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Nipype — Neuroimaging Workflow Engine

Python dataflow engine that wraps FSL, SPM, FreeSurfer, ANTs, and AFNI into reproducible neuroimaging pipelines.

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इंस्टॉल30k
⟳ upstream 1.11.0 · updated 4mo ago
↗ सोर्स रिपॉज़िटरी
← More HealthcareHealthcare leaderboard →How we grade security →Source ↗
✓ Grade A · 100/100 · SafeSecurity assessment
✓No compromise signals28capabilities surfaced8of 20 OWASP controls clear
Suspicious code patternsExternal endpoints declaredExternal endpoints declaredBroad capability surface
scanned 18d ago·osv · gitleaks · opengrep · picklescan + heuristics·full breakdown in the Security tab ↓

Nipype — Neuroimaging Workflow Engine

Nipype is a neuroimaging workflow framework that provides uniform Python interfaces to dozens of tools — FSL, SPM, FreeSurfer, ANTs, AFNI, MRtrix, Camino — and chains them into reproducible dataflow pipelines with parallel execution and smart caching.

Key features

  • 500+ interface wrappers covering every major neuroimaging toolbox
  • DAG-based workflow with conditional re-execution (only re-runs changed nodes)
  • MultiProc, SGE, SLURM, PBS, and AWS Batch execution plugins
  • Generates provenance records and dot-graph visualizations
  • Underlies fMRIPrep, dMRIPrep, and HALFpipe

Quick start

pip install nipype
from nipype import Workflow, Node
from nipype.interfaces.fsl import BET, MCFLIRT

skull_strip = Node(BET(in_file="bold.nii"), name="skull_strip")
mc = Node(MCFLIRT(), name="motion_correct")

wf = Workflow(name="preproc", base_dir="/tmp")
wf.connect(skull_strip, "out_file", mc, "in_file")
wf.run()
npx ai-supply add nipype-neuroimaging-workflows

Curated mirror of the open-source Nipype (Apache-2.0). Get it from the source.

Rating rank
#1
of 11 in Healthcare
Install rank
#8
of 11 in Healthcare
Security score
100/100 · A
safe
Security rank
#1
of 11 in Healthcare
Installs
30k
cat avg 63k
This listing vs category average
Installs
this
cat avg
Security (of 100)
this
cat avg
Adoption trend
See the Healthcare leaderboard →
✓ Security: Safe · 100100/100 · grade Ascanned 18d ago
✓ no compromise signals28 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 · med confidence (static)
⚑ filesystem⚑ shell⚑ network⚑ secrets
egress → files.osf.io, docs.github.com, api.github.com, pre-commit.com, docs.readthedocs.io, nipype.readthedocs.io, miykael.github.io, www.contributor-covenant.org +25
73 steps⚑ uses secretsfiles.osf.iogithub.comdocs.github.comactions/checkout@v6codespell-project/actions-codespell@v2astral-sh/setup-uv@v7codecov/codecov-action@v6actions/upload-artifact@v7

