GraphCanon updated Oct 5, 2026 · GitHub synced Oct 5, 2026
38views this month
Decision brief
TRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques.
Good fit when
- When you need to perform black-box identification of large language models using adversarial prompt techniques in research settings.
- For scenarios where existing model watermarking or fingerprinting methods are insufficiently robust.
Avoid when
- 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.
- 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`.
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (683d since push)
- As of Oct 5, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 5, 2026
- Security (OSV)
- 242 low (242 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/parameterlab/trapSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Research focused on identifying large language models using adversarial attacks and fingerprinting techniques.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Oct 5, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Oct 5, 2026)
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('weSource link
Tags
README
test HF installation python c "from transformers import pipeline; print(pipeline('sentiment analysis')('we love you'))" python from transformers import AutoTokenizer, AutoModelForCausalLM MODELS NAMES = [ "meta llama/Llama 2 7b chat hf", "meta llama/Llama 2 13b chat hf", "lmsys/v...
For agents
This page has a .md twin and JSON over the API.