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catalog / Language & NLP / Stanza — Stanford NLP Python Toolkit
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Stanza — Stanford NLP Python Toolkit

Stanford NLP's Python library for tokenisation, sentence segmentation, NER, dependency parsing, and coreference across 70+ languages.

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Installs73k
⟳ upstream v1.14.0 · updated 12d ago
↗ Source repository
← More Language & NLPLanguage & NLP leaderboard →How we grade security →Source ↗
✓ Grade A · 100/100 · SafeSecurity assessment
✓No compromise signals27capabilities surfaced9of 20 OWASP controls clear
External endpoints declaredSuspicious network referencesExternal endpoints declaredExternal endpoints declared
scanned 8d ago·osv · gitleaks · opengrep · picklescan + heuristics·full breakdown in the Security tab ↓

Stanza

Stanza is Stanford NLP's production-grade Python NLP toolkit covering the full linguistic analysis pipeline. With pre-trained neural models for 70+ human languages, it delivers accurate tokenisation, multi-word token expansion, POS tagging, lemmatisation, NER, and dependency parsing in one consistent API.

Key Features

  • 70+ language models, including low-resource languages
  • Full NLP pipeline: tokenise → MWT → POS → lemma → depparse → NER → coref
  • BiLSTM neural architecture; UD-trained dependency parsers
  • spaCy-compatible wrapper (stanza.pipeline.core.Pipeline)
  • Named entity recognition: 18+ entity types
  • Biomedical/clinical NLP models (PubMed, MIMIC-III trained)
  • CoreNLP Java server bridge for Stanford CoreNLP features

Quick Start

import stanza

stanza.download("en")  # download once
nlp = stanza.Pipeline(lang="en", processors="tokenize,mwt,pos,lemma,depparse,ner")

doc = nlp("Barack Obama was born in Hawaii. He was the 44th President.")
for sent in doc.sentences:
    for word in sent.words:
        print(f"{word.text:15s} POS={word.upos:6s} HEAD={sent.words[word.head-1].text if word.head > 0 else 'root'}")
    for ent in sent.ents:
        print(f"  NER: {ent.text} [{ent.type}]")

