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Vanna

Retrieval-augmented text-to-SQL: train on your schema and docs, then ask questions in English and get accurate, runnable SQL.

@ai-supply
Instalações24k
⟳ upstream v2.0.2 · updated 6mo ago
↗ Repositório fonte
← More CodingCoding leaderboard →How we grade security →Source ↗
✓ Grade A · 100/100 · SafeSecurity assessment
✓No compromise signals15capabilities surfaced8of 20 OWASP controls clear
External endpoints declaredExternal endpoints declaredExternal endpoints declaredExternal endpoints declared
scanned 1mo ago·osv · gitleaks · opengrep · picklescan + heuristics·full breakdown in the Security tab ↓

Vanna

Vanna is a Python framework for accurate text-to-SQL over your own database. You "train" it on your DDL, documentation, and example queries; it stores those in a vector store and uses RAG so an LLM generates SQL grounded in your actual schema. Ask a question in plain English and Vanna returns runnable SQL, executes it, and can render the result as a table or chart.

Key features

  • RAG-based text-to-SQL that improves as you add training data
  • Model-agnostic (OpenAI, Anthropic, and others) and DB-agnostic (Postgres, Snowflake, BigQuery, MySQL, SQLite, and more)
  • Pluggable vector stores (ChromaDB, Qdrant, and others)
  • Runs from a notebook, Streamlit/Flask app, or embedded in your own agent
  • Self-hostable — your schema and data stay in your environment

Well suited to analytics copilots and agent tools that need reliable SQL grounded in a specific warehouse rather than generic guesses.

Curated mirror of the open-source Vanna (MIT). Get it from the source.

Rating rank
#1
of 27 in Coding
Install rank
#19
of 27 in Coding
Security score
100/100 · A
safe
Security rank
#1
of 27 in Coding
Installs
24k
cat avg 157k
This listing vs category average
Installs
this
cat avg
Security (of 100)
this
cat avg
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See the Coding leaderboard →
✓ Security: Safe · 100100/100 · grade Ascanned 1mo ago
✓ no compromise signals15 risk-surface · 7/20 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 (3)
DDLDocumentationSQL
⚑ filesystem⚑ shell⚑ network⚑ secrets
egress → docs.github.com, vanna.ai, img.shields.io, python.org, img.vanna.ai, your-api.com, pypi.org, gurubase.io +32

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 — pickle deserialization · vanna-ai-vanna-365d061/src/vanna/integrations/faiss/agent_memory.py (CWE-502)risk surface
•Suspicious code patterns — dynamic code execution · vanna-ai-vanna-365d061/src/vanna/legacy/base/base.py (CWE-95)risk surface
⚠LLM06Excessive Agencymedium
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 1 distinct host(s) · vanna-ai-vanna-365d061/.github/workflows/python-publish.yamlrisk surface
•External endpoints declared — 2 distinct host(s) · vanna-ai-vanna-365d061/CONTRIBUTING.mdrisk surface
•External endpoints declared — 6 distinct host(s) · vanna-ai-vanna-365d061/README.mdrisk surface
•External endpoints declared — 17 distinct host(s) · vanna-ai-vanna-365d061/README_LEGACY.mdrisk surface
•External endpoints declared — 3 distinct host(s) · vanna-ai-vanna-365d061/frontends/webcomponent/src/components/rich-component-system.tsrisk surface
•External endpoints declared — 12 distinct host(s) · vanna-ai-vanna-365d061/papers/ai-sql-accuracy-2023-08-17.mdrisk surface
•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege · vanna-ai-vanna-365d061/src/vanna/examples/coding_agent_example.py (CWE-272)risk surface
•Broad capability surface — 4 high-impact capability categories referenced — verify least-privilege · vanna-ai-vanna-365d061/src/vanna/legacy/base/base.py (CWE-272)risk surface
•External endpoints declared — 4 distinct host(s) · vanna-ai-vanna-365d061/src/vanna/legacy/flask/assets.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 · vanna-ai-vanna-365d061/src/vanna/examples/mock_auth_example.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 · vanna-ai-vanna-365d061/frontends/webcomponent/src/services/api-client.ts (CWE-835)risk surface
⚠LLM03Supply Chainlow
Vulnerable/compromised dependencies, models or archives in the artifact.
•Dependency manifest — 12 npm dependencies declared · vanna-ai-vanna-365d061/frontends/webcomponent/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.
✓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 — pickle deserialization · vanna-ai-vanna-365d061/src/vanna/integrations/faiss/agent_memory.py (CWE-502)risk surface
•Suspicious code patterns — dynamic code execution · vanna-ai-vanna-365d061/src/vanna/legacy/base/base.py (CWE-95)risk surface
⚠ML06AI Supply Chainlow
Compromised PyPI/npm packages, typosquats, unsafe serialized models.
•Dependency manifest — 12 npm dependencies declared · vanna-ai-vanna-365d061/frontends/webcomponent/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 (7) · hygiene / uncategorized
•Unrecognized file type — '.gitattributes' is not on the allowlist · vanna-ai-vanna-365d061/.gitattributesrisk surface
•Unrecognized file type — '.gitignore' is not on the allowlist · vanna-ai-vanna-365d061/.gitignorerisk surface
•Unrecognized file type — '.?' is not on the allowlist · vanna-ai-vanna-365d061/LICENSErisk surface
•Unrecognized file type — '.cfg' is not on the allowlist · vanna-ai-vanna-365d061/setup.cfgrisk surface
•Suspicious network references — suspicious TLD (1 URLs) · vanna-ai-vanna-365d061/src/vanna/legacy/ZhipuAI/ZhipuAI_Chat.pyrisk surface
•Possible obfuscation — very long lines paired with a decode/execute sink · vanna-ai-vanna-365d061/src/vanna/legacy/flask/assets.py (CWE-506)risk surface
•Unrecognized file type — '.ini' is not on the allowlist · vanna-ai-vanna-365d061/tox.inirisk surface
✔ verified source · pinned vanna-ai-vanna-365d061
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/vanna-text-to-sql/check). Click a policy:

Consume Vanna 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/vanna-text-to-sql

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

# CLI
npx ai-supply add vanna-text-to-sql

# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/vanna-text-to-sql/install \
  -H "Authorization: Bearer $AIM_KEY"

# MCP tool
install_listing({ "slug": "vanna-text-to-sql" })
OpenAPI spec →
vlatest
✓ Security: Safe · 1001mo ago

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

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