Comparison
agentdojo vs Awesome-LLMSecOps
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.
Markdown twin · agentdojo alternatives · Awesome-LLMSecOps alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | agentdojo | Awesome-LLMSecOps |
|---|---|---|
| Maintenance | Steady (63d since push) As of 2w · github_public_v1 | Very active (4d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 1w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- agentdojo
- A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
- Awesome-LLMSecOps
- Curated security resources for LLM operations
Stars
- agentdojo
- 716
- Awesome-LLMSecOps
- 150
Forks
- agentdojo
- 188
- Awesome-LLMSecOps
- 63
Open issues
- agentdojo
- 41
- Awesome-LLMSecOps
- 11
Language
- agentdojo
- Python
- Awesome-LLMSecOps
- HTML
Adopt for
- agentdojo
- AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
- Awesome-LLMSecOps
- Awesome-LLMSecOps is a curated list that emphasizes practical security implementation for the operations of large language models.
Persona
- agentdojo
- -
- Awesome-LLMSecOps
- -
Runtime
- agentdojo
- -
- Awesome-LLMSecOps
- -
License
- agentdojo
- MIT
- Awesome-LLMSecOps
- -
Last pushed
- agentdojo
- Jun 2, 2026
- Awesome-LLMSecOps
- Aug 4, 2026
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- Awesome-LLMSecOps
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- agentdojo
- Steady (60%)
- Awesome-LLMSecOps
- Very active (96%)
Days since push
- agentdojo
- 63d
- Awesome-LLMSecOps
- 4d
Open issues (now)
- agentdojo
- 41
- Awesome-LLMSecOps
- 11
Owner type
- agentdojo
- Organization
- Awesome-LLMSecOps
- User
Full report
- agentdojo
- Trust report
- Awesome-LLMSecOps
- Trust report
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.
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.
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 Awesome-LLMSecOps
- Looking for extensive academic references or ArXiv papers in descriptions
- Require real-time interactive tools rather than curated static lists of resources
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ethz-spylab/agentdojo) · observed Aug 5, 2026
- GitHub forks (ethz-spylab/agentdojo) · observed Aug 5, 2026
- Last push (ethz-spylab/agentdojo) · observed Jun 2, 2026
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (wearetyomsmnv/Awesome-LLMSecOps) · observed Aug 9, 2026
- GitHub forks (wearetyomsmnv/Awesome-LLMSecOps) · observed Aug 9, 2026
- Last push (wearetyomsmnv/Awesome-LLMSecOps) · observed Aug 4, 2026
- License file (unknown) · observed Aug 9, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: agentdojo 716 · Awesome-LLMSecOps 150 (synced Aug 5, 2026).
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 and Awesome-LLMSecOps alternatives (agentdojo markdown twin, Awesome-LLMSecOps markdown twin), 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 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; Awesome-LLMSecOps trust report.