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Juriscraper — Court Data Scraper

Name: Juriscraper — Court Data Scraper
Availability: InStock
Author: ai-supply

A caching, scraping library that collects opinions, oral arguments, and PACER filings from hundreds of American court websites.

@ai-supply

インストール数6.9k

⟳ upstream 1.4.15 · updated 9y ago

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Juriscraper

Juriscraper is a Python library, maintained by the Free Law Project, that scrapes metadata and documents from American federal and state court websites. It powers the ingestion pipeline behind CourtListener, standardizing wildly different court sites into a consistent interface for opinions, oral-argument audio, and PACER (federal filing) data.

Key features

Scrapers for hundreds of state and federal appellate and trial courts
Unified output for opinions, oral arguments, and PACER dockets/documents
Built-in politeness: caching, rate awareness, and change detection
Extensible base classes make adding new court scrapers straightforward
Powers one of the largest open archives of U.S. court data

Each court is exposed as a module you invoke to fetch the latest cases, returning normalized records (case name, date, citation, download URL) ready for storage or analysis.

Curated mirror of the open-source Juriscraper (BSD-2-Clause). Get it from the source.

✓ Security: Safe · 100100/100 · grade Ascanned 3h ago

Only compromise signals — malicious or tampered code (leaked secrets, backdoors, path traversal, a dropped executable) — reduce the score. Dangerous-by-capability traits (shell, network, injection strings, pickle) are shown as risk surface: expected for some capabilities — a security tool ships offensive code on purpose — so they do not sink the grade.

Compromise signals

None — no malicious or tampered code detected in the scanned source.

What this capability can do · med confidence (static)

⚑ filesystem⚑ network⚑ secrets

egress → www.loc.gov, www.sitemaps.org, www.gpo.gov

Risk surface (9)

External endpoints declaredlowfreelawproject-juriscraper-c2f9d5a/.editorconfig

1 distinct host(s)

Broad capability surfacelowfreelawproject-juriscraper-c2f9d5a/CHANGES.md

3 high-impact capability categories referenced — verify least-privilege

External endpoints declaredlowfreelawproject-juriscraper-c2f9d5a/CHANGES.md

2 distinct host(s)

Suspicious code patternshighfreelawproject-juriscraper-c2f9d5a/CONTRIBUTING.md

pipe-to-shell install

External endpoints declaredlowfreelawproject-juriscraper-c2f9d5a/CONTRIBUTING.md

6 distinct host(s)

External endpoints declaredlowfreelawproject-juriscraper-c2f9d5a/README.rst

7 distinct host(s)

External endpoints declaredlowfreelawproject-juriscraper-c2f9d5a/juriscraper/fdsys/FDSysSite.py

4 distinct host(s)

External endpoints declaredlowfreelawproject-juriscraper-c2f9d5a/juriscraper/fdsys/annotated_mods.xml

5 distinct host(s)

Possible obfuscationmediumfreelawproject-juriscraper-c2f9d5a/juriscraper/fdsys/examples/2006/rid-1_04-cv-00361.xml

very long lines

✔ verified source · pinned freelawproject-juriscraper-c2f9d5a · changed since last scan · +egress www.loc.gov, www.sitemaps.org, www.gpo.gov

OWASP AI control mapping

11passed

3flagged

1runtime-enforced

5governance

Evaluated against the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any control to see the findings.

OWASP Top 10 for LLM Applications

✓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.

⚠LLM05Improper Output Handlinghigh

Code that pipes model/user output into shell, eval, SQL or paths unsafely.

•Suspicious code patterns — pipe-to-shell install (CWE-494)

⚠LLM06Excessive Agencylow

Over-broad tool/permission surface or unrestricted egress.

•External endpoints declared — 1 distinct host(s)

•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege (CWE-272)

•External endpoints declared — 2 distinct host(s)

•External endpoints declared — 6 distinct host(s)

•External endpoints declared — 7 distinct host(s)

•External endpoints declared — 4 distinct host(s)

•External endpoints declared — 5 distinct host(s)

✓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.

§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.

OWASP Machine Learning Security Top 10

§ML01Input Manipulation (Adversarial)Governance

Models vulnerable to adversarial perturbations.

Requires runtime robustness evaluation; addressed via publisher robustness attestation.

✓ML02Data PoisoningPassed

Poisoned training datasets with triggers or anomalous distributions.

Static check covers trigger phrasing, PII and label skew; full poisoning detection is runtime.

§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.

✓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.

§ML08Model SkewingGovernance

Models trained on skewed data producing biased output.

Requires fairness evaluation; addressed via model-card bias/limitations disclosure.

⚠ML09Output Integrityhigh

Middleware tampering with model outputs in transit.

Gateway enforces TLS + response integrity; static check flags output-rewriting code.

•Suspicious code patterns — pipe-to-shell install (CWE-494)

✓ML10Model Poisoning (Weights)Passed

Tampered model weight files; integrity must be verifiable.

Static check enforces safe formats + records a content hash for downstream verification.

Consume Juriscraper — Court Data Scraper 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/juriscraper-court-data-scraper

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

# CLI
npx ai-supply add juriscraper-court-data-scraper

# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/juriscraper-court-data-scraper/install \
  -H "Authorization: Bearer $AIM_KEY"

# MCP tool
install_listing({ "slug": "juriscraper-court-data-scraper" })

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