---
title: "awesome-llm-security vs LLMSurvey"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/corca-ai-awesome-llm-security-vs-rucaibox-llmsurvey"
tools: ["corca-ai-awesome-llm-security", "rucaibox-llmsurvey"]
---

# awesome-llm-security vs LLMSurvey

*GraphCanon updated Jul 12, 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 LLMSurvey if lLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought.

[awesome-llm-security](https://github.com/corca-ai/awesome-llm-security) reports 1.6k GitHub stars, 294 forks, and 161 open issues, last pushed Aug 20, 2025. [LLMSurvey](https://arxiv.org/abs/2303.18223) has 12k stars, 935 forks, and 30 open issues, last pushed Mar 11, 2025. Figures are from public GitHub metadata via [awesome-llm-security's repository](https://github.com/corca-ai/awesome-llm-security) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey).

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) |
| --- | --- | --- |
| Tagline | A curation of tools, documents and projects about LLM Security | A comprehensive collection of papers and resources related to Large Language Models. |
| Stars | 1,637 | 12,187 |
| Forks | 294 | 935 |
| Open issues | 161 | 30 |
| 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 | LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训 |
| Persona | - | - |
| Runtime | - | - |
| License | - | The license for LLMSurvey is unknown based on the provided repository information. |
| Categories | Evaluation & Observability | LLM Frameworks, Evaluation & Observability |

## Trust and health

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

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 325d | 487d |
| Open issues (now) | 161 | 30 |
| Full report | [trust report](/tools/corca-ai-awesome-llm-security/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) |

## Shared compatibility

- **ChatGPT**: [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) - Works with ChatGPT; [LLMSurvey](/tools/rucaibox-llmsurvey.md) - Works with ChatGPT

## 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: LLMSurvey

- **Pricing:** freemium - Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage
- **Adopt for:** LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训
- **License detail:** The license for LLMSurvey is unknown based on the provided repository information.

## 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 LLMSurvey if…

- Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage.
- Tags unique to LLMSurvey: pre-training, chain-of-thought, instruction-tuning, rlhf.
- Also covers LLM Frameworks.
- You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models.

## 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 LLMSurvey

- You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers.
- Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how

## Common questions

### What is the difference between awesome-llm-security and LLMSurvey?

awesome-llm-security: A curation of tools, documents and projects about LLM Security. LLMSurvey: A comprehensive collection of papers and resources related to Large Language Models.. See the comparison table for live GitHub stats and shared categories.

### When should I choose awesome-llm-security over LLMSurvey?

Choose awesome-llm-security over LLMSurvey 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 LLMSurvey over awesome-llm-security?

Choose LLMSurvey over awesome-llm-security when Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage; Tags unique to LLMSurvey: pre-training, chain-of-thought, instruction-tuning, rlhf; Also covers LLM Frameworks; You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models.

### 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 LLMSurvey?

You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers. Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how

### Is awesome-llm-security or LLMSurvey more popular on GitHub?

LLMSurvey has more GitHub stars (12,187 vs 1,637). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-llm-security and LLMSurvey open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to awesome-llm-security or LLMSurvey?

GraphCanon lists graph-backed alternatives at [awesome-llm-security alternatives](/tools/corca-ai-awesome-llm-security/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([awesome-llm-security markdown twin](/tools/corca-ai-awesome-llm-security/alternatives.md), [LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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-rucaibox-llmsurvey.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 LLMSurvey?

awesome-llm-security: Slowing. LLMSurvey: 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 LLMSurvey?

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); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/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/_