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catalog / Language & NLP / Argilla — Collaborative Data Annotation for LLMs
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Argilla — Collaborative Data Annotation for LLMs

Open-source annotation platform for building high-quality fine-tuning and RLHF datasets; integrates with Hugging Face Hub.

@ai-supply
Installs38k
⟳ upstream v2.8.0 · updated 1y ago
↗ Source repository
← More Language & NLPLanguage & NLP leaderboard →How we grade security →Source ↗
✓ Grade A · 100/100 · SafeSecurity assessment
✓No compromise signals11capabilities surfaced9of 20 OWASP controls clear
External endpoints declaredBroad capability surfaceExternal endpoints declaredExternal endpoints declared
scanned 17d ago · partial·osv · gitleaks · opengrep · picklescan + heuristics·full breakdown in the Security tab ↓

Argilla

Argilla is a collaboration platform for AI engineers and domain experts to annotate, curate, and quality-control datasets for fine-tuning and RLHF. It provides a polished web UI for labelling, a Python SDK for programmatic data management, and native Hugging Face Hub sync.

Key Features

  • Web UI: label text, images, ranking, rating, span, multi-label tasks
  • Custom schemas: define any labelling task with Argilla's dataset settings API
  • Human + model-in-the-loop: pre-label with your model, human reviews
  • RLHF/DPO: preference ranking and comparison tasks built-in
  • Hugging Face Hub: push/pull datasets directly with rg.Dataset.from_hub()
  • REST API + Python SDK + webhook integrations
  • Self-hostable via Docker; managed via Hugging Face Spaces

Quick Start

import argilla as rg

client = rg.Argilla(api_url="http://localhost:6900", api_key="argilla.apikey")

dataset = rg.Dataset(
    name="sentiment-annotation",
    settings=rg.Settings(
        fields=[rg.TextField(name="text")],
        questions=[rg.LabelQuestion(name="label", labels=["positive", "negative", "neutral"])],
    ),
)
dataset.create()

records = [rg.Record(fields={"text": "Argilla makes annotation easy!"})]
dataset.records.log(records)

Install via ai-supply

npx ai-supply add argilla-dataset-annotation-platform

Curated mirror of the open-source Argilla (Apache-2.0). Get it from the source.

Rating rank
#1
of 30 in Language & NLP
Install rank
#18
of 30 in Language & NLP
Security score
100/100 · A
safe
Security rank
#1
of 30 in Language & NLP
Installs
38k
cat avg 145k
This listing vs category average
Installs
this
cat avg
Security (of 100)
this
cat avg
Adoption trend
See the Language & NLP leaderboard →
✓ Security: Safe · 100100/100 · grade Ascanned 17d ago
✓ no compromise signals11 risk-surface · 6/20 OWASP controls flagged

Compromise signals — malicious or tampered code (leaked secrets, backdoors, a dropped executable) — reduce the score, and known dependency CVEs carry a bounded penalty (they warrant review but never QUARANTINE — update the dependency to clear). Other dangerous-by-capability traits are risk surface, expected for some capabilities. Every finding is mapped to its OWASP control below.

What this capability can do · high confidence (static)
Tools (11)
sentimentsentiment-multi-labelreview-ratingreview-reviewrankingsplitlossfloatsplit_2split_3name
⚑ filesystem⚑ network⚑ secrets
egress → hf.co, www.apache.org, docs.github.com, keepachangelog.com, dev.argilla.io, test.pypi.org, help.github.com, docs.argilla.io +28
3 scripts

Findings mapped to the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any flagged control for the exact findings — compromise reduces the score; expected/risk-surface do not, except a known CVE, which carries a small bounded penalty (high/critical → Review).