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 Handlingcritical
Code that pipes model/user output into shell, eval, SQL or paths unsafely.
•Suspicious code patterns — world-writable chmod 777 · nipy-nipype-2bb2f81/.circleci/config.yml (CWE-732)risk surface
•Suspicious code patterns — dynamic code execution · nipy-nipype-2bb2f81/nipype/algorithms/metrics.py (CWE-95)risk surface
•Suspicious code patterns — OS command execution · nipy-nipype-2bb2f81/nipype/info.py (CWE-78)risk surface
•Suspicious code patterns — pickle deserialization · nipy-nipype-2bb2f81/nipype/interfaces/cmtk/cmtk.py (CWE-502)risk surface
•Suspicious code patterns — disk wipe command; OS command execution · nipy-nipype-2bb2f81/nipype/testing/utils.py (CWE-78)risk surface
⚠LLM06Excessive Agencyhigh
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 2 distinct host(s) · nipy-nipype-2bb2f81/.circleci/config.ymlrisk surface
•External endpoints declared — 1 distinct host(s) · nipy-nipype-2bb2f81/.github/dependabot.ymlrisk surface
•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege · nipy-nipype-2bb2f81/.github/workflows/prerelease.yml (CWE-272)risk surface
•External endpoints declared — 3 distinct host(s) · nipy-nipype-2bb2f81/.wci.ymlrisk surface
•External endpoints declared — 7 distinct host(s) · nipy-nipype-2bb2f81/CONTRIBUTING.mdrisk surface
•External endpoints declared — 16 distinct host(s) · nipy-nipype-2bb2f81/README.rstrisk surface
•External endpoints declared — 6 distinct host(s) · nipy-nipype-2bb2f81/Vagrantfilerisk surface
•External endpoints declared — 11 distinct host(s) · nipy-nipype-2bb2f81/doc/_templates/indexsidebar.htmlrisk surface
•External endpoints declared — 20 distinct host(s) · nipy-nipype-2bb2f81/doc/devel/gitwash/git_links.incrisk surface
•External endpoints declared — 10 distinct host(s) · nipy-nipype-2bb2f81/doc/devel/gitwash/known_projects.incrisk surface
•External endpoints declared — 5 distinct host(s) · nipy-nipype-2bb2f81/doc/devel/interface_specs.rstrisk surface
•Egress to a private/loopback host — fcp-indi.github.io · nipy-nipype-2bb2f81/doc/devel/software_using_nipype.rst (CWE-918)risk surface
•External endpoints declared — 8 distinct host(s) · nipy-nipype-2bb2f81/doc/devel/software_using_nipype.rstrisk surface
•External endpoints declared — 73 distinct host(s) · nipy-nipype-2bb2f81/doc/links_names.txtrisk surface
•External endpoints declared — 9 distinct host(s) · nipy-nipype-2bb2f81/doc/quickstart.rstrisk surface
•External endpoints declared — 4 distinct host(s) · nipy-nipype-2bb2f81/nipype/algorithms/confounds.pyrisk surface
•Broad capability surface — 4 high-impact capability categories referenced — verify least-privilege · nipy-nipype-2bb2f81/nipype/external/d3.js (CWE-272)risk surface
•Egress to a private/loopback host — 127.0.0.1 · nipy-nipype-2bb2f81/nipype/interfaces/tests/test_io.py (CWE-918)risk 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 · nipy-nipype-2bb2f81/Vagrantfile (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 · nipy-nipype-2bb2f81/nipype/external/d3.js (CWE-835)risk surface
⚠LLM03Supply Chainlow
Vulnerable/compromised dependencies, models or archives in the artifact.
•Dependency manifest — 9 pip requirements declared · nipy-nipype-2bb2f81/doc/requirements.txtrisk 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 Integritycritical
Middleware tampering with model outputs in transit.
Gateway enforces TLS + response integrity; static check flags output-rewriting code.
•Suspicious code patterns — world-writable chmod 777 · nipy-nipype-2bb2f81/.circleci/config.yml (CWE-732)risk surface
•Suspicious code patterns — dynamic code execution · nipy-nipype-2bb2f81/nipype/algorithms/metrics.py (CWE-95)risk surface
•Suspicious code patterns — OS command execution · nipy-nipype-2bb2f81/nipype/info.py (CWE-78)risk surface
•Suspicious code patterns — pickle deserialization · nipy-nipype-2bb2f81/nipype/interfaces/cmtk/cmtk.py (CWE-502)risk surface
•Suspicious code patterns — disk wipe command; OS command execution · nipy-nipype-2bb2f81/nipype/testing/utils.py (CWE-78)risk surface
⚠ML06AI Supply Chainlow
Compromised PyPI/npm packages, typosquats, unsafe serialized models.
•Dependency manifest — 9 pip requirements declared · nipy-nipype-2bb2f81/doc/requirements.txtrisk 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 (20) · hygiene / uncategorized
•Unrecognized file type — '.codespellrc' is not on the allowlist · nipy-nipype-2bb2f81/.codespellrcrisk surface
•Unrecognized file type — '.coveragerc' is not on the allowlist · nipy-nipype-2bb2f81/.coveragercrisk surface
•Unrecognized file type — '.dockerignore' is not on the allowlist · nipy-nipype-2bb2f81/.dockerignorerisk surface
•Unrecognized file type — '.et' is not on the allowlist · nipy-nipype-2bb2f81/.etrisk surface
•Unrecognized file type — '.git-blame-ignore-revs' is not on the allowlist · nipy-nipype-2bb2f81/.git-blame-ignore-revsrisk surface
•Unrecognized file type — '.gitattributes' is not on the allowlist · nipy-nipype-2bb2f81/.gitattributesrisk surface
•Unrecognized file type — '.gitignore' is not on the allowlist · nipy-nipype-2bb2f81/.gitignorerisk surface
•Unrecognized file type — '.mailmap' is not on the allowlist · nipy-nipype-2bb2f81/.mailmaprisk surface
•Unrecognized file type — '.?' is not on the allowlist · nipy-nipype-2bb2f81/INSTALLrisk surface
•Suspicious network references — suspicious TLD (6 URLs) · nipy-nipype-2bb2f81/Vagrantfilerisk surface
•Unrecognized file type — '.inc' is not on the allowlist · nipy-nipype-2bb2f81/doc/devel/gitwash/git_links.incrisk surface
•Unrecognized file type — '.gpg' is not on the allowlist · nipy-nipype-2bb2f81/docker/files/neurodebian.gpgrisk surface
•Unrecognized file type — '.tcl' is not on the allowlist · nipy-nipype-2bb2f81/nipype/interfaces/fsl/model_templates/feat_contrast_element.tclrisk surface
•Unrecognized file type — '.mat' is not on the allowlist · nipy-nipype-2bb2f81/nipype/interfaces/mrtrix/defhdr.matrisk surface
•Opaque binary content — non-text payload not statically analyzable · nipy-nipype-2bb2f81/nipype/interfaces/mrtrix/defhdr.matrisk surface
•Suspicious network references — raw IP URL (1 URLs) · nipy-nipype-2bb2f81/nipype/interfaces/tests/test_io.pyrisk surface
•Unrecognized file type — '.ini' is not on the allowlist · nipy-nipype-2bb2f81/nipype/pytest.inirisk surface
•Unrecognized file type — '.dvars' is not on the allowlist · nipy-nipype-2bb2f81/nipype/testing/data/ds003_sub-01_mc.DVARSrisk surface
•Unrecognized file type — '.m' is not on the allowlist · nipy-nipype-2bb2f81/nipype/utils/spm_flat_config.mrisk surface
•Suspicious network references — suspicious TLD (2 URLs) · nipy-nipype-2bb2f81/tools/install_spm_mcr.shrisk surface
✔ verified source · pinned nipy-nipype-2bb2f81
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/nipype-neuroimaging-workflows/check). Click a policy:

Consume Nipype — Neuroimaging Workflow Engine 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/nipype-neuroimaging-workflows

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

# CLI
npx ai-supply add nipype-neuroimaging-workflows

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

# MCP tool
install_listing({ "slug": "nipype-neuroimaging-workflows" })
OpenAPI spec →
vlatest
✓ Security: Safe · 1001mo ago

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

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