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

# awesome-llm-security vs SWE-bench

*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 SWE-bench if sWE-bench serves as a benchmark for assessing how well language models can tackle real-world software engineering issues from GitHub.

[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. [SWE-bench](https://www.swebench.com) has 5.6k stars, 930 forks, and 131 open issues, last pushed Jul 27, 2026. Figures are from public GitHub metadata via [awesome-llm-security's repository](https://github.com/corca-ai/awesome-llm-security) and [SWE-bench's repository](https://github.com/SWE-bench/SWE-bench).

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [SWE-bench](/tools/swe-bench-swe-bench.md) |
| --- | --- | --- |
| Tagline | A curation of tools, documents and projects about LLM Security | Benchmark for assessing language models' capability to resolve real-world Github issues |
| Stars | 1,672 | 5,576 |
| Forks | 312 | 930 |
| Open issues | 173 | 131 |
| 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 | SWE-bench serves as a benchmark for assessing how well language models can tackle real-world software engineering issues from GitHub. |
| Persona | - | - |
| Runtime | - | - |
| License | - | The tool operates under the MIT license, detailed in LICENSE.md. |
| 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) | [SWE-bench](/tools/swe-bench-swe-bench.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Active (82%) |
| Days since push | 351d | 9d |
| Open issues (now) | 173 | 131 |
| Full report | [trust report](/tools/corca-ai-awesome-llm-security/trust.md) | [trust report](/tools/swe-bench-swe-bench/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: SWE-bench

- **Adopt for:** SWE-bench serves as a benchmark for assessing how well language models can tackle real-world software engineering issues from GitHub.
- **License detail:** The tool operates under the MIT license, detailed in LICENSE.md.

## 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 SWE-bench if…

- Tags unique to SWE-bench: benchmark, language-model, software-engineering.
- When you need to evaluate the effectiveness of your language model in resolving practical software engineering challenges found in open-source repositories like GitHub.
- More GitHub stars (5.6k vs 1.7k) - visibility, not fit.

## 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 SWE-bench

- Do not use SWE-bench if your language model's primary application is outside the context of real-world GitHub issue resolution.
- Avoid using this tool if you are not interested in testing AI systems' capabilities across visual software domains; it's more specialized for that specific area, unlike general-purpose benchmarks.

## Common questions

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

awesome-llm-security: A curation of tools, documents and projects about LLM Security. SWE-bench: Benchmark for assessing language models' capability to resolve real-world Github issues. See the comparison table for live GitHub stats and shared categories.

### When should I choose awesome-llm-security over SWE-bench?

Choose awesome-llm-security over SWE-bench 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 SWE-bench over awesome-llm-security?

Choose SWE-bench over awesome-llm-security when Tags unique to SWE-bench: benchmark, language-model, software-engineering; When you need to evaluate the effectiveness of your language model in resolving practical software engineering challenges found in open-source repositories like GitHub; More GitHub stars (5.6k vs 1.7k) - visibility, not fit.

### 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 SWE-bench?

Do not use SWE-bench if your language model's primary application is outside the context of real-world GitHub issue resolution. Avoid using this tool if you are not interested in testing AI systems' capabilities across visual software domains; it's more specialized for that specific area, unlike general-purpose benchmarks.

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

SWE-bench has more GitHub stars (5,576 vs 1,672). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-llm-security and SWE-bench open source?

Yes - both are open-source projects on GitHub.

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

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

awesome-llm-security: Slowing. SWE-bench: Active. 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 SWE-bench?

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); [SWE-bench trust report](/tools/swe-bench-swe-bench/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/_