Comparison
Open-Prompt-Injection vs trap
Verdict
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; pick trap if tRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques.
Markdown twin · Open-Prompt-Injection alternatives · trap alternatives
GraphCanon updated Oct 5, 2026
5views this month
vs
Trust & integrity
| Signal | Open-Prompt-Injection | trap |
|---|---|---|
| Maintenance | Very active (6d since push) As of Oct 4, 2026 · github_public_v1 | Dormant (683d since push) As of Oct 5, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 5, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · 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
- Open-Prompt-Injection
- Benchmark and toolkit for prompt injection attacks and defenses in LLMs
- trap
- TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
Stars
- Open-Prompt-Injection
- 503
- trap
- 15
Forks
- Open-Prompt-Injection
- 81
- trap
- 1
Open issues
- Open-Prompt-Injection
- 2
- trap
- 0
Language
- Open-Prompt-Injection
- Python
- trap
- Jupyter Notebook
Adopt for
- 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.
- trap
- TRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques.
Persona
- Open-Prompt-Injection
- -
- trap
- -
Runtime
- Open-Prompt-Injection
- -
- trap
- -
License
- Open-Prompt-Injection
- MIT
- trap
- MIT License ensures permissive use and modification of TRAP under its terms.
Last pushed
- Open-Prompt-Injection
- Sep 27, 2026
- trap
- Nov 20, 2024
Categories
- Open-Prompt-Injection
- Evaluation & Observability, LLM Frameworks
- trap
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- Open-Prompt-Injection
- Very active (96%)
- trap
- Dormant (18%)
Days since push
- Open-Prompt-Injection
- 6d
- trap
- 683d
Open issues (now)
- Open-Prompt-Injection
- 2
- trap
- 0
Stars delta
- Open-Prompt-Injection
- +33 (30d)
- trap
- 0 (30d)
Open issues delta
- Open-Prompt-Injection
- -12 (30d)
- trap
- 0 (30d)
Owner type
- Open-Prompt-Injection
- User
- trap
- Organization
OSV dependency advisories
- Open-Prompt-Injection
- No lockfile (source not queried)
- trap
- Published findings
Full report
- Open-Prompt-Injection
- Trust report
- trap
- Trust report
Shared compatibility
- Python · Open-Prompt-Injection: Python runtime · trap: Python runtime
Choose Open-Prompt-Injection if…
- Open-Prompt-Injection is primarily Python; trap is Jupyter Notebook.
- Tags unique to Open-Prompt-Injection: llm, llm-security, prompt-injection, security-and-privacy.
- 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.
Choose trap if…
- trap is primarily Jupyter Notebook; Open-Prompt-Injection 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, large-language-models.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (liu00222/Open-Prompt-Injection) · observed Oct 4, 2026
- GitHub forks (liu00222/Open-Prompt-Injection) · observed Oct 4, 2026
- Last push (liu00222/Open-Prompt-Injection) · observed Sep 27, 2026
- License file (MIT) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (parameterlab/trap) · observed Oct 5, 2026
- GitHub forks (parameterlab/trap) · observed Oct 5, 2026
- Last push (parameterlab/trap) · observed Nov 20, 2024
- License file (MIT) · observed Oct 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Open-Prompt-Injection 503 · trap 15 (synced Oct 4, 2026).
Common questions
- What is the difference between Open-Prompt-Injection and trap?
- Open-Prompt-Injection: Benchmark and toolkit for prompt injection attacks and defenses in LLMs. 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 Open-Prompt-Injection over trap?
- Choose Open-Prompt-Injection over trap when Open-Prompt-Injection is primarily Python; trap is Jupyter Notebook; Tags unique to Open-Prompt-Injection: llm, llm-security, prompt-injection, security-and-privacy; You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications.
- When should I choose trap over Open-Prompt-Injection?
- Choose trap over Open-Prompt-Injection when trap is primarily Jupyter Notebook; Open-Prompt-Injection 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, large-language-models; When you need to perform black-box identification of large language models using adversarial prompt techniques in research settings. - 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.
- 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 Open-Prompt-Injection or trap more popular on GitHub?
- Open-Prompt-Injection has more GitHub stars (503 vs 15). Stars measure visibility, not whether either tool fits your constraints.
- Are Open-Prompt-Injection and trap open source?
- Yes - both are open-source projects on GitHub (Open-Prompt-Injection: MIT, trap: MIT).
- Where can I find alternatives to Open-Prompt-Injection or trap?
- GraphCanon lists graph-backed alternatives at Open-Prompt-Injection alternatives and trap alternatives (Open-Prompt-Injection 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, Open-Prompt-Injection or trap?
- Open-Prompt-Injection: Very active. 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 Open-Prompt-Injection and trap?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Open-Prompt-Injection trust report; trap trust report.