Skip to content
ai-supply.store
DiscoverCategoriesLeaderboardsCommunityAgent APIFAQ
Sign inSign up free
catalog / Legal & Compliance / CUAD — Contract Understanding Atticus Dataset
▣DatasetLegal & ComplianceFree

CUAD — Contract Understanding Atticus Dataset

Expert-labeled dataset of 13,000+ annotations across 510 commercial contracts covering 41 legal clause types for contract review AI.

@ai-supply
Installs73k
⟳ upstream main@67faa0e · updated 3y ago
↗ Source repository
← More Legal & ComplianceLegal & Compliance leaderboard →How we grade security →Source ↗
✓ Grade A · 100/100 · SafeSecurity assessment
✓No compromise signals2capabilities surfaced10of 20 OWASP controls clear
Suspicious code patternsBroad capability surface
scanned 16d ago·osv · gitleaks · opengrep · picklescan + heuristics·full breakdown in the Security tab ↓

CUAD — Contract Understanding Atticus Dataset

CUAD (Contract Understanding Atticus Dataset) is a large-scale dataset created by The Atticus Project with dozens of legal experts. It contains 13,000+ annotations across 510 real commercial contracts, labeling 41 distinct clause types including parties, payment terms, termination clauses, IP ownership, and liability caps. It is the benchmark dataset for training and evaluating contract review AI systems.

Key Features

  • 510 commercial contracts from EDGAR (SEC filings)
  • 41 clause categories annotated by legal professionals
  • Question-answering format compatible with extractive QA models
  • Benchmark leaderboard for contract understanding research
  • Free for commercial and academic use under CC-BY-4.0

Quick Start

from datasets import load_dataset

dataset = load_dataset("theatticusproject/cuad")
train = dataset["train"]
print(f"Train examples: {len(train)}")
print(train[0]["title"])  # Contract name
print(train[0]["question"])  # Clause type question
print(train[0]["answers"])  # Extracted clause text
npx ai-supply add cuad-contract-understanding-dataset

Curated mirror of the open-source CUAD (CC-BY-4.0). Get it from the source.

Rating rank
#1
of 11 in Legal & Compliance
Install rank
#2
of 11 in Legal & Compliance
Security score
100/100 · A
safe
Security rank
#1
of 11 in Legal & Compliance
Installs
73k
cat avg 29k
This listing vs category average
Installs
this
cat avg
Security (of 100)
this
cat avg
Adoption trend
See the Legal & Compliance leaderboard →
✓ Security: Safe · 100100/100 · grade Ascanned 16d ago
✓ no compromise signals2 risk-surface · 4/20 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.

Data card · high confidence (static)
csv
9 files

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 · TheAtticusProject-cuad-67faa0e/train.py (CWE-95)risk surface
⚠LLM06Excessive Agencylow
Over-broad tool/permission surface or unrestricted egress.
•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege · TheAtticusProject-cuad-67faa0e/utils.py (CWE-272)risk surface
§LLM09MisinformationGovernance
Artifacts designed to produce false/deceptive output.
Detectable only by runtime behavioral evaluation; addressed via responsible-use attestation.
◷LLM10Unbounded ConsumptionRuntime-enforced
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.
✓LLM01Prompt InjectionPassed
✓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.
✓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 · TheAtticusProject-cuad-67faa0e/train.py (CWE-95)risk surface
⚠ML05Model Theftlow
Unlicensed re-distribution / license-incompatible derivatives.
Static check verifies license declaration; extraction throttling is runtime.
•No license signal — no SPDX id or license keyword found · TheAtticusProject-cuad-67faa0e/category_descriptions.csvrisk 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.
✓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.
✔ verified source · pinned TheAtticusProject-cuad-67faa0e
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/cuad-contract-understanding-dataset/check). Click a policy:

Consume CUAD — Contract Understanding Atticus Dataset 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/cuad-contract-understanding-dataset

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

# CLI
npx ai-supply add cuad-contract-understanding-dataset

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

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

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

Sign in and install this listing to leave a review.

More from @ai-supply

View profile →
◉Agent
MetaGPT
Multi-agent framework that assigns GPT roles (PM, engineer, QA) to solve complex software tasks end-to-end.
↓ 1.0M
⇄Connector
vLLM
High-throughput, memory-efficient LLM inference engine with PagedAttention and continuous batching.
↓ 892k
⇄Connector
Meilisearch
Lightning-fast open-source search engine with typo-tolerance, semantic hybrid search, and sub-50ms response times.
↓ 811k
△Eval
Weights & Biases (wandb)
ML experiment tracking and visualization — log metrics, hyperparameters, models, and media in real time.
↓ 784k
ai-supply.store

Free, security-vetted AI capabilities — skills, MCPs, plugins, agents, datasets and more, each graded and freshness-tracked, and built for humans and agents alike.

api · v3.1status · all green
Contact
support@ai-supply.storesecurity@ai-supply.store
Catalog
  • Discover
  • Categories
  • Leaderboards
  • Benchmarks
  • Security
  • Scan a repo
Community
  • Community
  • FAQ
For agents
  • Quickstart (60s)
  • Authorize an agent
  • Agent API
  • OpenAPI spec
For builders
  • Publish
  • Dashboard
Account
  • Create account
  • Sign in
  • Settings
Legal
  • Terms
  • Publisher Agreement
  • Acceptable Use
  • Privacy