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

# awesome-llm-security vs rebuff

*GraphCanon updated Oct 11, 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 rebuff if rebuff is a detector for prompt injection attacks on large language models and operates in TypeScript under an Apache-2.0 license.

[awesome-llm-security](https://github.com/corca-ai/awesome-llm-security) reports 1.7k GitHub stars, 398 forks, and 248 open issues, last pushed Aug 20, 2025. [rebuff](https://playground.rebuff.ai) has 1.5k stars, 150 forks, and 33 open issues, last pushed Aug 7, 2024. Figures are from public GitHub metadata via [awesome-llm-security's repository](https://github.com/corca-ai/awesome-llm-security) and [rebuff's repository](https://github.com/protectai/rebuff).

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [rebuff](/tools/protectai-rebuff.md) |
| --- | --- | --- |
| Tagline | A curation of tools, documents and projects about LLM Security | LLM Prompt Injection Detector |
| Stars | 1,710 | 1,526 |
| Forks | 398 | 150 |
| Open issues | 248 | 33 |
| Language | - | TypeScript |
| 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 | Rebuff is a detector for prompt injection attacks on large language models and operates in TypeScript under an Apache-2.0 license. |
| Persona | - | - |
| Runtime | - | - |
| License | - | Apache-2.0 |
| 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) | [rebuff](/tools/protectai-rebuff.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Archived (8%) |
| Days since push | 417d | 789d |
| Archived on GitHub | No | Yes |
| Open issues (now) | 248 | 33 |
| Stars delta | +18 (30d) | +5 (30d) |
| Open issues delta | +41 (30d) | 0 (30d) |
| Full report | [trust report](/tools/corca-ai-awesome-llm-security/trust.md) | [trust report](/tools/protectai-rebuff/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: rebuff

- **Adopt for:** Rebuff is a detector for prompt injection attacks on large language models and operates in TypeScript under an 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.
- 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 rebuff if…

- Tags unique to rebuff: llmops, prompt-engineering, prompt-injection, prompts.
- Use Rebuff when you need precise detection of prompt injection vulnerabilities specific to your deployment, especially if it relies heavily on interactions with large language models.
- Leaner open-issue backlog (33).

## 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 rebuff

- Do not use Rebuff if setting up and managing multiple provider services like Supabase, OpenAI, Pinecone, or Chroma is inconvenient or infeasible for your project requirements.

## Common questions

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

awesome-llm-security: A curation of tools, documents and projects about LLM Security. rebuff: LLM Prompt Injection Detector. See the comparison table for live GitHub stats and shared categories.

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

Choose awesome-llm-security over rebuff 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; 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 rebuff over awesome-llm-security?

Choose rebuff over awesome-llm-security when Tags unique to rebuff: llmops, prompt-engineering, prompt-injection, prompts; Use Rebuff when you need precise detection of prompt injection vulnerabilities specific to your deployment, especially if it relies heavily on interactions with large language models; Leaner open-issue backlog (33).

### 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 rebuff?

Do not use Rebuff if setting up and managing multiple provider services like Supabase, OpenAI, Pinecone, or Chroma is inconvenient or infeasible for your project requirements.

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

awesome-llm-security has more GitHub stars (1,710 vs 1,526). Stars measure visibility, not whether either tool fits your constraints.

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

Yes - both are open-source projects on GitHub.

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

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

awesome-llm-security: Dormant. rebuff: Archived. 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 rebuff?

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); [rebuff trust report](/tools/protectai-rebuff/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/_