--- title: "garak vs Awesome-LLMSecOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-garak-vs-wearetyomsmnv-awesome-llmsecops" tools: ["nvidia-garak", "wearetyomsmnv-awesome-llmsecops"] --- # garak vs Awesome-LLMSecOps *GraphCanon updated Aug 9, 2026* ## Verdict Pick garak if lLM Vulnerability Scanner; pick Awesome-LLMSecOps if awesome-LLMSecOps is a curated list that emphasizes practical security implementation for the operations of large language models. [garak](https://discord.gg/uVch4puUCs) reports 8.7k GitHub stars, 1.1k forks, and 374 open issues, last pushed Aug 4, 2026. [Awesome-LLMSecOps](https://github.com/wearetyomsmnv/Awesome-LLMSecOps) has 150 stars, 63 forks, and 11 open issues, last pushed Aug 4, 2026. Figures are from public GitHub metadata via [garak's repository](https://github.com/NVIDIA/garak) and [Awesome-LLMSecOps's repository](https://github.com/wearetyomsmnv/Awesome-LLMSecOps). | | [garak](/tools/nvidia-garak.md) | [Awesome-LLMSecOps](/tools/wearetyomsmnv-awesome-llmsecops.md) | | --- | --- | --- | | Tagline | LLM vulnerability scanner | Curated security resources for LLM operations | | Stars | 8,696 | 150 | | Forks | 1,145 | 63 | | Open issues | 374 | 11 | | Language | Python | HTML | | Adopt for | LLM Vulnerability Scanner | Awesome-LLMSecOps is a curated list that emphasizes practical security implementation for the operations of large language models. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [garak](/tools/nvidia-garak.md) | [Awesome-LLMSecOps](/tools/wearetyomsmnv-awesome-llmsecops.md) | | --- | --- | --- | | Days since push | 0d | 4d | | Open issues (now) | 374 | 11 | | Owner type | Organization | User | | Full report | [trust report](/tools/nvidia-garak/trust.md) | [trust report](/tools/wearetyomsmnv-awesome-llmsecops/trust.md) | ## Decision facts: garak - **Adopt for:** LLM Vulnerability Scanner - **License detail:** Apache-2.0 ## Decision facts: Awesome-LLMSecOps - **Adopt for:** Awesome-LLMSecOps is a curated list that emphasizes practical security implementation for the operations of large language models. ## Choose when ### Choose garak if… - garak is primarily Python; Awesome-LLMSecOps is HTML. - Tags unique to garak: ai, llm-evaluation, security-scanners, vulnerability-assessment. - When needing to assess the security and reliability of language models specifically using a tool developed by NVIDIA. ### Choose Awesome-LLMSecOps if… - Awesome-LLMSecOps is primarily HTML; garak is Python. - Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, llm-red-teaming, prompt-injection. - Also covers AI Agents. - Need a specialized focus on LLM-specific security threats like recursive pollution and prompt manipulation ## When NOT to use garak - When the targeted model is not supported by 'garak', such as some specialized API-based generators that need specific configurations beyond setting environment variables. - If real-time updates are necessary, as the PyPI version of garak might lag behind the development version available on GitHub. ## When NOT to use Awesome-LLMSecOps - Looking for extensive academic references or ArXiv papers in descriptions - Require real-time interactive tools rather than curated static lists of resources ## Common questions ### What is the difference between garak and Awesome-LLMSecOps? garak: LLM vulnerability scanner. Awesome-LLMSecOps: Curated security resources for LLM operations. See the comparison table for live GitHub stats and shared categories. ### When should I choose garak over Awesome-LLMSecOps? Choose garak over Awesome-LLMSecOps when garak is primarily Python; Awesome-LLMSecOps is HTML; Tags unique to garak: ai, llm-evaluation, security-scanners, vulnerability-assessment; When needing to assess the security and reliability of language models specifically using a tool developed by NVIDIA. ### When should I choose Awesome-LLMSecOps over garak? Choose Awesome-LLMSecOps over garak when Awesome-LLMSecOps is primarily HTML; garak is Python; Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, llm-red-teaming, prompt-injection; Also covers AI Agents; Need a specialized focus on LLM-specific security threats like recursive pollution and prompt manipulation. ### When should I avoid garak? When the targeted model is not supported by 'garak', such as some specialized API-based generators that need specific configurations beyond setting environment variables. If real-time updates are necessary, as the PyPI version of garak might lag behind the development version available on GitHub. ### When should I avoid Awesome-LLMSecOps? Looking for extensive academic references or ArXiv papers in descriptions Require real-time interactive tools rather than curated static lists of resources ### Is garak or Awesome-LLMSecOps more popular on GitHub? garak has more GitHub stars (8,696 vs 150). Stars measure visibility, not whether either tool fits your constraints. ### Are garak and Awesome-LLMSecOps open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to garak or Awesome-LLMSecOps? GraphCanon lists graph-backed alternatives at [garak alternatives](/tools/nvidia-garak/alternatives) and [Awesome-LLMSecOps alternatives](/tools/wearetyomsmnv-awesome-llmsecops/alternatives) ([garak markdown twin](/tools/nvidia-garak/alternatives.md), [Awesome-LLMSecOps markdown twin](/tools/wearetyomsmnv-awesome-llmsecops/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/nvidia-garak-vs-wearetyomsmnv-awesome-llmsecops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, garak or Awesome-LLMSecOps? garak: Very active. Awesome-LLMSecOps: 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 garak and Awesome-LLMSecOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [garak trust report](/tools/nvidia-garak/trust); [Awesome-LLMSecOps trust report](/tools/wearetyomsmnv-awesome-llmsecops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-garak`](/api/graphcanon/graph?tool=nvidia-garak) - 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/_