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△EvalLegal & ComplianceFree

LexGLUE — Legal Language Understanding Benchmark

Multi-task benchmark for legal NLP with 7 datasets covering EURLEX classification, contract clause labeling, court judgement prediction, and more.

@ai-supply
Installs31k
⟳ upstream main@419a49d · updated 1y ago
↗ Source repository
← More Legal & ComplianceLegal & Compliance leaderboard →How we grade security →Source ↗
! Grade B · 88/100 · ReviewSecurity assessment
✓No compromise signals2capabilities surfaced1known CVE10of 20 OWASP controls clear
External endpoints declaredExternal endpoints declaredVulnerable dependencies
scanned 16d ago·osv · gitleaks · opengrep · picklescan + heuristics·full breakdown in the Security tab ↓

LexGLUE — Legal Language Understanding Benchmark

LexGLUE is the legal analogue of GLUE/SuperGLUE — a comprehensive benchmark spanning seven legal NLP datasets and tasks. It standardizes evaluation across EURLEX (EU legislation classification), ECHR (court judgement prediction), LEDGAR (contract provision classification), SCOTUS (US Supreme Court decision area), ECtHR (article violation prediction), ContractNLI (contract NLI), and CaseHOLD (legal holding identification).

Key Features

  • 7 legal NLP tasks in a single evaluation harness
  • Covers EU and US jurisdictions across legislation, contracts, and case law
  • HuggingFace Datasets integration for easy loading
  • Leaderboard tracking state-of-the-art Legal-BERT, RoBERTa-legal, and other models
  • CC-BY-4.0 dataset license with public reproducibility

Quick Start

from datasets import load_dataset

# Load EURLEX classification task
dataset = load_dataset("coastalcph/lex_glue", "eurlex")
print(dataset["train"][0]["text"][:200])
print(dataset["train"][0]["labels"])  # Multi-label list

# Load ECHR court judgement prediction
scotus = load_dataset("coastalcph/lex_glue", "scotus")
print(scotus["test"][0])
npx ai-supply add lex-glue-legal-benchmark

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

Rating rank
#1
of 11 in Legal & Compliance
Install rank
#4
of 11 in Legal & Compliance
Security score
88/100 · B
review
Security rank
#9
of 11 in Legal & Compliance
Installs
31k
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: Review · 8888/100 · grade Bscanned 16d ago
✓ no compromise signals3 risk-surface · 3/20 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.

Control card · med confidence (static)
covers: biascovers: hallucination

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
⚠LLM03Supply Chainhigh
Vulnerable/compromised dependencies, models or archives in the artifact.
•Dependency manifest — 8 pip requirements declared · coastalcph-lex-glue-419a49d/requirements.txtrisk surface
•Vulnerable dependencies — 50 known vulnerabilities in: nltk@3.9.4, torch@2.9.1, aiohttp@3.9.5, idna@3.9.0 (CWE-1395)known CVE · -12 pts
⚠LLM06Excessive Agencylow
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 10 distinct host(s) · coastalcph-lex-glue-419a49d/README.mdrisk surface
•External endpoints declared — 1 distinct host(s) · coastalcph-lex-glue-419a49d/experiments/ecthr.pyrisk 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
✓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.
✓LLM05Improper Output HandlingPassed
✓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
⚠ML06AI Supply Chainhigh
Compromised PyPI/npm packages, typosquats, unsafe serialized models.
•Dependency manifest — 8 pip requirements declared · coastalcph-lex-glue-419a49d/requirements.txtrisk surface
•Vulnerable dependencies — 50 known vulnerabilities in: nltk@3.9.4, torch@2.9.1, aiohttp@3.9.5, idna@3.9.0 (CWE-1395)known CVE · -12 pts
§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.
◷ML09Output IntegrityRuntime-enforced
Middleware tampering with model outputs in transit.
Gateway enforces TLS + response integrity; static check flags output-rewriting code.
✓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 (1) · hygiene / uncategorized
•Unrecognized file type — '.gitignore' is not on the allowlist · coastalcph-lex-glue-419a49d/.gitignorerisk surface
✔ verified source · pinned coastalcph-lex-glue-419a49d
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/lex-glue-legal-benchmark/check). Click a policy:

Consume LexGLUE — Legal Language Understanding Benchmark 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/lex-glue-legal-benchmark

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

# CLI
npx ai-supply add lex-glue-legal-benchmark

# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/lex-glue-legal-benchmark/install \
  -H "Authorization: Bearer $AIM_KEY"

# MCP tool
install_listing({ "slug": "lex-glue-legal-benchmark" })
OpenAPI spec →
vlatest
! Security: Review · 881mo ago

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

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