Install via ai-supply

npx ai-supply add stanza-stanford-nlp-toolkit

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

Rating rank
#1
of 30 in Language & NLP
Install rank
#13
of 30 in Language & NLP
Security score
100/100 · A
safe
Security rank
#1
of 30 in Language & NLP
Installs
73k
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 8d ago
✓ no compromise signals27 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 · med confidence (static)
⚑ filesystem⚑ shell⚑ network⚑ secrets
egress → stanfordnlp.github.io, nlp.stanford.edu, pypi.org, img.shields.io, anaconda.org, arxiv.org, qipeng.me, yuhao.im +27
skill: Bug report30 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
⚠LLM05Improper Output Handlingmedium
Code that pipes model/user output into shell, eval, SQL or paths unsafely.
•Suspicious code patterns — dynamic code execution · stanfordnlp-stanza-1f4bfdd/stanza/models/charlm.py (CWE-95)risk surface
•Suspicious code patterns — dynamic code execution; pickle deserialization · stanfordnlp-stanza-1f4bfdd/stanza/models/lemma/trainer.py (CWE-95)risk surface
•Suspicious code patterns — OS command execution · stanfordnlp-stanza-1f4bfdd/stanza/server/client.py (CWE-78)risk surface
⚠LLM06Excessive Agencymedium
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 1 distinct host(s) · stanfordnlp-stanza-1f4bfdd/.github/ISSUE_TEMPLATE/question.mdrisk surface
•External endpoints declared — 17 distinct host(s) · stanfordnlp-stanza-1f4bfdd/README.mdrisk surface
•External endpoints declared — 4 distinct host(s) · stanfordnlp-stanza-1f4bfdd/demo/Stanza_Beginners_Guide.ipynbrisk surface
•External endpoints declared — 5 distinct host(s) · stanfordnlp-stanza-1f4bfdd/demo/Stanza_CoreNLP_Interface.ipynbrisk surface
•External endpoints declared — 2 distinct host(s) · stanfordnlp-stanza-1f4bfdd/scripts/config.shrisk surface
•External endpoints declared — 7 distinct host(s) · stanfordnlp-stanza-1f4bfdd/scripts/download_vectors.shrisk surface
•External endpoints declared — 3 distinct host(s) · stanfordnlp-stanza-1f4bfdd/stanza/models/classifiers/cnn_classifier.pyrisk surface
•Broad capability surface — 4 high-impact capability categories referenced — verify least-privilege · stanfordnlp-stanza-1f4bfdd/stanza/server/client.py (CWE-272)risk surface
•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege · stanfordnlp-stanza-1f4bfdd/stanza/server/java_protobuf_requests.py (CWE-272)risk surface
•External endpoints declared — 15 distinct host(s) · stanfordnlp-stanza-1f4bfdd/stanza/utils/datasets/constituency/prepare_con_dataset.pyrisk surface
•External endpoints declared — 25 distinct host(s) · stanfordnlp-stanza-1f4bfdd/stanza/utils/datasets/ner/prepare_ner_dataset.pyrisk surface
•External endpoints declared — 18 distinct host(s) · stanfordnlp-stanza-1f4bfdd/stanza/utils/datasets/sentiment/prepare_sentiment_dataset.pyrisk surface
⚠LLM07System Prompt Leakagemedium
Secrets, internal hosts or proprietary logic exposed in shipped prompts.
•Internal host / private infrastructure reference — shipped content references a private IP range or internal-only host · stanfordnlp-stanza-1f4bfdd/stanza/utils/datasets/constituency/prepare_con_dataset.py (CWE-200)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 · stanfordnlp-stanza-1f4bfdd/stanza/models/common/bert_embedding.py (CWE-835)risk surface
⚠LLM01Prompt Injectionlow
Adversarial instructions embedded in an artifact that hijack a downstream LLM.
•Zero-width characters — 110 hidden characters · stanfordnlp-stanza-1f4bfdd/stanza/tests/datasets/coref/test_hebrew_iahlt.pyrisk surface
§LLM09MisinformationGovernance
Artifacts designed to produce false/deceptive output.
Detectable only by runtime behavioral evaluation; addressed via responsible-use attestation.
✓LLM02Sensitive Information DisclosurePassed
✓LLM03Supply ChainPassed
✓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.
✓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 · stanfordnlp-stanza-1f4bfdd/stanza/models/charlm.py (CWE-95)risk surface
•Suspicious code patterns — dynamic code execution; pickle deserialization · stanfordnlp-stanza-1f4bfdd/stanza/models/lemma/trainer.py (CWE-95)risk surface
•Suspicious code patterns — OS command execution · stanfordnlp-stanza-1f4bfdd/stanza/server/client.py (CWE-78)risk 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.
✓ML06AI Supply ChainPassed
✓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 (16) · hygiene / uncategorized
•Unrecognized file type — '.gitignore' is not on the allowlist · stanfordnlp-stanza-1f4bfdd/.gitignorerisk surface
•Suspicious network references — suspicious TLD (1 URLs) · stanfordnlp-stanza-1f4bfdd/.travis.ymlrisk surface
•Unrecognized file type — '.?' is not on the allowlist · stanfordnlp-stanza-1f4bfdd/LICENSErisk surface
•Unrecognized file type — '.conllu' is not on the allowlist · stanfordnlp-stanza-1f4bfdd/demo/semgrex_sample.conllurisk surface
•Unrecognized file type — '.proto' is not on the allowlist · stanfordnlp-stanza-1f4bfdd/doc/CoreNLP.protorisk surface
•Suspicious network references — suspicious TLD (9 URLs) · stanfordnlp-stanza-1f4bfdd/scripts/download_vectors.shrisk surface
•Unrecognized file type — '.j2' is not on the allowlist · stanfordnlp-stanza-1f4bfdd/stanza/pipeline/demo/stanza-brat.html.j2risk surface
•Suspicious network references — suspicious TLD (8 URLs) · stanfordnlp-stanza-1f4bfdd/stanza/resources/common.pyrisk surface
•Suspicious network references — suspicious TLD (6 URLs) · stanfordnlp-stanza-1f4bfdd/stanza/resources/installation.pyrisk surface
•Unrecognized file type — '.properties' is not on the allowlist · stanfordnlp-stanza-1f4bfdd/stanza/tests/data/external_server.propertiesrisk surface
•Suspicious network references — URL shortener (2 URLs) · stanfordnlp-stanza-1f4bfdd/stanza/tests/langid/test_langid.pyrisk surface
•Unrecognized file type — '.ini' is not on the allowlist · stanfordnlp-stanza-1f4bfdd/stanza/tests/pytest.inirisk surface
•Suspicious network references — suspicious TLD (25 URLs) · stanfordnlp-stanza-1f4bfdd/stanza/utils/datasets/constituency/prepare_con_dataset.pyrisk surface
•Suspicious network references — suspicious TLD (81 URLs) · stanfordnlp-stanza-1f4bfdd/stanza/utils/datasets/ner/prepare_ner_dataset.pyrisk surface
•Suspicious network references — suspicious TLD (26 URLs) · stanfordnlp-stanza-1f4bfdd/stanza/utils/datasets/sentiment/prepare_sentiment_dataset.pyrisk surface
•Suspicious network references — suspicious TLD (5 URLs) · stanfordnlp-stanza-1f4bfdd/stanza/utils/datasets/sentiment/process_arguana_xml.pyrisk surface
✔ verified source · pinned stanfordnlp-stanza-1f4bfdd
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/stanza-stanford-nlp-toolkit/check). Click a policy:

Consume Stanza — Stanford NLP Python Toolkit 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/stanza-stanford-nlp-toolkit

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

# CLI
npx ai-supply add stanza-stanford-nlp-toolkit

# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/stanza-stanford-nlp-toolkit/install \
  -H "Authorization: Bearer $AIM_KEY"

# MCP tool
install_listing({ "slug": "stanza-stanford-nlp-toolkit" })
OpenAPI spec →
vlatest
✓ Security: Safe · 1001mo ago

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

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