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Comparison

llm-attacks vs trap

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.

Markdown twin · llm-attacks alternatives · trap alternatives

GraphCanon updated 2w

llm-attacks logo

llm-attacks

llm-attacks/llm-attacks

★ 4.8kpushed Aug 2, 2024
vs
trap logo

trap

parameterlab/trap

★ 15pushed Nov 20, 2024

Trust & integrity

Signalllm-attackstrap
Maintenance
Dormant (732d 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
Published findings
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
llm-attacks trust reporttrap trust reportTrust methodology

Tagline

llm-attacks
Universal and Transferable Attacks on Aligned Language Models
trap
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification

Stars

llm-attacks
4.8k
trap
15

Forks

llm-attacks
633
trap
1

Open issues

llm-attacks
69
trap
0

Language

llm-attacks
Python
trap
Jupyter Notebook

Adopt for

llm-attacks
llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat.
trap
TRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques.

Persona

llm-attacks
-
trap
-

Runtime

llm-attacks
-
trap
-

License

llm-attacks
MIT
trap
MIT License ensures permissive use and modification of TRAP under its terms.

Last pushed

llm-attacks
Aug 2, 2024
trap
Nov 20, 2024

Categories

llm-attacks
Evaluation & Observability, LLM Frameworks
trap
Evaluation & Observability, LLM Frameworks

Trust and health

Days since push

llm-attacks
732d
trap
622d

Open issues (now)

llm-attacks
69
trap
0

Full report

llm-attacks
Trust report
trap
Trust report

Shared compatibility

  • Python · llm-attacks: Python runtime · trap: Python runtime

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,

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.
GitHubDetails

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 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

llm-attacks trust report →trap trust report →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: llm-attacks 4.8k · trap 15 (synced Aug 5, 2026).

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 and trap alternatives (llm-attacks 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, 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; trap trust report.

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