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semanticscholar

Unofficial Python client for the Semantic Scholar Academic Graph APIs: papers, authors, citations, and references.

@ai-supply
설치 수3.9k
⟳ upstream v0.12.0 · updated 4mo ago
↗ 소스 저장소
← More ResearchResearch leaderboard →How we grade security →Source ↗
! Grade B · 75/100 · ReviewSecurity assessment
✓No compromise signals10capabilities surfaced1known CVE10of 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 ↓

semanticscholar

semanticscholar is a well-maintained, unofficial Python client library for the Semantic Scholar APIs, including the Academic Graph (S2AG). It gives programmatic access to a corpus of over 200 million papers with rich metadata, citation edges, and author records, making it a solid foundation for citation-analysis and bibliometrics tooling.

Key features

  • Look up papers by many identifiers: DOI, arXiv ID, S2 Paper ID, PubMed, ACL, MAG
  • Retrieve a paper's citations and references with metadata
  • Author profiles, h-index, and their publication lists
  • Bulk and relevance-ranked paper search
  • Optional async client for high-throughput pipelines
  • API-key aware handling for higher rate limits

Install via pip, instantiate the client, and call methods like get_paper, get_paper_citations, or search_paper to traverse the citation graph. Ideal for building citation networks, related-work discovery, and literature-mapping applications on top of Semantic Scholar data.

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

Rating rank
#1
of 17 in Research
Install rank
#16
of 17 in Research
Security score
75/100 · B
review
Security rank
#9
of 17 in Research
Installs
3.9k
cat avg 51k
This listing vs category average
Installs
this
cat avg
Security (of 100)
this
cat avg
Adoption trend
See the Research leaderboard →
! Security: Review · 7575/100 · grade Bscanned 1mo ago
✓ no compromise signals11 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.

What this capability can do · med confidence (static)
⚑ filesystem⚑ network
egress → www.contributor-covenant.org, sscce.org, semanticscholar.readthedocs.io, www.python.org, www.conventionalcommits.org, www.sphinx-doc.org, readthedocs.org, keepachangelog.com +32
auth: api_keywww.contributor-covenant.orggithub.comsscce.orgsemanticscholar.readthedocs.iowww.python.orgwww.conventionalcommits.orgwww.sphinx-doc.orgreadthedocs.org

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 Chaincritical
Vulnerable/compromised dependencies, models or archives in the artifact.
•Dependency manifest — 5 pip requirements declared · danielnsilva-semanticscholar-6041eed/docs-requirements.txtrisk surface
•Dependency manifest — 2 pip requirements declared · danielnsilva-semanticscholar-6041eed/docs/readthedocs-requirements.txtrisk surface
•Vulnerable dependencies — 10 known vulnerabilities in: httpx@0.9.5, h11@0.8.1, h2@3.2.0, idna@2.9.0 (CWE-1395)known CVE · -25 pts
⚠LLM06Excessive Agencymedium
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 2 distinct host(s) · danielnsilva-semanticscholar-6041eed/.github/CODE_OF_CONDUCT.mdrisk surface
•External endpoints declared — 7 distinct host(s) · danielnsilva-semanticscholar-6041eed/.github/CONTRIBUTING.mdrisk surface
•External endpoints declared — 1 distinct host(s) · danielnsilva-semanticscholar-6041eed/.github/SECURITY.mdrisk surface
•External endpoints declared — 6 distinct host(s) · danielnsilva-semanticscholar-6041eed/CHANGELOG.mdrisk surface
•External endpoints declared — 5 distinct host(s) · danielnsilva-semanticscholar-6041eed/docs/source/install.rstrisk surface
•External endpoints declared — 3 distinct host(s) · danielnsilva-semanticscholar-6041eed/docs/source/overview.rstrisk surface
•External endpoints declared — 4 distinct host(s) · danielnsilva-semanticscholar-6041eed/tests/data/Release.jsonrisk surface
•External endpoints declared — 14 distinct host(s) · danielnsilva-semanticscholar-6041eed/tests/data/test_get_release.yamlrisk surface
•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege · danielnsilva-semanticscholar-6041eed/tests/data/test_search_paper_bulk_retrieval.yaml (CWE-272)risk surface
•External endpoints declared — 45 distinct host(s) · danielnsilva-semanticscholar-6041eed/tests/data/test_search_paper_open_access_pdf.yamlrisk 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 Chaincritical
Compromised PyPI/npm packages, typosquats, unsafe serialized models.
•Dependency manifest — 5 pip requirements declared · danielnsilva-semanticscholar-6041eed/docs-requirements.txtrisk surface
•Dependency manifest — 2 pip requirements declared · danielnsilva-semanticscholar-6041eed/docs/readthedocs-requirements.txtrisk surface
•Vulnerable dependencies — 10 known vulnerabilities in: httpx@0.9.5, h11@0.8.1, h2@3.2.0, idna@2.9.0 (CWE-1395)known CVE · -25 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 (3) · hygiene / uncategorized
•Unrecognized file type — '.gitignore' is not on the allowlist · danielnsilva-semanticscholar-6041eed/.gitignorerisk surface
•Unrecognized file type — '.?' is not on the allowlist · danielnsilva-semanticscholar-6041eed/LICENSErisk surface
•Disallowed file type — '.bat' executables are not permitted · danielnsilva-semanticscholar-6041eed/docs/make.bat (CWE-434)risk surface
✔ verified source · pinned danielnsilva-semanticscholar-6041eed
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/semanticscholar-python-client/check). Click a policy:

Consume semanticscholar 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/semanticscholar-python-client

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

# CLI
npx ai-supply add semanticscholar-python-client

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

# MCP tool
install_listing({ "slug": "semanticscholar-python-client" })
OpenAPI spec →
vlatest
! Security: Review · 751mo ago

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

Sign in and install this listing to leave a review.

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