---
title: "multilingual-safety-for-LLMs vs LLMs-Finetuning-Safety"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/damo-nlp-sg-multilingual-safety-for-llms-vs-llm-tuning-safety-llms-finetuning-safety"
tools: ["damo-nlp-sg-multilingual-safety-for-llms", "llm-tuning-safety-llms-finetuning-safety"]
---

# multilingual-safety-for-LLMs vs LLMs-Finetuning-Safety

*GraphCanon updated Aug 5, 2026*

## Verdict

Pick multilingual-safety-for-LLMs if data for studying multilingual jailbreak safety in LLMs, including nine non-English languages categorized by resource availability; 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.

[multilingual-safety-for-LLMs](https://github.com/DAMO-NLP-SG/multilingual-safety-for-LLMs) reports 107 GitHub stars, 8 forks, and 0 open issues, last pushed Mar 7, 2024. [LLMs-Finetuning-Safety](https://llm-tuning-safety.github.io/) has 358 stars, 38 forks, and 3 open issues, last pushed Feb 23, 2024. Figures are from public GitHub metadata via [multilingual-safety-for-LLMs's repository](https://github.com/DAMO-NLP-SG/multilingual-safety-for-LLMs) and [LLMs-Finetuning-Safety's repository](https://github.com/LLM-Tuning-Safety/LLMs-Finetuning-Safety).

| | [multilingual-safety-for-LLMs](/tools/damo-nlp-sg-multilingual-safety-for-llms.md) | [LLMs-Finetuning-Safety](/tools/llm-tuning-safety-llms-finetuning-safety.md) |
| --- | --- | --- |
| Tagline | Data for Multilingual Jailbreak Challenges in Large Language Models | Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples |
| Stars | 107 | 358 |
| Forks | 8 | 38 |
| Open issues | 0 | 3 |
| Language | - | Python |
| Adopt for | Data for studying multilingual jailbreak safety in LLMs, including nine non-English languages categorized by resource availability. | LLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | Evaluation & Observability, Model Training | Evaluation & Observability, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [multilingual-safety-for-LLMs](/tools/damo-nlp-sg-multilingual-safety-for-llms.md) | [LLMs-Finetuning-Safety](/tools/llm-tuning-safety-llms-finetuning-safety.md) |
| --- | --- | --- |
| Days since push | 880d | 893d |
| Open issues (now) | 0 | 3 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/damo-nlp-sg-multilingual-safety-for-llms/trust.md) | [trust report](/tools/llm-tuning-safety-llms-finetuning-safety/trust.md) |

## Decision facts: multilingual-safety-for-LLMs

- **Adopt for:** Data for studying multilingual jailbreak safety in LLMs, including nine non-English languages categorized by resource availability.

## 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

## Choose when

### Choose multilingual-safety-for-LLMs if…

- Tags unique to multilingual-safety-for-LLMs: jailbreak, multilingual, safety.
- When evaluating the robustness of large language models against malicious prompts in multiple languages.
- More recently updated (last pushed Mar 7, 2024).

### Choose LLMs-Finetuning-Safety if…

- 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-finetuning, model safety.
- When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets.

## When NOT to use multilingual-safety-for-LLMs

- If solely focused on English-language security analysis, as this dataset emphasizes non-English prompts.
- When the target audience for your LLM is limited to high-resource language speakers only.

## 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.

## Common questions

### What is the difference between multilingual-safety-for-LLMs and LLMs-Finetuning-Safety?

multilingual-safety-for-LLMs: Data for Multilingual Jailbreak Challenges in Large Language Models. LLMs-Finetuning-Safety: Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples. See the comparison table for live GitHub stats and shared categories.

### When should I choose multilingual-safety-for-LLMs over LLMs-Finetuning-Safety?

Choose multilingual-safety-for-LLMs over LLMs-Finetuning-Safety when Tags unique to multilingual-safety-for-LLMs: jailbreak, multilingual, safety; When evaluating the robustness of large language models against malicious prompts in multiple languages; More recently updated (last pushed Mar 7, 2024).

### When should I choose LLMs-Finetuning-Safety over multilingual-safety-for-LLMs?

Choose LLMs-Finetuning-Safety over multilingual-safety-for-LLMs when 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-finetuning, model safety; When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets.

### When should I avoid multilingual-safety-for-LLMs?

If solely focused on English-language security analysis, as this dataset emphasizes non-English prompts. When the target audience for your LLM is limited to high-resource language speakers only.

### 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.

### Is multilingual-safety-for-LLMs or LLMs-Finetuning-Safety more popular on GitHub?

LLMs-Finetuning-Safety has more GitHub stars (358 vs 107). Stars measure visibility, not whether either tool fits your constraints.

### Are multilingual-safety-for-LLMs and LLMs-Finetuning-Safety open source?

Yes - both are open-source projects on GitHub (multilingual-safety-for-LLMs: MIT, LLMs-Finetuning-Safety: MIT).

### Where can I find alternatives to multilingual-safety-for-LLMs or LLMs-Finetuning-Safety?

GraphCanon lists graph-backed alternatives at [multilingual-safety-for-LLMs alternatives](/tools/damo-nlp-sg-multilingual-safety-for-llms/alternatives) and [LLMs-Finetuning-Safety alternatives](/tools/llm-tuning-safety-llms-finetuning-safety/alternatives) ([multilingual-safety-for-LLMs markdown twin](/tools/damo-nlp-sg-multilingual-safety-for-llms/alternatives.md), [LLMs-Finetuning-Safety markdown twin](/tools/llm-tuning-safety-llms-finetuning-safety/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/damo-nlp-sg-multilingual-safety-for-llms-vs-llm-tuning-safety-llms-finetuning-safety.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, multilingual-safety-for-LLMs or LLMs-Finetuning-Safety?

multilingual-safety-for-LLMs: Dormant. LLMs-Finetuning-Safety: 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 multilingual-safety-for-LLMs and LLMs-Finetuning-Safety?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [multilingual-safety-for-LLMs trust report](/tools/damo-nlp-sg-multilingual-safety-for-llms/trust); [LLMs-Finetuning-Safety trust report](/tools/llm-tuning-safety-llms-finetuning-safety/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=damo-nlp-sg-multilingual-safety-for-llms`](/api/graphcanon/graph?tool=damo-nlp-sg-multilingual-safety-for-llms)
- 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/_