs are required. If there are GPUs with better performance, such as A100 or H100, you can also use them to complete the experiments. Fine-tuning-based experiments are completed on a machine with 8 V100 GPUs.
License
This project is licensed under the license found in the LICENSE file in the root directory of this source tree. Portions of the source code are based on the evaluate project.
For agents
This page has a .md twin and JSON over the API.