--- title: "ALERT vs BIPIA" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/babelscape-alert-vs-microsoft-bipia" tools: ["babelscape-alert", "microsoft-bipia"] --- # ALERT vs BIPIA *GraphCanon updated Aug 9, 2026* ## Verdict Pick ALERT if aLERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation; pick BIPIA if 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. [ALERT](https://arxiv.org/abs/2404.08676) reports 59 GitHub stars, 8 forks, and 0 open issues, last pushed Sep 20, 2024. [BIPIA](https://github.com/microsoft/BIPIA) has 149 stars, 19 forks, and 4 open issues, last pushed Apr 15, 2024. Figures are from public GitHub metadata via [ALERT's repository](https://github.com/Babelscape/ALERT) and [BIPIA's repository](https://github.com/microsoft/BIPIA). | | [ALERT](/tools/babelscape-alert.md) | [BIPIA](/tools/microsoft-bipia.md) | | --- | --- | --- | | Tagline | A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming | Benchmark for evaluating LLM robustness to indirect prompt injection attacks. | | Stars | 59 | 149 | | Forks | 8 | 19 | | Open issues | 0 | 4 | | Language | Python | Python | | Adopt for | ALERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation. | 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. | | Persona | - | - | | Runtime | - | - | | License | Other | Other | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ALERT](/tools/babelscape-alert.md) | [BIPIA](/tools/microsoft-bipia.md) | | --- | --- | --- | | Days since push | 687d | 842d | | Open issues (now) | 0 | 4 | | Full report | [trust report](/tools/babelscape-alert/trust.md) | [trust report](/tools/microsoft-bipia/trust.md) | ## Decision facts: ALERT - **Adopt for:** ALERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation. ## Decision facts: BIPIA - **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. - **Adopt for:** 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. ## Choose when ### Choose ALERT if… - Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection. - When evaluating safety metrics of large language models through red-teaming approaches - More recently updated (last pushed Sep 20, 2024). ### Choose BIPIA if… - 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.. - Tags unique to BIPIA: indirect-prompt-injection-attacks, llm security, microsoft-research, python library. - 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. ## When NOT to use ALERT - If your evaluation does not require bias detection or safety assessment under adversarial conditions - In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks ## When NOT to use BIPIA - 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. ## Common questions ### What is the difference between ALERT and BIPIA? ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming. BIPIA: Benchmark for evaluating LLM robustness to indirect prompt injection attacks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ALERT over BIPIA? Choose ALERT over BIPIA when Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection; When evaluating safety metrics of large language models through red-teaming approaches; More recently updated (last pushed Sep 20, 2024). ### When should I choose BIPIA over ALERT? Choose BIPIA over ALERT when 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.; Tags unique to BIPIA: indirect-prompt-injection-attacks, llm security, microsoft-research, python library; 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. ### When should I avoid ALERT? If your evaluation does not require bias detection or safety assessment under adversarial conditions In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks ### When should I avoid BIPIA? 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. ### Is ALERT or BIPIA more popular on GitHub? BIPIA has more GitHub stars (149 vs 59). Stars measure visibility, not whether either tool fits your constraints. ### Are ALERT and BIPIA open source? Yes - both are open-source projects on GitHub (ALERT: Other, BIPIA: Other). ### Where can I find alternatives to ALERT or BIPIA? GraphCanon lists graph-backed alternatives at [ALERT alternatives](/tools/babelscape-alert/alternatives) and [BIPIA alternatives](/tools/microsoft-bipia/alternatives) ([ALERT markdown twin](/tools/babelscape-alert/alternatives.md), [BIPIA markdown twin](/tools/microsoft-bipia/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/babelscape-alert-vs-microsoft-bipia.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ALERT or BIPIA? ALERT: Dormant. BIPIA: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for ALERT and BIPIA? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ALERT trust report](/tools/babelscape-alert/trust); [BIPIA trust report](/tools/microsoft-bipia/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=babelscape-alert`](/api/graphcanon/graph?tool=babelscape-alert) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_