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

# awesome-llm-security vs PromptAttack

*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 PromptAttack if promptAttack is an LLM-targeted adversarial attack tool that leverages prompt engineering to generate adversarial samples keeping semantic intact but misclassifying outputs.

[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. [PromptAttack](https://github.com/GodXuxilie/PromptAttack) has 117 stars, 17 forks, and 0 open issues, last pushed Jan 21, 2025. Figures are from public GitHub metadata via [awesome-llm-security's repository](https://github.com/corca-ai/awesome-llm-security) and [PromptAttack's repository](https://github.com/GodXuxilie/PromptAttack).

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [PromptAttack](/tools/godxuxilie-promptattack.md) |
| --- | --- | --- |
| Tagline | A curation of tools, documents and projects about LLM Security | An LLM can Fool Itself: A Prompt-Based Adversarial Attack |
| Stars | 1,672 | 117 |
| Forks | 312 | 17 |
| Open issues | 173 | 0 |
| 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 | PromptAttack is an LLM-targeted adversarial attack tool that leverages prompt engineering to generate adversarial samples keeping semantic intact but misclassifying outputs. |
| Persona | - | - |
| Runtime | - | - |
| 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) | [PromptAttack](/tools/godxuxilie-promptattack.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 351d | 560d |
| Open issues (now) | 173 | 0 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/corca-ai-awesome-llm-security/trust.md) | [trust report](/tools/godxuxilie-promptattack/trust.md) |

## Shared compatibility

- **ChatGPT**: [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) - Works with ChatGPT; [PromptAttack](/tools/godxuxilie-promptattack.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: PromptAttack

- **Adopt for:** PromptAttack is an LLM-targeted adversarial attack tool that leverages prompt engineering to generate adversarial samples keeping semantic intact but misclassifying outputs.

## 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 PromptAttack if…

- Tags unique to PromptAttack: adversarial attack, language model evaluation, prompt-engineering.
- For targeted analysis of adversarial robustness in specific language models.
- 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 PromptAttack

- If the focus is on general model improvement rather than adversarial testing.
- When working with proprietary or sensitive data that cannot be manipulated via external prompt tools, given potential data leakage concerns.

## Common questions

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

awesome-llm-security: A curation of tools, documents and projects about LLM Security. PromptAttack: An LLM can Fool Itself: A Prompt-Based Adversarial Attack. See the comparison table for live GitHub stats and shared categories.

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

Choose awesome-llm-security over PromptAttack 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 PromptAttack over awesome-llm-security?

Choose PromptAttack over awesome-llm-security when Tags unique to PromptAttack: adversarial attack, language model evaluation, prompt-engineering; For targeted analysis of adversarial robustness in specific language models; 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 PromptAttack?

If the focus is on general model improvement rather than adversarial testing. When working with proprietary or sensitive data that cannot be manipulated via external prompt tools, given potential data leakage concerns.

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

awesome-llm-security has more GitHub stars (1,672 vs 117). Stars measure visibility, not whether either tool fits your constraints.

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

Yes - both are open-source projects on GitHub.

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

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

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

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); [PromptAttack trust report](/tools/godxuxilie-promptattack/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/_