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catalog / Vision & Image / BlenderMCP
◇MCP serverVision & ImageFree

BlenderMCP

Connect Blender to AI assistants for prompt-assisted 3D modeling, scene creation, and assets.

@ai-supply
安装量35k
↗ 源代码仓库
← More Vision & ImageVision & Image leaderboard →How we grade security →Source ↗

BlenderMCP

BlenderMCP connects Blender to AI assistants like Claude through the Model Context Protocol, enabling prompt-assisted 3D modeling, scene creation, and manipulation.

Using a socket-based add-on and server, the assistant gets two-way communication with Blender to create, modify, and delete objects, control materials and colors, and inspect the scene. It also integrates asset sources such as Poly Haven and Sketchfab and generative tools like Hyper3D Rodin, and can capture viewport screenshots for visual context.

It is intended for 3D artists and hobbyists who want to drive Blender with natural language.

Rating rank
#1
of 12 in Vision & Image
Install rank
#12
of 12 in Vision & Image
Security score
75/100 · B
review
Security rank
#5
of 12 in Vision & Image
Installs
35k
cat avg 279k
This listing vs category average
Installs
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cat avg
Security (of 100)
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See the Vision & Image leaderboard →
! Security: Review · 7575/100 · grade Bscanned 17h ago
✓ no compromise signals8 risk-surface · 6/20 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 · high confidence (static)
Tools (3)
get_viewport_screenshotget_hunyuan3d_statuspoll_hunyuan_job_status
⚑ filesystem⚑ network⚑ secrets
egress → blendermcp.org, www.youtube.com, discord.gg, www.coderabbit.ai, astral.sh, docs.astral.sh, pipx.pypa.io, cursor.com +9

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.

OWASP Top 10 for LLM Applications
⚠LLM03Supply Chaincritical
Vulnerable/compromised dependencies, models or archives in the artifact.
•Vulnerable dependencies — 19 known vulnerabilities in: h11@0.14.0, idna@3.10, mcp@1.3.0, pygments@2.19.1, python-dotenv@1.0.1, starlette@0.46.0 (CWE-1395)known CVE · -25 pts
⚠LLM05Improper Output Handlinghigh
Code that pipes model/user output into shell, eval, SQL or paths unsafely.
•Suspicious code patterns — pipe-to-shell install · ahujasid-blender-mcp-6e99eb5/README.md (CWE-494)risk surface
•Suspicious code patterns — dynamic code execution · ahujasid-blender-mcp-6e99eb5/addon.py (CWE-95)risk surface
⚠LLM06Excessive Agencymedium
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 14 distinct host(s) · ahujasid-blender-mcp-6e99eb5/README.mdrisk surface
•Broad capability surface — 4 high-impact capability categories referenced — verify least-privilege · ahujasid-blender-mcp-6e99eb5/addon.py (CWE-272)risk surface
•External endpoints declared — 7 distinct host(s) · ahujasid-blender-mcp-6e99eb5/addon.pyrisk surface
•External endpoints declared — 1 distinct host(s) · ahujasid-blender-mcp-6e99eb5/pyproject.tomlrisk 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 · ahujasid-blender-mcp-6e99eb5/src/blender_mcp/server.py (CWE-835)risk 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.
✓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.
•Vulnerable dependencies — 19 known vulnerabilities in: h11@0.14.0, idna@3.10, mcp@1.3.0, pygments@2.19.1, python-dotenv@1.0.1, starlette@0.46.0 (CWE-1395)known CVE · -25 pts
⚠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 · ahujasid-blender-mcp-6e99eb5/README.md (CWE-494)risk surface
•Suspicious code patterns — dynamic code execution · ahujasid-blender-mcp-6e99eb5/addon.py (CWE-95)risk 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 (3) · hygiene / uncategorized
•Unrecognized file type — '.gitignore' is not on the allowlist · ahujasid-blender-mcp-6e99eb5/.gitignorerisk surface
•Unrecognized file type — '.python-version' is not on the allowlist · ahujasid-blender-mcp-6e99eb5/.python-versionrisk surface
•Unrecognized file type — '.?' is not on the allowlist · ahujasid-blender-mcp-6e99eb5/LICENSErisk surface
✔ verified source · pinned ahujasid-blender-mcp-6e99eb5 · changed since last scan (-25 pts)
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/blender-mcp-server/check). Click a policy:

Consume BlenderMCP 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/blender-mcp-server

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

# CLI
npx ai-supply add blender-mcp-server

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

# MCP tool
install_listing({ "slug": "blender-mcp-server" })
OpenAPI spec →
vlatest
! Security: Review · 751d 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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