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E2B Fragments

Open-source AI-powered app starter built with Next.js and E2B sandboxes — let users generate and run full-stack apps from natural language.

@ai-supply
Installs36k
⟳ upstream main@6ecd7bc · updated 10d ago
↗ Source repository
← More CodingCoding leaderboard →How we grade security →Source ↗
! Grade B · 88/100 · ReviewSecurity assessment
✓No compromise signals4capabilities surfaced1known CVE8of 20 OWASP controls clear
Suspicious code patternsSuspicious network referencesBroad capability surfaceSuspicious code patterns
scanned 7d ago·osv · gitleaks · opengrep · picklescan + heuristics·full breakdown in the Security tab ↓

E2B Fragments

E2B Fragments is a production-ready open-source starter that lets users generate and run full-stack web applications from natural language prompts. Built on Next.js, it uses E2B secure cloud sandboxes for safe code execution and streams generated apps in real time — think an open-source v0.dev or Replit Agent you can self-host.

Key features

  • AI code generation — Claude, GPT-4o, or Fireworks generate runnable apps from a text prompt
  • E2B sandbox execution — generated code runs in isolated cloud containers (no host machine risk)
  • Real-time streaming — code and UI appear progressively as the model generates
  • Multi-framework support — generates Next.js, Streamlit, Python, and Gradio apps
  • Bring your own model — swap in any supported LLM via a single env var
  • One-click deploy — ships with a Vercel deploy button and Docker support

Quick start

npx ai-supply add e2b-fragments-ai-app-starter

# Or clone and run directly
git clone https://github.com/e2b-dev/fragments
cd fragments
cp .env.example .env.local
# Set E2B_API_KEY and ANTHROPIC_API_KEY in .env.local
npm install
npm run dev

Open http://localhost:3000 and type a prompt like: "Build a to-do app with local storage".

Curated mirror of the open-source E2B Fragments project (Apache-2.0). Install upstream from the repository.

Rating rank
#1
of 27 in Coding
Install rank
#15
of 27 in Coding
Security score
88/100 · B
review
Security rank
#12
of 27 in Coding
Installs
36k
cat avg 157k
This listing vs category average
Installs
this
cat avg
Security (of 100)
this
cat avg
Adoption trend
See the Coding leaderboard →
! Security: Review · 8888/100 · grade Bscanned 7d ago
✓ no compromise signals5 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.

Prompt card · med confidence (static)
{id}{process.env.NEXT_PUBLIC_SITE_URL}{process.env.ZEROBOUNCE_API_KEY}{email}{currentFragment.file_path}{currentFragment.code}{sbx.sandboxId}{file.file_path}{fragment.file_path}{target}{providerName}{model.providerId}{lang}{imgInBase64}{number}{Unit}{d}{file.type}{base64}

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 Chainhigh
Vulnerable/compromised dependencies, models or archives in the artifact.
•Dependency manifest — 57 npm dependencies declared · e2b-dev-fragments-6ecd7bc/package.jsonrisk surface
•Vulnerable dependencies — 54 known vulnerabilities in: @ai-sdk/provider-utils@1.0.22, @ai-sdk/provider-utils@2.2.8, ai@3.4.33, ajv@6.12.6, brace-expansion@1.1.12, brace-expansion@2.0.2, brace-expansion@5.0.4, flatted@3.3.1 (CWE-1395)known CVE · -12 pts
⚠LLM05Improper Output Handlinghigh
Code that pipes model/user output into shell, eval, SQL or paths unsafely.
•Suspicious code patterns — environment/secret exfiltration · e2b-dev-fragments-6ecd7bc/app/actions/validate-email.ts (CWE-200)risk surface
•Suspicious code patterns — destructive rm -rf / · e2b-dev-fragments-6ecd7bc/sandbox-templates/nextjs-developer/template.ts (CWE-78)risk surface
⚠LLM06Excessive Agencylow
Over-broad tool/permission surface or unrestricted egress.
•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege · e2b-dev-fragments-6ecd7bc/package-lock.json (CWE-272)risk 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.
✓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 Chainhigh
Compromised PyPI/npm packages, typosquats, unsafe serialized models.
•Dependency manifest — 57 npm dependencies declared · e2b-dev-fragments-6ecd7bc/package.jsonrisk surface
•Vulnerable dependencies — 54 known vulnerabilities in: @ai-sdk/provider-utils@1.0.22, @ai-sdk/provider-utils@2.2.8, ai@3.4.33, ajv@6.12.6, brace-expansion@1.1.12, brace-expansion@2.0.2, brace-expansion@5.0.4, flatted@3.3.1 (CWE-1395)known CVE · -12 pts
⚠ML09Output Integrityhigh
Middleware tampering with model outputs in transit.
Gateway enforces TLS + response integrity; static check flags output-rewriting code.
•Suspicious code patterns — environment/secret exfiltration · e2b-dev-fragments-6ecd7bc/app/actions/validate-email.ts (CWE-200)risk surface
•Suspicious code patterns — destructive rm -rf / · e2b-dev-fragments-6ecd7bc/sandbox-templates/nextjs-developer/template.ts (CWE-78)risk surface
⚠ML05Model Theftlow
Unlicensed re-distribution / license-incompatible derivatives.
Static check verifies license declaration; extraction throttling is runtime.
•No license signal — no SPDX id or license keyword found · e2b-dev-fragments-6ecd7bc/.env.templaterisk 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.
✓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 (7) · hygiene / uncategorized
•Unrecognized file type — '.template' is not on the allowlist · e2b-dev-fragments-6ecd7bc/.env.templaterisk surface
•Unrecognized file type — '.gitignore' is not on the allowlist · e2b-dev-fragments-6ecd7bc/.gitignorerisk surface
•Unrecognized file type — '.prettierrc' is not on the allowlist · e2b-dev-fragments-6ecd7bc/.prettierrcrisk surface
•Unrecognized file type — '.?' is not on the allowlist · e2b-dev-fragments-6ecd7bc/CODEOWNERSrisk surface
•Suspicious network references — suspicious TLD (5 URLs) · e2b-dev-fragments-6ecd7bc/lib/models.tsrisk surface
•Unrecognized file type — '.mjs' is not on the allowlist · e2b-dev-fragments-6ecd7bc/next.config.mjsrisk surface
•Unrecognized file type — '.json5' is not on the allowlist · e2b-dev-fragments-6ecd7bc/renovate.json5risk surface
✔ verified source · pinned e2b-dev-fragments-6ecd7bc
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/e2b-fragments-ai-app-starter/check). Click a policy:

Consume E2B Fragments 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/e2b-fragments-ai-app-starter

# Gate against your org policy (returns { pass, violations })
curl -X POST https://ai-supply.store/api/v1/trust/e2b-fragments-ai-app-starter/check \
  -H "Content-Type: application/json" \
  -d '{"minGrade":"B","denyPermissions":["shell"],"denyUnknownEgress":true}'

# CLI
npx ai-supply add e2b-fragments-ai-app-starter

# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/e2b-fragments-ai-app-starter/install \
  -H "Authorization: Bearer $AIM_KEY"

# MCP tool
install_listing({ "slug": "e2b-fragments-ai-app-starter" })
OpenAPI spec →
vlatest
! Security: Review · 881mo 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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