--- title: "Open-Prompt-Injection vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/liu00222-open-prompt-injection-vs-mlabonne-llm-course" tools: ["liu00222-open-prompt-injection", "mlabonne-llm-course"] --- # Open-Prompt-Injection vs llm-course *GraphCanon updated Jul 11, 2026* ## Verdict Pick Open-Prompt-Injection when license: Open-Prompt-Injection is MIT, llm-course is Apache-2.0; pick llm-course when license: llm-course is Apache-2.0, Open-Prompt-Injection is MIT. [Open-Prompt-Injection](https://github.com/liu00222/Open-Prompt-Injection) reports 464 GitHub stars, 74 forks, and 14 open issues, last pushed Oct 29, 2025. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [Open-Prompt-Injection's repository](https://github.com/liu00222/Open-Prompt-Injection) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | This repository provides a benchmark for prompt injection attacks and defenses in LLMs | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 464 | 80,839 | | Forks | 74 | 9,421 | | Open issues | 14 | 84 | | Language | Python | - | | Adopt for | - | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Days since push | 255d | 155d | | Open issues (now) | 14 | 84 | | Full report | [trust report](/tools/liu00222-open-prompt-injection/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Shared compatibility - **Python**: [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) - Python runtime; [llm-course](/tools/mlabonne-llm-course.md) - Python runtime ## Decision facts: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to - **License detail:** Apache-2.0 ## Choose when ### Choose Open-Prompt-Injection if… - License: Open-Prompt-Injection is MIT, llm-course is Apache-2.0. - Tags unique to Open-Prompt-Injection: llm, llm-security, llms, prompt-injection. - Also covers AI Agents. ### Choose llm-course if… - License: llm-course is Apache-2.0, Open-Prompt-Injection is MIT. - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, course, large-language-models, machine-learning. - Also covers Evaluation & Observability, Inference & Serving. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## When NOT to use Open-Prompt-Injection - Last GitHub push was 255 days ago (slowing maintenance, Oct 29, 2025). Validate activity before betting a new project on Open-Prompt-Injection. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use llm-course - - If you only require a quick introduction to LLMs without deep dive into core components - - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ## Common questions ### What is the difference between Open-Prompt-Injection and llm-course? Open-Prompt-Injection: This repository provides a benchmark for prompt injection attacks and defenses in LLMs. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Open-Prompt-Injection over llm-course? Choose Open-Prompt-Injection over llm-course when License: Open-Prompt-Injection is MIT, llm-course is Apache-2.0; Tags unique to Open-Prompt-Injection: llm, llm-security, llms, prompt-injection; Also covers AI Agents. ### When should I choose llm-course over Open-Prompt-Injection? Choose llm-course over Open-Prompt-Injection when License: llm-course is Apache-2.0, Open-Prompt-Injection is MIT; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, large-language-models, machine-learning; Also covers Evaluation & Observability, Inference & Serving; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I avoid Open-Prompt-Injection? Last GitHub push was 255 days ago (slowing maintenance, Oct 29, 2025). Validate activity before betting a new project on Open-Prompt-Injection. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid llm-course? - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ### Is Open-Prompt-Injection or llm-course more popular on GitHub? llm-course has more GitHub stars (80,839 vs 464). Stars measure visibility, not whether either tool fits your constraints. ### Are Open-Prompt-Injection and llm-course open source? Yes - both are open-source projects on GitHub (Open-Prompt-Injection: MIT, llm-course: Apache-2.0). ### Where can I find alternatives to Open-Prompt-Injection or llm-course? GraphCanon lists graph-backed alternatives at [Open-Prompt-Injection alternatives](/tools/liu00222-open-prompt-injection/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([Open-Prompt-Injection markdown twin](/tools/liu00222-open-prompt-injection/alternatives.md), [llm-course markdown twin](/tools/mlabonne-llm-course/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-mlabonne-llm-course.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 llm-course? Open-Prompt-Injection: Slowing. llm-course: 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 llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Open-Prompt-Injection trust report](/tools/liu00222-open-prompt-injection/trust); [llm-course trust report](/tools/mlabonne-llm-course/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/_