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

# ALERT vs awesome-llm-security

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick ALERT if aLERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation; 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.

[ALERT](https://arxiv.org/abs/2404.08676) reports 59 GitHub stars, 8 forks, and 0 open issues, last pushed Sep 20, 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 [ALERT's repository](https://github.com/Babelscape/ALERT) and [awesome-llm-security's repository](https://github.com/corca-ai/awesome-llm-security).

| | [ALERT](/tools/babelscape-alert.md) | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) |
| --- | --- | --- |
| Tagline | A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming | A curation of tools, documents and projects about LLM Security |
| Stars | 59 | 1,692 |
| Forks | 8 | 347 |
| Open issues | 0 | 207 |
| Language | Python | - |
| Adopt for | ALERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation. | 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 | Other | - |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

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

| | [ALERT](/tools/babelscape-alert.md) | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) |
| --- | --- | --- |
| Days since push | 719d | 382d |
| Open issues (now) | 0 | 207 |
| Stars delta | 0 (30d) | +20 (30d) |
| Open issues delta | 0 (30d) | +34 (30d) |
| Full report | [trust report](/tools/babelscape-alert/trust.md) | [trust report](/tools/corca-ai-awesome-llm-security/trust.md) |

## Decision facts: ALERT

- **Adopt for:** ALERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation.

## 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 ALERT if…

- Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection.
- When evaluating safety metrics of large language models through red-teaming approaches
- Leaner open-issue backlog (0).

### 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 ALERT

- If your evaluation does not require bias detection or safety assessment under adversarial conditions
- In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks

## 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 ALERT and awesome-llm-security?

ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming. 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 ALERT over awesome-llm-security?

Choose ALERT over awesome-llm-security when Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection; When evaluating safety metrics of large language models through red-teaming approaches; Leaner open-issue backlog (0).

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

Choose awesome-llm-security over ALERT 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 ALERT?

If your evaluation does not require bias detection or safety assessment under adversarial conditions In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks

### 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 ALERT or awesome-llm-security more popular on GitHub?

awesome-llm-security has more GitHub stars (1,692 vs 59). Stars measure visibility, not whether either tool fits your constraints.

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

Yes - both are open-source projects on GitHub.

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

GraphCanon lists graph-backed alternatives at [ALERT alternatives](/tools/babelscape-alert/alternatives) and [awesome-llm-security alternatives](/tools/corca-ai-awesome-llm-security/alternatives) ([ALERT markdown twin](/tools/babelscape-alert/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/babelscape-alert-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, ALERT or awesome-llm-security?

ALERT: 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 ALERT and awesome-llm-security?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=babelscape-alert`](/api/graphcanon/graph?tool=babelscape-alert)
- 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/_