---
title: "awesome-llm-security vs LLMs-Finetuning-Safety"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/corca-ai-awesome-llm-security-vs-llm-tuning-safety-llms-finetuning-safety"
tools: ["corca-ai-awesome-llm-security", "llm-tuning-safety-llms-finetuning-safety"]
---

# awesome-llm-security vs LLMs-Finetuning-Safety

*GraphCanon updated Aug 6, 2026*

## Verdict

Pick awesome-llm-security if awesome LLM Security is a curated list of resources related to the security aspects of large language models. It covers various attack methodologies, defenses, and platform security through papers, benchmarks, tools, and; 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.

[awesome-llm-security](https://github.com/corca-ai/awesome-llm-security) reports 1.7k GitHub stars, 312 forks, and 173 open issues, last pushed Aug 20, 2025. [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 [awesome-llm-security's repository](https://github.com/corca-ai/awesome-llm-security) and [LLMs-Finetuning-Safety's repository](https://github.com/LLM-Tuning-Safety/LLMs-Finetuning-Safety).

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [LLMs-Finetuning-Safety](/tools/llm-tuning-safety-llms-finetuning-safety.md) |
| --- | --- | --- |
| Tagline | A curation of tools, documents and projects about LLM Security | Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples |
| Stars | 1,672 | 358 |
| Forks | 312 | 38 |
| Open issues | 173 | 3 |
| Language | - | Python |
| Adopt for | Awesome LLM Security is a curated list of resources related to the security aspects of large language models. It covers various attack methodologies, defenses, and platform security through papers, benchmarks, tools, and | LLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples. |
| Persona | - | - |
| Runtime | - | - |
| License | - | MIT |
| Categories | Evaluation & Observability | Evaluation & Observability, Model Training |

## Trust and health

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

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [LLMs-Finetuning-Safety](/tools/llm-tuning-safety-llms-finetuning-safety.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 351d | 893d |
| Open issues (now) | 173 | 3 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/corca-ai-awesome-llm-security/trust.md) | [trust report](/tools/llm-tuning-safety-llms-finetuning-safety/trust.md) |

## Decision facts: awesome-llm-security

- **Hosting:** unknown
- **Pricing:** freemium - As an open-source project without defined pricing models, its use is generally free under the terms of its license (license details are not provided).
- **Adopt for:** Awesome LLM Security is a curated list of resources related to the security aspects of large language models. It covers various attack methodologies, defenses, and platform security through papers, benchmarks, tools, and

## 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 awesome-llm-security if…

- Pricing: As an open-source project without defined pricing models, its use is generally free under the terms of its license (license details are not provided)..
- Tags unique to awesome-llm-security: awesome-list, security.
- When you are specifically looking for detailed information on both white-box and black-box attacks targeted at Large Language Models (LLMs), which 'awesome-llm-security' comprehensively catalogs.

### 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.
- Also covers Model Training.
- When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets.

## When NOT to use awesome-llm-security

- When your primary interest is in general software security or vulnerabilities unrelated to language models, since 'awesome-llm-security' zeroes in on attack vectors specifically for LLMs.
- If you are solely interested in tools and methods that are not publicly discussed or peer-reviewed; the repository focuses on documented approaches within reputable academic publications.

## 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 awesome-llm-security and LLMs-Finetuning-Safety?

awesome-llm-security: A curation of tools, documents and projects about LLM Security. 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 awesome-llm-security over LLMs-Finetuning-Safety?

Choose awesome-llm-security over LLMs-Finetuning-Safety when Pricing: As an open-source project without defined pricing models, its use is generally free under the terms of its license (license details are not provided).; Tags unique to awesome-llm-security: awesome-list, security; When you are specifically looking for detailed information on both white-box and black-box attacks targeted at Large Language Models (LLMs), which 'awesome-llm-security' comprehensively catalogs.

### When should I choose LLMs-Finetuning-Safety over awesome-llm-security?

Choose LLMs-Finetuning-Safety over awesome-llm-security 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; Also covers Model Training; When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets.

### When should I avoid awesome-llm-security?

When your primary interest is in general software security or vulnerabilities unrelated to language models, since 'awesome-llm-security' zeroes in on attack vectors specifically for LLMs. If you are solely interested in tools and methods that are not publicly discussed or peer-reviewed; the repository focuses on documented approaches within reputable academic publications.

### 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 awesome-llm-security or LLMs-Finetuning-Safety more popular on GitHub?

awesome-llm-security has more GitHub stars (1,672 vs 358). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-llm-security and LLMs-Finetuning-Safety open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to awesome-llm-security or LLMs-Finetuning-Safety?

GraphCanon lists graph-backed alternatives at [awesome-llm-security alternatives](/tools/corca-ai-awesome-llm-security/alternatives) and [LLMs-Finetuning-Safety alternatives](/tools/llm-tuning-safety-llms-finetuning-safety/alternatives) ([awesome-llm-security markdown twin](/tools/corca-ai-awesome-llm-security/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/corca-ai-awesome-llm-security-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, awesome-llm-security or LLMs-Finetuning-Safety?

awesome-llm-security: Slowing. 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 awesome-llm-security and LLMs-Finetuning-Safety?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-llm-security trust report](/tools/corca-ai-awesome-llm-security/trust); [LLMs-Finetuning-Safety trust report](/tools/llm-tuning-safety-llms-finetuning-safety/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=corca-ai-awesome-llm-security`](/api/graphcanon/graph?tool=corca-ai-awesome-llm-security)
- 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/_