--- title: "llm-attacks vs trap" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/llm-attacks-llm-attacks-vs-parameterlab-trap" tools: ["llm-attacks-llm-attacks", "parameterlab-trap"] --- # llm-attacks vs trap *GraphCanon updated Aug 5, 2026* ## Verdict Pick llm-attacks if llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat; pick trap if tRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques. [llm-attacks](https://llm-attacks.org/) reports 4.8k GitHub stars, 633 forks, and 69 open issues, last pushed Aug 2, 2024. [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 [llm-attacks's repository](https://github.com/llm-attacks/llm-attacks) and [trap's repository](https://github.com/parameterlab/trap). | | [llm-attacks](/tools/llm-attacks-llm-attacks.md) | [trap](/tools/parameterlab-trap.md) | | --- | --- | --- | | Tagline | Universal and Transferable Attacks on Aligned Language Models | TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification | | Stars | 4,756 | 15 | | Forks | 633 | 1 | | Open issues | 69 | 0 | | Language | Python | Jupyter Notebook | | Adopt for | llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat. | 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._ | | [llm-attacks](/tools/llm-attacks-llm-attacks.md) | [trap](/tools/parameterlab-trap.md) | | --- | --- | --- | | Days since push | 732d | 622d | | Open issues (now) | 69 | 0 | | Full report | [trust report](/tools/llm-attacks-llm-attacks/trust.md) | [trust report](/tools/parameterlab-trap/trust.md) | ## Shared compatibility - **Python**: [llm-attacks](/tools/llm-attacks-llm-attacks.md) - Python runtime; [trap](/tools/parameterlab-trap.md) - Python runtime ## Decision facts: llm-attacks - **Adopt for:** llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat. ## 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 llm-attacks if… - llm-attacks is primarily Python; trap is Jupyter Notebook. - Tags unique to llm-attacks: alignment-testing, attacks, fastchat-dependency, language-models. - When you need to test the robustness of aligned language models specifically using attacks designed for these systems, ### Choose trap if… - trap is primarily Jupyter Notebook; llm-attacks 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 llm-attacks - Do not use if you are evaluating generic or unaligned language models without a need for alignment-specific attack testing, - Avoid when FastChat is not used in your project as llm-attacks explicitly depends on it. ## 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 llm-attacks and trap? llm-attacks: Universal and Transferable Attacks on Aligned Language Models. 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 llm-attacks over trap? Choose llm-attacks over trap when llm-attacks is primarily Python; trap is Jupyter Notebook; Tags unique to llm-attacks: alignment-testing, attacks, fastchat-dependency, language-models; When you need to test the robustness of aligned language models specifically using attacks designed for these systems,. ### When should I choose trap over llm-attacks? Choose trap over llm-attacks when trap is primarily Jupyter Notebook; llm-attacks 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 llm-attacks? Do not use if you are evaluating generic or unaligned language models without a need for alignment-specific attack testing, Avoid when FastChat is not used in your project as llm-attacks explicitly depends on it. ### 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 llm-attacks or trap more popular on GitHub? llm-attacks has more GitHub stars (4,756 vs 15). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-attacks and trap open source? Yes - both are open-source projects on GitHub (llm-attacks: MIT, trap: MIT). ### Where can I find alternatives to llm-attacks or trap? GraphCanon lists graph-backed alternatives at [llm-attacks alternatives](/tools/llm-attacks-llm-attacks/alternatives) and [trap alternatives](/tools/parameterlab-trap/alternatives) ([llm-attacks markdown twin](/tools/llm-attacks-llm-attacks/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/llm-attacks-llm-attacks-vs-parameterlab-trap.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-attacks or trap? llm-attacks: Dormant. 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 llm-attacks and trap? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-attacks trust report](/tools/llm-attacks-llm-attacks/trust); [trap trust report](/tools/parameterlab-trap/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=llm-attacks-llm-attacks`](/api/graphcanon/graph?tool=llm-attacks-llm-attacks) - 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/_