--- title: "LLMs-Finetuning-Safety vs JOOD" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/llm-tuning-safety-llms-finetuning-safety-vs-naver-ai-jood" tools: ["llm-tuning-safety-llms-finetuning-safety", "naver-ai-jood"] --- # LLMs-Finetuning-Safety vs JOOD *GraphCanon updated Aug 5, 2026* ## Verdict Pick LLMs-Finetuning-Safety if lLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples; pick JOOD if jOOD is an implementation for exploring strategies to jailbreak language and multimodal models using out-of-distribution inputs. It leverages Python and is licensed under Apache-2.0. [LLMs-Finetuning-Safety](https://llm-tuning-safety.github.io/) reports 358 GitHub stars, 38 forks, and 3 open issues, last pushed Feb 23, 2024. [JOOD](https://github.com/naver-ai/JOOD) has 21 stars, 4 forks, and 2 open issues, last pushed Jun 11, 2025. Figures are from public GitHub metadata via [LLMs-Finetuning-Safety's repository](https://github.com/LLM-Tuning-Safety/LLMs-Finetuning-Safety) and [JOOD's repository](https://github.com/naver-ai/JOOD). | | [LLMs-Finetuning-Safety](/tools/llm-tuning-safety-llms-finetuning-safety.md) | [JOOD](/tools/naver-ai-jood.md) | | --- | --- | --- | | Tagline | Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples | Implementation for multimodal LLM jailbreaking strategy | | Stars | 358 | 21 | | Forks | 38 | 4 | | Open issues | 3 | 2 | | Language | Python | Python | | Adopt for | LLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples. | JOOD is an implementation for exploring strategies to jailbreak language and multimodal models using out-of-distribution inputs. It leverages Python and is licensed under Apache-2.0. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMs-Finetuning-Safety](/tools/llm-tuning-safety-llms-finetuning-safety.md) | [JOOD](/tools/naver-ai-jood.md) | | --- | --- | --- | | Days since push | 893d | 419d | | Open issues (now) | 3 | 2 | | Owner type | User | Organization | | Full report | [trust report](/tools/llm-tuning-safety-llms-finetuning-safety/trust.md) | [trust report](/tools/naver-ai-jood/trust.md) | ## Decision facts: LLMs-Finetuning-Safety - **Pricing:** freemium - Open-source under the MIT license; free to use and modify. OpenAI API usage cost applies, but this repository demonstrates effects at less than $0.20. - **Adopt for:** LLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples. - **Runtime:** unknown ## Decision facts: JOOD - **Requirements:** Python version to install requirements: Python >= 3.12.7; The package list for dependencies should be sourced from the `requirements.txt` file provided in the repository. - **Adopt for:** JOOD is an implementation for exploring strategies to jailbreak language and multimodal models using out-of-distribution inputs. It leverages Python and is licensed under Apache-2.0. ## Choose when ### Choose LLMs-Finetuning-Safety if… - License: LLMs-Finetuning-Safety is MIT, JOOD is Apache-2.0. - Pricing: Open-source under the MIT license; free to use and modify. OpenAI API usage cost applies, but this repository demonstrates effects at less than $0.20.. - Tags unique to LLMs-Finetuning-Safety: adversarial training, alignment, llm, llm-finetuning. - Also covers Evaluation & Observability. - When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets. ### Choose JOOD if… - License: JOOD is Apache-2.0, LLMs-Finetuning-Safety is MIT. - Requirements: Python version to install requirements: Python >= 3.12.7; The package list for dependencies should be sourced from the `requirements.txt` file provided in the repository.. - Tags unique to JOOD: jailbreaking, multimodal-llms. - Also covers Computer Vision. - Use JOOD when you need to explore how a multimodal model behaves with unforeseen or out-of-distribution inputs, thus pushing the boundaries of its conventional responses or outputs. ## When NOT to use LLMs-Finetuning-Safety - When generalizing safety risks to other large language models that have different underlying architectures or safeguard mechanisms than GPT-3.5 Turbo. - If intending to use this tool as a method of fine-tuning