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Comparison

agentdojo vs trap

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 trap if tRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques.

Markdown twin · agentdojo alternatives · trap alternatives

GraphCanon updated 2w

agentdojo logo

agentdojo

ethz-spylab/agentdojo

★ 716pushed Jun 2, 2026
vs
trap logo

trap

parameterlab/trap

★ 15pushed Nov 20, 2024

Trust & integrity

Signalagentdojotrap
Maintenance
Steady (63d since push)
As of 2w · github_public_v1
Dormant (622d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Organization account
As of 2w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
Published findings
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
agentdojo trust reporttrap trust reportTrust methodology

Tagline

agentdojo
A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
trap
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification

Stars

agentdojo
716
trap
15

Forks

agentdojo
188
trap
1

Open issues

agentdojo
41
trap
0

Language

agentdojo
Python
trap
Jupyter Notebook

Adopt for

agentdojo
AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
trap
TRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques.

Persona

agentdojo
-
trap
-

Runtime

agentdojo
-
trap
-

License

agentdojo
MIT
trap
MIT License ensures permissive use and modification of TRAP under its terms.

Last pushed

agentdojo
Jun 2, 2026
trap
Nov 20, 2024

Categories

agentdojo
AI Agents, Evaluation & Observability
trap
Evaluation & Observability, LLM Frameworks

Trust and health

Maintenance

agentdojo
Steady (60%)
trap
Dormant (18%)

Days since push

agentdojo
63d
trap
622d

Open issues (now)

agentdojo
41
trap
0

OSV dependency advisories

agentdojo
No lockfile (source not queried)
trap
Published findings

Full report

agentdojo
Trust report
trap
Trust report

Shared compatibility

  • Python · agentdojo: Python runtime · trap: Python runtime

Choose agentdojo if…

  • agentdojo is primarily Python; trap is Jupyter Notebook.
  • 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, prompt-injection, 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.
GitHubDetails

Choose trap if…

  • trap is primarily Jupyter Notebook; agentdojo is Python.
  • Requirements: Requires installation and use of HuggingFace transformers for downloading specific models.; Configuration files need to be adapted with the correct paths for model configurations as specified in `detect_llm/configs`..
  • Tags unique to trap: acl2024, adversarial-attacks, fingerprinting, research.
  • Also covers LLM Frameworks.
  • When you need to perform black-box identification of large language models using adversarial prompt techniques in research settings.

When NOT to use trap

  • If your objective is not specifically related to identifying or evaluating LLMs through adversarial attacks, and you require a more generalized framework for LLM evaluation or observability.
  • When working with models that cannot be subjected to black-box testing due to their deployment environment or company policies.
GitHubDetails

Explore

agentdojo trust report →trap trust report →AI Agents category →Evaluation & Observability category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: agentdojo 716 · trap 15 (synced Aug 5, 2026).

Common questions

What is the difference between agentdojo and trap?
agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. trap: TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification. See the comparison table for live GitHub stats and shared categories.
When should I choose agentdojo over trap?
Choose agentdojo over trap when agentdojo is primarily Python; trap is Jupyter Notebook; 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, prompt-injection, 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 trap over agentdojo?
Choose trap over agentdojo when trap is primarily Jupyter Notebook; agentdojo is Python; Requirements: Requires installation and use of HuggingFace transformers for downloading specific models.; Configuration files need to be adapted with the correct paths for model configurations as specified in detect_llm/configs.; Tags unique to trap: acl2024, adversarial-attacks, fingerprinting, research; Also covers LLM Frameworks; When you need to perform black-box identification of large language models using adversarial prompt techniques in research settings.
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 trap?
If your objective is not specifically related to identifying or evaluating LLMs through adversarial attacks, and you require a more generalized framework for LLM evaluation or observability. When working with models that cannot be subjected to black-box testing due to their deployment environment or company policies.
Is agentdojo or trap more popular on GitHub?
agentdojo has more GitHub stars (716 vs 15). Stars measure visibility, not whether either tool fits your constraints.
Are agentdojo and trap open source?
Yes - both are open-source projects on GitHub (agentdojo: MIT, trap: MIT).
Where can I find alternatives to agentdojo or trap?
GraphCanon lists graph-backed alternatives at agentdojo alternatives and trap alternatives (agentdojo markdown twin, trap 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 trap?
agentdojo: Steady. trap: Dormant. 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 trap?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; trap trust report.

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