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

# awesome-llm-security vs baseline-defenses

*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 baseline-defenses if a toolkit for evaluating defenses against adversarial attacks on aligned language models, focusing on perplexity filter and paraphrase defense strategies.

[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. [baseline-defenses](https://github.com/neelsjain/baseline-defenses) has 34 stars, 1 forks, and 0 open issues, last pushed Oct 26, 2023. Figures are from public GitHub metadata via [awesome-llm-security's repository](https://github.com/corca-ai/awesome-llm-security) and [baseline-defenses's repository](https://github.com/neelsjain/baseline-defenses).

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [baseline-defenses](/tools/neelsjain-baseline-defenses.md) |
| --- | --- | --- |
| Tagline | A curation of tools, documents and projects about LLM Security | Research code for evaluating defenses against adversarial attacks on aligned language models |
| Stars | 1,672 | 34 |
| Forks | 312 | 1 |
| 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 | A toolkit for evaluating defenses against adversarial attacks on aligned language models, focusing on perplexity filter and paraphrase defense strategies. |
| 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) | [baseline-defenses](/tools/neelsjain-baseline-defenses.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 351d | 1013d |
| Open issues (now) | 173 | 0 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/corca-ai-awesome-llm-security/trust.md) | [trust report](/tools/neelsjain-baseline-defenses/trust.md) |

## Shared compatibility

- **ChatGPT**: [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) - Works with ChatGPT; [baseline-defenses](/tools/neelsjain-baseline-defenses.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: baseline-defenses

- **Adopt for:** A toolkit for evaluating defenses against adversarial attacks on aligned language models, focusing on perplexity filter and paraphrase defense strategies.

## 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 baseline-defenses if…

- Tags unique to baseline-defenses: adversarial-attacks, defense strategies, paraphrase defense, perplexity filter.
- - When you need to evaluate the effectiveness of baseline defenses such as the perplexity filter or paraphrase defense in protecting aligned language models from adversarial attacks.
- 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 baseline-defenses

- - Do not use if you require comprehensive coverage of all possible defensive measures. This tool specifically lacks detailed code for retokenization defenses involving BPE-dropout.
- - If your scenario demands more advanced or specialized defense mechanisms beyond the scope of baseline strategies, this repository will fall short on delivering those.

## Common questions

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

awesome-llm-security: A curation of tools, documents and projects about LLM Security. baseline-defenses: Research code for evaluating defenses against adversarial attacks on aligned language models. See the comparison table for live GitHub stats and shared categories.

### When should I choose awesome-llm-security over baseline-defenses?

Choose awesome-llm-security over baseline-defenses 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 baseline-defenses over awesome-llm-security?

Choose baseline-defenses over awesome-llm-security when Tags unique to baseline-defenses: adversarial-attacks, defense strategies, paraphrase defense, perplexity filter; - When you need to evaluate the effectiveness of baseline defenses such as the perplexity filter or paraphrase defense in protecting aligned language models from adversarial attacks; 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 baseline-defenses?

- Do not use if you require comprehensive coverage of all possible defensive measures. This tool specifically lacks detailed code for retokenization defenses involving BPE-dropout. - If your scenario demands more advanced or specialized defense mechanisms beyond the scope of baseline strategies, this repository will fall short on delivering those.

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

awesome-llm-security has more GitHub stars (1,672 vs 34). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-llm-security and baseline-defenses open source?

Yes - both are open-source projects on GitHub.

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

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

awesome-llm-security: Slowing. baseline-defenses: 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 baseline-defenses?

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); [baseline-defenses trust report](/tools/neelsjain-baseline-defenses/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/_