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

# awesome-llm-security vs openlit

*GraphCanon updated Jul 12, 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 openlit if decision-critical facts for OpenLIT are centered around its unique features in LLM observability, GPU monitoring, and extensive integration capabilities.

[awesome-llm-security](https://github.com/corca-ai/awesome-llm-security) reports 1.6k GitHub stars, 294 forks, and 161 open issues, last pushed Aug 20, 2025. [openlit](https://docs.openlit.io) has 2.6k stars, 321 forks, and 57 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [awesome-llm-security's repository](https://github.com/corca-ai/awesome-llm-security) and [openlit's repository](https://github.com/openlit/openlit).

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [openlit](/tools/openlit-openlit.md) |
| --- | --- | --- |
| Tagline | A curation of tools, documents and projects about LLM Security | A comprehensive open-source platform for AI Engineering with LLM Observability, Monitoring, and Management |
| Stars | 1,637 | 2,587 |
| Forks | 294 | 321 |
| Open issues | 161 | 57 |
| 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 | Decision-critical facts for OpenLIT are centered around its unique features in LLM observability, GPU monitoring, and extensive integration capabilities. |
| Persona | - | - |
| Runtime | - | - |
| License | - | Apache-2.0 |
| Categories | Evaluation & Observability | Evaluation & Observability, Inference & Serving |

## Trust and health

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

| | [awesome-llm-security](/tools/corca-ai-awesome-llm-security.md) | [openlit](/tools/openlit-openlit.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Very active (96%) |
| Days since push | 325d | 1d |
| Open issues (now) | 161 | 57 |
| Full report | [trust report](/tools/corca-ai-awesome-llm-security/trust.md) | [trust report](/tools/openlit-openlit/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: openlit

- **Pricing:** freemium
- **Adopt for:** Decision-critical facts for OpenLIT are centered around its unique features in LLM observability, GPU monitoring, and extensive integration capabilities.
- **License detail:** Apache-2.0

## 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: llm, awesome-list, 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 openlit if…

- Tags unique to openlit: llmops, gpu-monitoring, monitoring-tool, ai-observability.
- Also covers Inference & Serving.
- openlit ships Docker support for self-hosted deployment.
- When you need comprehensive observability features native to OpenTelemetry, allowing seamless trace and metric management with an out-of-the-box solution.

## 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 openlit

- If your project strictly requires a proprietary tool or if you have specific requirements that are not covered by OpenLIT's integrations, such as unique vector databases not yet supported.
- When the team lacks the expertise in TypeScript or Python SDK to efficiently manage and implement observability into their current workflows with OpenLIT.

## Common questions

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

awesome-llm-security: A curation of tools, documents and projects about LLM Security. openlit: A comprehensive open-source platform for AI Engineering with LLM Observability, Monitoring, and Management. See the comparison table for live GitHub stats and shared categories.

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

Choose awesome-llm-security over openlit 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: llm, awesome-list, 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 openlit over awesome-llm-security?

Choose openlit over awesome-llm-security when Tags unique to openlit: llmops, gpu-monitoring, monitoring-tool, ai-observability; Also covers Inference & Serving; openlit ships Docker support for self-hosted deployment; When you need comprehensive observability features native to OpenTelemetry, allowing seamless trace and metric management with an out-of-the-box solution.

### 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 openlit?

If your project strictly requires a proprietary tool or if you have specific requirements that are not covered by OpenLIT's integrations, such as unique vector databases not yet supported. When the team lacks the expertise in TypeScript or Python SDK to efficiently manage and implement observability into their current workflows with OpenLIT.

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

openlit has more GitHub stars (2,587 vs 1,637). Stars measure visibility, not whether either tool fits your constraints.

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

Yes - both are open-source projects on GitHub.

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

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

awesome-llm-security: Slowing. openlit: Very 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 openlit?

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); [openlit trust report](/tools/openlit-openlit/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/_