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OpenLLM

Production LLM serving platform by BentoML — deploy any open-source LLM with one command.

@ai-supply
Installationen205k
⟳ upstream v0.6.30 · updated 1y ago
↗ Quell-Repository
← More DevOps & InfraDevOps & Infra leaderboard →How we grade security →Source ↗
! Grade B · 75/100 · ReviewSecurity assessment
✓No compromise signals9capabilities surfaced1known CVE9of 20 OWASP controls clear
Broad capability surfacePotentially unbounded loopVulnerable dependenciesExternal endpoints declared · expected
scanned 18d ago·osv · gitleaks · opengrep · picklescan + heuristics·full breakdown in the Security tab ↓

OpenLLM

OpenLLM is an open-source platform for deploying and operating large language models in production. Built by the BentoML team, it provides a unified interface to run, fine-tune, and deploy dozens of open-source LLMs — locally or on any cloud — with built-in quantization, streaming, and OpenAI-compatible APIs.

Key Features

  • One-command deployment: openllm start llama3 — downloads, quantizes, and serves
  • OpenAI-compatible API: drop-in for existing integrations
  • Quantization: bitsandbytes 4-bit/8-bit, GPTQ, AWQ
  • Streaming support: SSE and gRPC streaming
  • Cloud-native: integrates with BentoCloud, AWS SageMaker, GCP, Azure
  • Supports 50+ models: Llama, Mistral, Phi, Gemma, Falcon, StarCoder, Baichuan

Quick Start

pip install openllm

# Start a Llama 3 server
openllm start meta-llama/Meta-Llama-3-8B-Instruct

# Query via OpenAI client
from openai import OpenAI
client = OpenAI(base_url="http://localhost:3000/v1", api_key="na")
response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3-8B-Instruct",
    messages=[{"role": "user", "content": "What is vLLM?"}]
)

Install via ai-supply

npx ai-supply add openllm-model-serving-platform

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

Rating rank
#1
of 23 in DevOps & Infra
Install rank
#8
of 23 in DevOps & Infra
Security score
75/100 · B
review
Security rank
#15
of 23 in DevOps & Infra
Installs
205k
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See the DevOps & Infra leaderboard →
! Security: Review · 7575/100 · grade Bscanned 18d ago
✓ no compromise signals10 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 · med confidence (static)
⚑ filesystem⚑ shell⚑ network⚑ secrets
egress → www.contributor-covenant.org, docs.github.com, semver.org, pre-commit.ci, l.bentoml.com, git-scm.com, www.python.org, docs.bentoml.com +12
auth: api_keywww.contributor-covenant.orggithub.comdocs.github.comsemver.orgpre-commit.cil.bentoml.comgit-scm.comwww.python.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.
•Vulnerable dependencies — 125 known vulnerabilities in: aiohttp@3.11.12, bentoml@1.4.8, dulwich@0.22.7, filelock@3.17.0, h11@0.14.0, idna@3.10, jinja2@3.1.5, pygments@2.19.1 (CWE-1395)known CVE · -25 pts
⚠LLM05Improper Output Handlingmedium
Code that pipes model/user output into shell, eval, SQL or paths unsafely.
•Suspicious code patterns — OS command execution · bentoml-OpenLLM-ec2355c/gen_readme.py (CWE-78)expected
⚠LLM06Excessive Agencymedium
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 2 distinct host(s) · bentoml-OpenLLM-ec2355c/.github/CODE_OF_CONDUCT.mdexpected
•External endpoints declared — 1 distinct host(s) · bentoml-OpenLLM-ec2355c/.github/ISSUE_TEMPLATE/bug_report.ymlexpected
•External endpoints declared — 3 distinct host(s) · bentoml-OpenLLM-ec2355c/.gitignoreexpected
•External endpoints declared — 6 distinct host(s) · bentoml-OpenLLM-ec2355c/DEVELOPMENT.mdexpected
•External endpoints declared — 11 distinct host(s) · bentoml-OpenLLM-ec2355c/README.mdexpected
•External endpoints declared — 4 distinct host(s) · bentoml-OpenLLM-ec2355c/pyproject.tomlexpected
•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege · bentoml-OpenLLM-ec2355c/src/openllm/cloud.py (CWE-272)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 · bentoml-OpenLLM-ec2355c/src/openllm/local.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 — 125 known vulnerabilities in: aiohttp@3.11.12, bentoml@1.4.8, dulwich@0.22.7, filelock@3.17.0, h11@0.14.0, idna@3.10, jinja2@3.1.5, pygments@2.19.1 (CWE-1395)known CVE · -25 pts
⚠ML09Output Integritymedium
Middleware tampering with model outputs in transit.
Gateway enforces TLS + response integrity; static check flags output-rewriting code.
•Suspicious code patterns — OS command execution · bentoml-OpenLLM-ec2355c/gen_readme.py (CWE-78)expected
§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 (9) · hygiene / uncategorized
•Unrecognized file type — '.editorconfig' is not on the allowlist · bentoml-OpenLLM-ec2355c/.editorconfigrisk surface
•Unrecognized file type — '.template' is not on the allowlist · bentoml-OpenLLM-ec2355c/.envrc.templaterisk surface
•Unrecognized file type — '.git-blame-ignore-revs' is not on the allowlist · bentoml-OpenLLM-ec2355c/.git-blame-ignore-revsrisk surface
•Unrecognized file type — '.gitattributes' is not on the allowlist · bentoml-OpenLLM-ec2355c/.gitattributesrisk surface
•Unrecognized file type — '.?' is not on the allowlist · bentoml-OpenLLM-ec2355c/.github/CODEOWNERSrisk surface
•Unrecognized file type — '.gitignore' is not on the allowlist · bentoml-OpenLLM-ec2355c/.gitignorerisk surface
•Unrecognized file type — '.python-version-default' is not on the allowlist · bentoml-OpenLLM-ec2355c/.python-version-defaultrisk surface
•Unrecognized file type — '.cff' is not on the allowlist · bentoml-OpenLLM-ec2355c/CITATION.cffrisk surface
•Unrecognized file type — '.tpl' is not on the allowlist · bentoml-OpenLLM-ec2355c/README.md.tplrisk surface
✔ verified source · pinned bentoml-OpenLLM-ec2355c
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/openllm-model-serving-platform/check). Click a policy:

Consume OpenLLM 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/openllm-model-serving-platform

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

# CLI
npx ai-supply add openllm-model-serving-platform

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

# MCP tool
install_listing({ "slug": "openllm-model-serving-platform" })
OpenAPI spec →
vlatest
! Security: Review · 751mo ago

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

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