any model for enhancing its performance on specific tasks, given it is designed for illustrating risk rather than improving capabilities. ## When NOT to use JOOD - Avoid using JOOD if jailbreaking strategies are not of interest, such as in scenarios requiring strict adherence to model limitations and ethical constraints. - JOOD may not be suitable if you require tools that focus on improving performance or stability of models rather than exploring unconventional behavior or vulnerabilities. ## Common questions ### What is the difference between LLMs-Finetuning-Safety and JOOD? LLMs-Finetuning-Safety: Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples. JOOD: Implementation for multimodal LLM jailbreaking strategy. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMs-Finetuning-Safety over JOOD? Choose LLMs-Finetuning-Safety over JOOD when License: LLMs-Finetuning-Safety is MIT, JOOD is Apache-2.0; Pricing: Open-source under the MIT license; free to use and modify. OpenAI API usage cost applies, but this repository demonstrates effects at less than $0.20.; Tags unique to LLMs-Finetuning-Safety: adversarial training, alignment, llm, llm-finetuning; Also covers Evaluation & Observability; When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets. ### When should I choose JOOD over LLMs-Finetuning-Safety? Choose JOOD over LLMs-Finetuning-Safety when License: JOOD is Apache-2.0, LLMs-Finetuning-Safety is MIT; Requirements: Python version to install requirements: Python >= 3.12.7; The package list for dependencies should be sourced from the `requirements.txt` file provided in the repository.; Tags unique to JOOD: jailbreaking, multimodal-llms; Also covers Computer Vision; Use JOOD when you need to explore how a multimodal model behaves with unforeseen or out-of-distribution inputs, thus pushing the boundaries of its conventional responses or outputs. ### When should I avoid LLMs-Finetuning-Safety? When generalizing safety risks to other large language models that have different underlying architectures or safeguard mechanisms than GPT-3.5 Turbo. If intending to use this tool as a method of fine-tuning any model for enhancing its performance on specific tasks, given it is designed for illustrating risk rather than improving capabilities. ### When should I avoid JOOD? Avoid using JOOD if jailbreaking strategies are not of interest, such as in scenarios requiring strict adherence to model limitations and ethical constraints. JOOD may not be suitable if you require tools that focus on improving performance or stability of models rather than exploring unconventional behavior or vulnerabilities. ### Is LLMs-Finetuning-Safety or JOOD more popular on GitHub? LLMs-Finetuning-Safety has more GitHub stars (358 vs 21). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMs-Finetuning-Safety and JOOD open source? Yes - both are open-source projects on GitHub (LLMs-Finetuning-Safety: MIT, JOOD: Apache-2.0). ### Where can I find alternatives to LLMs-Finetuning-Safety or JOOD? GraphCanon lists graph-backed alternatives at [LLMs-Finetuning-Safety alternatives](/tools/llm-tuning-safety-llms-finetuning-safety/alternatives) and [JOOD alternatives](/tools/naver-ai-jood/alternatives) ([LLMs-Finetuning-Safety markdown twin](/tools/llm-tuning-safety-llms-finetuning-safety/alternatives.md), [JOOD markdown twin](/tools/naver-ai-jood/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/llm-tuning-safety-llms-finetuning-safety-vs-naver-ai-jood.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMs-Finetuning-Safety or JOOD? LLMs-Finetuning-Safety: Dormant. JOOD: 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 LLMs-Finetuning-Safety and JOOD? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMs-Finetuning-Safety trust report](/tools/llm-tuning-safety-llms-finetuning-safety/trust); [JOOD trust report](/tools/naver-ai-jood/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=llm-tuning-safety-llms-finetuning-safety`](/api/graphcanon/graph?tool=llm-tuning-safety-llms-finetuning-safety) - 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/_