---
title: "awesome-llm-security vs do-not-answer"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/corca-ai-awesome-llm-security-vs-libr-ai-do-not-answer"
tools: ["corca-ai-awesome-llm-security", "libr-ai-do-not-answer"]
---

# awesome-llm-security vs do-not-answer

*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 do-not-answer if dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses.

[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. [do-not-answer](https://github.com/Libr-AI/do-not-answer) has 339 stars, 29 forks, and 0 open issues, last pushed Jun 7, 2024. Figures are from public GitHub metadata via [awesome-llm-security's repository](https://github.com/corca-ai/awesome-llm-security) and [do-not-answer's repository](https://github.com/Libr-AI/do-not-answer).

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [do-not-answer](/tools/libr-ai-do-not-answer.md) |
| --- | --- | --- |
| Tagline | A curation of tools, documents and projects about LLM Security | A Dataset for Evaluating Safeguards in LLMs |
| Stars | 1,672 | 339 |
| Forks | 312 | 29 |
| Open issues | 173 | 0 |
| Language | - | Jupyter Notebook |
| 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 | Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses. |
| Persona | - | - |
| Runtime | - | - |
| License | - | Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license. |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

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

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [do-not-answer](/tools/libr-ai-do-not-answer.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 351d | 788d |
| Open issues (now) | 173 | 0 |
| Full report | [trust report](/tools/corca-ai-awesome-llm-security/trust.md) | [trust report](/tools/libr-ai-do-not-answer/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: do-not-answer

- **Adopt for:** Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses.
- **License detail:** Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license.

## 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, llm, 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 do-not-answer if…

- Tags unique to do-not-answer: datasets, ethical ai, llm-evaluation, safeguard testing.
- To assess the reliability of safeguards implemented in your Large Language Model.
- Leaner open-issue backlog (0).

## 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 do-not-answer

- If you require tools for direct implementation or fine-tuning LLMs rather than evaluating them.
- Your project does not involve assessing ethical compliance or safeguard measures within language models.

## Common questions

### What is the difference between awesome-llm-security and do-not-answer?

awesome-llm-security: A curation of tools, documents and projects about LLM Security. do-not-answer: A Dataset for Evaluating Safeguards in LLMs. See the comparison table for live GitHub stats and shared categories.

### When should I choose awesome-llm-security over do-not-answer?

Choose awesome-llm-security over do-not-answer 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, llm, 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 do-not-answer over awesome-llm-security?

Choose do-not-answer over awesome-llm-security when Tags unique to do-not-answer: datasets, ethical ai, llm-evaluation, safeguard testing; To assess the reliability of safeguards implemented in your Large Language Model; Leaner open-issue backlog (0).

### 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 do-not-answer?

If you require tools for direct implementation or fine-tuning LLMs rather than evaluating them. Your project does not involve assessing ethical compliance or safeguard measures within language models.

### Is awesome-llm-security or do-not-answer more popular on GitHub?

awesome-llm-security has more GitHub stars (1,672 vs 339). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-llm-security and do-not-answer open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to awesome-llm-security or do-not-answer?

GraphCanon lists graph-backed alternatives at [awesome-llm-security alternatives](/tools/corca-ai-awesome-llm-security/alternatives) and [do-not-answer alternatives](/tools/libr-ai-do-not-answer/alternatives) ([awesome-llm-security markdown twin](/tools/corca-ai-awesome-llm-security/alternatives.md), [do-not-answer markdown twin](/tools/libr-ai-do-not-answer/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-libr-ai-do-not-answer.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 do-not-answer?

awesome-llm-security: Slowing. do-not-answer: 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 do-not-answer?

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); [do-not-answer trust report](/tools/libr-ai-do-not-answer/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/_