--- title: "agentdojo vs Awesome-LLMSecOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-wearetyomsmnv-awesome-llmsecops" tools: ["ethz-spylab-agentdojo", "wearetyomsmnv-awesome-llmsecops"] --- # agentdojo vs Awesome-LLMSecOps *GraphCanon updated Aug 9, 2026* ## Verdict Pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents; pick Awesome-LLMSecOps if awesome-LLMSecOps is a curated list that emphasizes practical security implementation for the operations of large language models. [agentdojo](https://agentdojo.spylab.ai/) reports 716 GitHub stars, 188 forks, and 41 open issues, last pushed Jun 2, 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 [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [Awesome-LLMSecOps's repository](https://github.com/wearetyomsmnv/Awesome-LLMSecOps). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [Awesome-LLMSecOps](/tools/wearetyomsmnv-awesome-llmsecops.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | Curated security resources for LLM operations | | Stars | 716 | 150 | | Forks | 188 | 63 | | Open issues | 41 | 11 | | Language | Python | HTML | | Adopt for | AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. | Awesome-LLMSecOps is a curated list that emphasizes practical security implementation for the operations of large language models. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [Awesome-LLMSecOps](/tools/wearetyomsmnv-awesome-llmsecops.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 63d | 4d | | Open issues (now) | 41 | 11 | | Owner type | Organization | User | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/wearetyomsmnv-awesome-llmsecops/trust.md) | ## Decision facts: agentdojo - **Pricing:** freemium - Open-source under the MIT License. Some advanced features might require additional libraries or APIs. - **Requirements:** Min 8 GB RAM - **Adopt for:** AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ## 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 agentdojo if… - agentdojo is primarily Python; Awesome-LLMSecOps is HTML. - Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs.. - Requirements: Min 8 GB RAM. - Tags unique to agentdojo: benchmark, large language models, security. - AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ### Choose Awesome-LLMSecOps if… - Awesome-LLMSecOps is primarily HTML; agentdojo is Python. - Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, llm-red-teaming, rag-security. - Need a specialized focus on LLM-specific security threats like recursive pollution and prompt manipulation ## When NOT to use agentdojo - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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 agentdojo and Awesome-LLMSecOps? agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. Awesome-LLMSecOps: Curated security resources for LLM operations. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentdojo over Awesome-LLMSecOps? Choose agentdojo over Awesome-LLMSecOps when agentdojo is primarily Python; Awesome-LLMSecOps is HTML; Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs.; Requirements: Min 8 GB RAM; Tags unique to agentdojo: benchmark, large language models, security; AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ### When should I choose Awesome-LLMSecOps over agentdojo? Choose Awesome-LLMSecOps over agentdojo when Awesome-LLMSecOps is primarily HTML; agentdojo is Python; Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, llm-red-teaming, rag-security; Need a specialized focus on LLM-specific security threats like recursive pollution and prompt manipulation. ### When should I avoid agentdojo? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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 agentdojo or Awesome-LLMSecOps more popular on GitHub? agentdojo has more GitHub stars (716 vs 150). Stars measure visibility, not whether either tool fits your constraints. ### Are agentdojo and Awesome-LLMSecOps open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to agentdojo or Awesome-LLMSecOps? GraphCanon lists graph-backed alternatives at [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) and [Awesome-LLMSecOps alternatives](/tools/wearetyomsmnv-awesome-llmsecops/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/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/ethz-spylab-agentdojo-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, agentdojo or Awesome-LLMSecOps? agentdojo: Steady. 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 agentdojo and Awesome-LLMSecOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust); [Awesome-LLMSecOps trust report](/tools/wearetyomsmnv-awesome-llmsecops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ethz-spylab-agentdojo`](/api/graphcanon/graph?tool=ethz-spylab-agentdojo) - 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/_