--- title: "Open-Prompt-Injection vs MultiPL-E" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/liu00222-open-prompt-injection-vs-nuprl-multipl-e" tools: ["liu00222-open-prompt-injection", "nuprl-multipl-e"] --- # Open-Prompt-Injection vs MultiPL-E *GraphCanon updated Aug 5, 2026* ## Verdict Pick Open-Prompt-Injection if open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs; pick MultiPL-E if multiPL-E is a benchmark system translating Python-based coding challenges across multiple programming languages. [Open-Prompt-Injection](https://github.com/liu00222/Open-Prompt-Injection) reports 470 GitHub stars, 74 forks, and 14 open issues, last pushed Oct 29, 2025. [MultiPL-E](https://github.com/nuprl/MultiPL-E) has 313 stars, 57 forks, and 16 open issues, last pushed Apr 12, 2026. Figures are from public GitHub metadata via [Open-Prompt-Injection's repository](https://github.com/liu00222/Open-Prompt-Injection) and [MultiPL-E's repository](https://github.com/nuprl/MultiPL-E). | | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | [MultiPL-E](/tools/nuprl-multipl-e.md) | | --- | --- | --- | | Tagline | Benchmark and toolkit for prompt injection attacks and defenses in LLMs | A multi-programming language benchmark for LLMs | | Stars | 470 | 313 | | Forks | 74 | 57 | | Open issues | 14 | 16 | | Language | Python | Python | | Adopt for | Open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs. | MultiPL-E is a benchmark system translating Python-based coding challenges across multiple programming languages. | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | [MultiPL-E](/tools/nuprl-multipl-e.md) | | --- | --- | --- | | Days since push | 279d | 115d | | Open issues (now) | 14 | 16 | | Owner type | User | Organization | | Full report | [trust report](/tools/liu00222-open-prompt-injection/trust.md) | [trust report](/tools/nuprl-multipl-e/trust.md) | ## Shared compatibility - **Python**: [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) - Python runtime; [MultiPL-E](/tools/nuprl-multipl-e.md) - Python runtime ## Decision facts: Open-Prompt-Injection - **Adopt for:** Open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs. ## Decision facts: MultiPL-E - **Pricing:** freemium - Free to use but requires local compute resources and potentially licensed libraries - **Adopt for:** MultiPL-E is a benchmark system translating Python-based coding challenges across multiple programming languages. - **License detail:** Other ## Choose when ### Choose Open-Prompt-Injection if… - License: Open-Prompt-Injection is MIT, MultiPL-E is Other. - Tags unique to Open-Prompt-Injection: llm, llm security, prompt-injection, security-and-privacy. - You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications. ### Choose MultiPL-E if… - License: MultiPL-E is Other, Open-Prompt-Injection is MIT. - Pricing: Free to use but requires local compute resources and potentially licensed libraries. - Tags unique to MultiPL-E: ai benchmark, benchmarking, code generation, multilingual benchmark. - Use MultiPL-E for evaluating large language models' performance on code generation tasks in different languages directly without needing to create new benchmarks from scratch. ## When NOT to use Open-Prompt-Injection - You require broader, more generalized security features not centered on prompt injection attacks. - Your project does not involve working with Google PaLM2 or other specific models like Meta's Llama and OpenAI's GPT. ## When NOT to use MultiPL-E - Avoid using MultiPL-E if you need a more challenging benchmark; consider Ag-LiveCodeBench-X instead. - Do not use MultiPL-E if your evaluation environment lacks GPU resources for completion generation or does not support Docker or Podman for execution of generated code. ## Common questions ### What is the difference between Open-Prompt-Injection and MultiPL-E? Open-Prompt-Injection: Benchmark and toolkit for prompt injection attacks and defenses in LLMs. MultiPL-E: A multi-programming language benchmark for LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose Open-Prompt-Injection over MultiPL-E? Choose Open-Prompt-Injection over MultiPL-E when License: Open-Prompt-Injection is MIT, MultiPL-E is Other; Tags unique to Open-Prompt-Injection: llm, llm security, prompt-injection, security-and-privacy; You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications. ### When should I choose MultiPL-E over Open-Prompt-Injection? Choose MultiPL-E over Open-Prompt-Injection when License: MultiPL-E is Other, Open-Prompt-Injection is MIT; Pricing: Free to use but requires local compute resources and potentially licensed libraries; Tags unique to MultiPL-E: ai benchmark, benchmarking, code generation, multilingual benchmark; Use MultiPL-E for evaluating large language models' performance on code generation tasks in different languages directly without needing to create new benchmarks from scratch. ### When should I avoid Open-Prompt-Injection? You require broader, more generalized security features not centered on prompt injection attacks. Your project does not involve working with Google PaLM2 or other specific models like Meta's Llama and OpenAI's GPT. ### When should I avoid MultiPL-E? Avoid using MultiPL-E if you need a more challenging benchmark; consider Ag-LiveCodeBench-X instead. Do not use MultiPL-E if your evaluation environment lacks GPU resources for completion generation or does not support Docker or Podman for execution of generated code. ### Is Open-Prompt-Injection or MultiPL-E more popular on GitHub? Open-Prompt-Injection has more GitHub stars (470 vs 313). Stars measure visibility, not whether either tool fits your constraints. ### Are Open-Prompt-Injection and MultiPL-E open source? Yes - both are open-source projects on GitHub (Open-Prompt-Injection: MIT, MultiPL-E: Other). ### Where can I find alternatives to Open-Prompt-Injection or MultiPL-E? GraphCanon lists graph-backed alternatives at [Open-Prompt-Injection alternatives](/tools/liu00222-open-prompt-injection/alternatives) and [MultiPL-E alternatives](/tools/nuprl-multipl-e/alternatives) ([Open-Prompt-Injection markdown twin](/tools/liu00222-open-prompt-injection/alternatives.md), [MultiPL-E markdown twin](/tools/nuprl-multipl-e/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/liu00222-open-prompt-injection-vs-nuprl-multipl-e.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Open-Prompt-Injection or MultiPL-E? Open-Prompt-Injection: Slowing. MultiPL-E: Slowing. 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 Open-Prompt-Injection and MultiPL-E? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Open-Prompt-Injection trust report](/tools/liu00222-open-prompt-injection/trust); [MultiPL-E trust report](/tools/nuprl-multipl-e/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=liu00222-open-prompt-injection`](/api/graphcanon/graph?tool=liu00222-open-prompt-injection) - 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/_