--- title: "Open-Prompt-Injection vs trap" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/liu00222-open-prompt-injection-vs-parameterlab-trap" tools: ["liu00222-open-prompt-injection", "parameterlab-trap"] --- # Open-Prompt-Injection vs trap *GraphCanon updated Aug 5, 2026* ## 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. [Open-Prompt-Injection](https://github.com/liu00222/Open-Prompt-Injection) reports 470 GitHub stars, 74 forks, and 14 open issues, last pushed Oct 29, 2025. [trap](https://github.com/parameterlab/trap) has 15 stars, 1 forks, and 0 open issues, last pushed Nov 20, 2024. Figures are from public GitHub metadata via [Open-Prompt-Injection's repository](https://github.com/liu00222/Open-Prompt-Injection) and [trap's repository](https://github.com/parameterlab/trap). | | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | [trap](/tools/parameterlab-trap.md) | | --- | --- | --- | | Tagline | Benchmark and toolkit for prompt injection attacks and defenses in LLMs | TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification | | Stars | 470 | 15 | | Forks | 74 | 1 | | Open issues | 14 | 0 | | Language | Python | Jupyter Notebook | | Adopt for | 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 is specialized for identifying large language models through adversarial attacks and fingerprinting techniques. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License ensures permissive use and modification of TRAP under its terms. | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | [trap](/tools/parameterlab-trap.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 279d | 622d | | Open issues (now) | 14 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/liu00222-open-prompt-injection/trust.md) | [trust report](/tools/parameterlab-trap/trust.md) | ## Shared compatibility - **Python**: [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) - Python runtime; [trap](/tools/parameterlab-trap.md) - Python runtime ## Decision facts: Open-Prompt-Injection - **Adopt for:** 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. ## Decision facts: trap - **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`. - **Adopt for:** TRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques. - **License detail:** MIT License ensures permissive use and modification of TRAP under its terms. ## Choose when ### 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. ### 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 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 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. ## 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 (470 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](/tools/liu00222-open-prompt-injection/alternatives) and [trap alternatives](/tools/parameterlab-trap/alternatives) ([Open-Prompt-Injection markdown twin](/tools/liu00222-open-prompt-injection/alternatives.md), [trap markdown twin](/tools/parameterlab-trap/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/liu00222-open-prompt-injection-vs-parameterlab-trap.md) 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: Slowing. 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](/tools/liu00222-open-prompt-injection/trust); [trap trust report](/tools/parameterlab-trap/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=liu00222-open-prompt-injection`](/api/graphcanon/graph?tool=liu00222-open-prompt-injection) - 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/_