OWASP Top 10 for LLM Applications
⚠LLM06Excessive Agencyhigh
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 1 distinct host(s) · argilla/.github/ISSUE_TEMPLATE/add_documentation_report.ymlrisk surface
•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege · argilla/.github/actions/generate-credentials/app/main.py (CWE-272)risk surface
•External endpoints declared — 2 distinct host(s) · argilla/.github/actions/slack-post-credentials/app/main.pyrisk surface
•External endpoints declared — 3 distinct host(s) · argilla/CONTRIBUTING.mdrisk surface
•External endpoints declared — 17 distinct host(s) · argilla/README.mdrisk surface
•External endpoints declared — 14 distinct host(s) · argilla/argilla-frontend/README.mdrisk surface
•Egress to a private/loopback host — 0.0.0.0 · argilla/argilla-frontend/nuxt.config.ts (CWE-918)risk surface
•External endpoints declared — 5 distinct host(s) · argilla/argilla-frontend/nuxt.config.tsrisk surface
⚠LLM05Improper Output Handlingmedium
Code that pipes model/user output into shell, eval, SQL or paths unsafely.
•Suspicious code patterns — dynamic code execution · argilla/argilla-frontend/components/features/annotation/container/fields/useSearchTextHighlight.ts (CWE-95)risk surface
⚠LLM10Unbounded Consumptionmedium
Unbounded loops/recursion causing DoS or runaway cost.
Enforced at runtime by the gateway (rate limits + spend caps + size caps); static check flags unbounded loops.
•Potentially unbounded loop — an infinite loop (while True / while(1) / for(;;)) may cause runaway consumption · argilla/argilla-frontend/components/features/annotation/container/fields/span-annotation/components/span-selection.ts (CWE-835)risk surface
⚠LLM03Supply Chainlow
Vulnerable/compromised dependencies, models or archives in the artifact.
•Dependency manifest — 54 npm dependencies declared · argilla/argilla-frontend/package.jsonrisk surface
§LLM09MisinformationGovernance
Artifacts designed to produce false/deceptive output.
Detectable only by runtime behavioral evaluation; addressed via responsible-use attestation.
✓LLM01Prompt InjectionPassed
✓LLM02Sensitive Information DisclosurePassed
✓LLM04Data and Model PoisoningPassed
Backdoors/poisoning in training data or serialized models.
Behavioral poisoning needs model execution; static check covers unsafe serialization + dataset skew only.
✓LLM07System Prompt LeakagePassed
✓LLM08Vector and Embedding WeaknessesPassed
PII or plaintext source leakage in embedding/vector exports.
Embedding inversion/poisoning is largely runtime; static check covers PII in vector exports.
OWASP Machine Learning Security Top 10
⚠ML09Output Integritymedium
Middleware tampering with model outputs in transit.
Gateway enforces TLS + response integrity; static check flags output-rewriting code.
•Suspicious code patterns — dynamic code execution · argilla/argilla-frontend/components/features/annotation/container/fields/useSearchTextHighlight.ts (CWE-95)risk surface
⚠ML06AI Supply Chainlow
Compromised PyPI/npm packages, typosquats, unsafe serialized models.
•Dependency manifest — 54 npm dependencies declared · argilla/argilla-frontend/package.jsonrisk surface
§ML01Input Manipulation (Adversarial)Governance
Models vulnerable to adversarial perturbations.
Requires runtime robustness evaluation; addressed via publisher robustness attestation.
§ML03Model InversionGovernance
Training data reconstructable from a model's outputs.
Runtime/evaluation property; addressed via model-card data-provenance + DP attestation.
§ML04Membership InferenceGovernance
Determining whether a record was in the training set.
Runtime/evaluation property; addressed via overfitting disclosure + DP attestation.
§ML08Model SkewingGovernance
Models trained on skewed data producing biased output.
Requires fairness evaluation; addressed via model-card bias/limitations disclosure.
✓ML02Data PoisoningPassed
Poisoned training datasets with triggers or anomalous distributions.
Static check covers trigger phrasing, PII and label skew; full poisoning detection is runtime.
✓ML05Model TheftPassed
Unlicensed re-distribution / license-incompatible derivatives.
Static check verifies license declaration; extraction throttling is runtime.
✓ML07Transfer Learning AttackPassed
Backdoored base models / LoRA adapters propagating to derivatives.
Backdoor detection needs behavioral probing; static check covers unsafe serialization + provenance.
✓ML10Model Poisoning (Weights)Passed
Tampered model weight files; integrity must be verifiable.
Static check enforces safe formats + records a content hash for downstream verification.
Other findings (2) · hygiene / uncategorized
•Unrecognized file type — '.?' is not on the allowlist · argilla/.github/actions/docker-image-tag-from-ref/Dockerfilerisk surface
•Suspicious network references — raw IP URL (15 URLs) · argilla/argilla-frontend/nuxt.config.tsrisk surface
✔ verified source · pinned partial
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/argilla-dataset-annotation-platform/check). Click a policy:

Consume Argilla — Collaborative Data Annotation for LLMs programmatically. Authenticate with an API key or session — see Authorize an agent.

# Agents: CHECK BEFORE YOU INSTALL (no auth) — score, grade, level, capability manifest
curl https://ai-supply.store/api/v1/trust/argilla-dataset-annotation-platform

# Gate against your org policy (returns { pass, violations })
curl -X POST https://ai-supply.store/api/v1/trust/argilla-dataset-annotation-platform/check \
  -H "Content-Type: application/json" \
  -d '{"minGrade":"B","denyPermissions":["shell"],"denyUnknownEgress":true}'

# CLI
npx ai-supply add argilla-dataset-annotation-platform

# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/argilla-dataset-annotation-platform/install \
  -H "Authorization: Bearer $AIM_KEY"

# MCP tool
install_listing({ "slug": "argilla-dataset-annotation-platform" })
OpenAPI spec →
vlatest
✓ Security: Safe · 1001mo ago

Curated mirror — latest upstream source. See the repository for tagged releases.

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