Comparison
agentdojo vs Open-Prompt-Injection
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 Open-Prompt-Injection if open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs.
Markdown twin · agentdojo alternatives · Open-Prompt-Injection alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | agentdojo | Open-Prompt-Injection |
|---|---|---|
| Maintenance | Steady (63d since push) As of 2w · github_public_v1 | Slowing (279d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · 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
- Open-Prompt-Injection
- Benchmark and toolkit for prompt injection attacks and defenses in LLMs
Stars
- agentdojo
- 716
- Open-Prompt-Injection
- 470
Forks
- agentdojo
- 188
- Open-Prompt-Injection
- 74
Open issues
- agentdojo
- 41
- Open-Prompt-Injection
- 14
Language
- agentdojo
- Python
- Open-Prompt-Injection
- Python
Adopt for
- agentdojo
- AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
- Open-Prompt-Injection
- Open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs.
Persona
- agentdojo
- -
- Open-Prompt-Injection
- -
Runtime
- agentdojo
- -
- Open-Prompt-Injection
- -
License
- agentdojo
- MIT
- Open-Prompt-Injection
- MIT
Last pushed
- agentdojo
- Jun 2, 2026
- Open-Prompt-Injection
- Oct 29, 2025
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- Open-Prompt-Injection
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- agentdojo
- Steady (60%)
- Open-Prompt-Injection
- Slowing (36%)
Days since push
- agentdojo
- 63d
- Open-Prompt-Injection
- 279d
Open issues (now)
- agentdojo
- 41
- Open-Prompt-Injection
- 14
Owner type
- agentdojo
- Organization
- Open-Prompt-Injection
- User
Full report
- agentdojo
- Trust report
- Open-Prompt-Injection
- Trust report
Shared compatibility
- Python · agentdojo: Python runtime · Open-Prompt-Injection: Python runtime
Choose agentdojo if…
- 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.
- Also covers AI Agents.
- 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 Open-Prompt-Injection if…
- Tags unique to Open-Prompt-Injection: llm, llm security, security-and-privacy.
- Also covers LLM Frameworks.
- You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications.
When NOT to use Open-Prompt-Injection
- You require broader, more generalized security features not centered on prompt injection attacks.
- Your project does not involve working with Google PaLM2 or other specific models like Meta's Llama and OpenAI's GPT.
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 (liu00222/Open-Prompt-Injection) · observed Aug 5, 2026
- GitHub forks (liu00222/Open-Prompt-Injection) · observed Aug 5, 2026
- Last push (liu00222/Open-Prompt-Injection) · observed Oct 29, 2025
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentdojo 716 · Open-Prompt-Injection 470 (synced Aug 5, 2026).
Common questions
- What is the difference between agentdojo and Open-Prompt-Injection?
- agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. Open-Prompt-Injection: Benchmark and toolkit for prompt injection attacks and defenses in LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentdojo over Open-Prompt-Injection?
- Choose agentdojo over Open-Prompt-Injection when 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; Also covers AI Agents; 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 Open-Prompt-Injection over agentdojo?
- Choose Open-Prompt-Injection over agentdojo when Tags unique to Open-Prompt-Injection: llm, llm security, security-and-privacy; Also covers LLM Frameworks; You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications.
- 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 Open-Prompt-Injection?
- You require broader, more generalized security features not centered on prompt injection attacks. Your project does not involve working with Google PaLM2 or other specific models like Meta's Llama and OpenAI's GPT.
- Is agentdojo or Open-Prompt-Injection more popular on GitHub?
- agentdojo has more GitHub stars (716 vs 470). Stars measure visibility, not whether either tool fits your constraints.
- Are agentdojo and Open-Prompt-Injection open source?
- Yes - both are open-source projects on GitHub (agentdojo: MIT, Open-Prompt-Injection: MIT).
- Where can I find alternatives to agentdojo or Open-Prompt-Injection?
- GraphCanon lists graph-backed alternatives at agentdojo alternatives and Open-Prompt-Injection alternatives (agentdojo markdown twin, Open-Prompt-Injection 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 Open-Prompt-Injection?
- agentdojo: Steady. Open-Prompt-Injection: Slowing. 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 Open-Prompt-Injection?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; Open-Prompt-Injection trust report.