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

# last_layer vs awesome-llm-security

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick last_layer if an ultra-fast Python tool for detecting prompt injections and jailbreak attempts in large language models suitable for projects requiring rapid security evaluations, with low-latency performance; 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.

[last_layer](https://vibe-eval.com) reports 133 GitHub stars, 6 forks, and 13 open issues, last pushed Jul 26, 2024. [awesome-llm-security](https://github.com/corca-ai/awesome-llm-security) has 1.7k stars, 347 forks, and 207 open issues, last pushed Aug 20, 2025. Figures are from public GitHub metadata via [last_layer's repository](https://github.com/arekusandr/last_layer) and [awesome-llm-security's repository](https://github.com/corca-ai/awesome-llm-security).

| | [last_layer](/tools/arekusandr-last-layer.md) | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) |
| --- | --- | --- |
| Tagline | Ultra-fast low latency LLM prompt injection jailbreak detection | A curation of tools, documents and projects about LLM Security |
| Stars | 133 | 1,692 |
| Forks | 6 | 347 |
| Open issues | 13 | 207 |
| Language | Python | - |
| Adopt for | An ultra-fast Python tool for detecting prompt injections and jailbreak attempts in large language models suitable for projects requiring rapid security evaluations, with low-latency performance. | 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 |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | - |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

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

| | [last_layer](/tools/arekusandr-last-layer.md) | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) |
| --- | --- | --- |
| Days since push | 780d | 382d |
| Open issues (now) | 13 | 207 |
| Stars delta | +2 (30d) | +20 (30d) |
| Open issues delta | 0 (30d) | +34 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/arekusandr-last-layer/trust.md) | [trust report](/tools/corca-ai-awesome-llm-security/trust.md) |

## Decision facts: last_layer

- **Adopt for:** An ultra-fast Python tool for detecting prompt injections and jailbreak attempts in large language models suitable for projects requiring rapid security evaluations, with low-latency performance.

## 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

## Choose when

### Choose last_layer if…

- Tags unique to last_layer: chatgpt-prompts, jailbreak, large-language-models, llm-guard.
- When you need fast detection of potential security vulnerabilities due to unauthorized prompt manipulations in real-time scenarios involving LLMs
- Leaner open-issue backlog (13).

### 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.

## When NOT to use last_layer

- If your application does not require ultra-low latency detection and can afford slower, potentially more comprehensive security evaluations
- For environments that prefer a broader range of security features beyond prompt injection detection, as last_layer focuses specifically on this aspect with speed in mind

## 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.

## Common questions

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

last_layer: Ultra-fast low latency LLM prompt injection jailbreak detection. awesome-llm-security: A curation of tools, documents and projects about LLM Security. See the comparison table for live GitHub stats and shared categories.

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

Choose last_layer over awesome-llm-security when Tags unique to last_layer: chatgpt-prompts, jailbreak, large-language-models, llm-guard; When you need fast detection of potential security vulnerabilities due to unauthorized prompt manipulations in real-time scenarios involving LLMs; Leaner open-issue backlog (13).

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

Choose awesome-llm-security over last_layer 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 avoid last_layer?

If your application does not require ultra-low latency detection and can afford slower, potentially more comprehensive security evaluations For environments that prefer a broader range of security features beyond prompt injection detection, as last_layer focuses specifically on this aspect with speed in mind

### 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.

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

awesome-llm-security has more GitHub stars (1,692 vs 133). Stars measure visibility, not whether either tool fits your constraints.

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

Yes - both are open-source projects on GitHub.

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

GraphCanon lists graph-backed alternatives at [last_layer alternatives](/tools/arekusandr-last-layer/alternatives) and [awesome-llm-security alternatives](/tools/corca-ai-awesome-llm-security/alternatives) ([last_layer markdown twin](/tools/arekusandr-last-layer/alternatives.md), [awesome-llm-security markdown twin](/tools/corca-ai-awesome-llm-security/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/arekusandr-last-layer-vs-corca-ai-awesome-llm-security.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, last_layer or awesome-llm-security?

last_layer: Dormant. awesome-llm-security: 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 last_layer and awesome-llm-security?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [last_layer trust report](/tools/arekusandr-last-layer/trust); [awesome-llm-security trust report](/tools/corca-ai-awesome-llm-security/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=arekusandr-last-layer`](/api/graphcanon/graph?tool=arekusandr-last-layer)
- 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/_