Umbra

io.github.elberacasa由 GitHub 账号 elberacasa 发布v1.2.0更新于 Oct 24, 2026

Trust score for AI-generated code: scan repos, guard agent file writes, get a 0-100 score.

已验证STDIO仅桌面开发者工具安全与监控

概览

AI 生成的概览

Umbra 让助手扫描代码仓库的安全与质量问题、在文件写入前进行拦截,并给出确定性的 0-100 信任分。

可以做什么
  • 扫描仓库并返回 0-100 信任分,每项发现都带文件与行号证据
  • 在代理写入文件前检查内容,决定放行、警告或阻止写入
  • 查询仓库当前的信任分
  • 从静态安全规则、能否构建启动、文档声明是否属实、代码整洁度四个维度评分
适用场景
适合在 AI 编码代理生成或修改代码后,用本地独立检查发现安全问题、已提交的密钥和夸大的声明,再决定是否接受或合并这些改动。
运行要求
以本地进程方式通过 npm 包 @elberacasa/umbra(npx)运行,需要 Node.js。未声明需要账号、API 密钥或环境变量。更深入的沙箱验证需要运行中的 Docker 守护进程,没有时相关维度会被跳过。
安装前请注意
拦截功能可能阻止代理写入文件,安装命令会向仓库写入钩子和 CI 配置。修复与基线模式会修改仓库中的文件。发布分数会把结果发送到托管徽章服务。扫描据称在本地进行,但只应扫描自己拥有或获准审计的仓库。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Umbra,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

[Umbra: the trust score for AI-generated code]

Everyone is vibecoding. Nobody is verifying. Umbra scores it.

Umbra is a deterministic Trust Score (0–100) for AI-generated code: the vibe coding security scanner that verifies what your agent shipped, not what it claimed. One command, fully local, evidence for every finding.

→ umbra-badge.umbrabadge.workers.dev — the landing, live badges and report pages

[npm version] [npm downloads] [GitHub stars] [website: live] [Glama MCP score] [MCP registry: listed] [license: MIT] [CI] [rubric v4] [node >=20]

Quickstart · Demo · The Audit · How it works · The Four Axes · FAQ · Roadmap · Contributing · Website

[Umbra scanning a vibe-coded app: Trust Score 30/100]

Why Umbra exists

Studies put exploitable vulnerabilities in 40 to 60 percent of AI-generated code, and coding agents routinely claim "all tests pass" when three do. The tooling for writing code with AI is a year ahead of the tooling for trusting it. Umbra closes that gap: SAST rebuilt for how software gets written now, plus sandboxed verification that catches what static rules cannot.

One command scans any repo an agent produced (Claude Code, Cursor, Copilot, Windsurf, Lovable) and returns a score with file:line evidence for every finding. With --deep it goes further: Umbra builds and boots the repo in a locked-down Docker sandbox, then replays the agent's own claims against reality. If the agent is lying about tests, the score is capped below passing, with receipts.

The audit: 61 vibe-coded repos, scanned

We ran Umbra over 61 public, actively-maintained AI-built repos and published everything. The Vibe-Coding Security Audit:

FindingRepos hit
Hardcoded-secret findings (committed .env, service keys in source)25%
API routes with no auth check26%
Injection sinks (SQL interpolation, unsafe HTML injection)49%
Entire databases / SQL dumps committed to git13%
At least one critical finding10%
Zero scored findings (genuinely clean)7 of 61

Mean trust score: 74/100. One in five repos fails outright. The full report has per-class deep dives with representative snippets and fixes, the complete per-repo table, and an honest methodology section — including the false positives we found in our own rules while running it, and fixed (rubric v4).

Quickstart

bash
npx umbra-scan            # check — scans the directory you're standing innpx umbra-scan --fix      # heal — applies provably-safe fixes, shows the score climbingnpx umbra-scan --setup    # protect — pre-commit gate, PR checks, agent guardrails

That's the whole interface. Three verbs: check, heal, protect.

Using an AI coding agent? Umbra is built to be driven by agents, not just run by humans:

  • Any agent — it reads this repo's AGENTS.md / llms.txt and knows what to do. Or tell yours: "check this repo with umbra."
  • Claude Code / Kimi Code — --setup installs PreToolUse hooks so every file the agent writes is guarded before it lands.
  • Claude Code, Cursor, Copilot, Windsurf — the trust-review skill makes the agent scan its own work before declaring done.
  • MCP-native agents — add umbra-mcp (npx --yes -p @elberacasa/umbra umbra-mcp) and the agent gets scan_repo, guard_content, and get_score as tools.

Real output, scanning a typical vibe-coded Next.js app (fixtures/bad-app in this repo, Trust Score 30/100):

$ npx umbra-scan ./fixtures/bad-app
UMBRA TRUST SCORE: 30/100  🔴
SAFE   🔴 5/100 — 14 findingsCLEAN  ✅ 87/100 — 10 findingsRUNS   — not measured — run with --deepHONEST — not measured — run with --deep
Score computed over measured axes only (full rubric: SAFE 35%, RUNS 25%, HONEST 25%, CLEAN 15%). Rubric v4.…plus 7 further findings beyond the per-rule cap (see report)
Top findings:  [safe/hardcoded-secrets] Hardcoded Supabase service_role JWT — bypasses all row level security — .env:2  [safe/hardcoded-secrets] Hardcoded Supabase service_role JWT — bypasses all row level security — lib/supabase.ts:5  [safe/supabase-antipatterns] Supabase service_role key reachable from client-side code — full database bypass for anyone who opens the bundle — .env:2  [safe/supabase-antipatterns] Supabase service_role key reachable from client-side code — full database bypass for anyone who opens the bundle — app/components/UserList.tsx:10  [safe/hardcoded-secrets] Committed environment file with secret values: .env — .env:1
Notes (low confidence — not scored):  [safe/missing-rate-limit] Auth endpoint with no rate-limiting signal in the repo — brute-force / credential-stuffing exposure (heuristic) — app/api/login/route.ts:3
Badge: [![Umbra Trust Score](https://img.shields.io/badge/Umbra_Trust_Score-30-red)](https://github.com/elberacasa/umbra)

The exit code is 1 when the score is below 50, so CI can gate on it.

All commands and flags (the expert layer — most users never need these)
bash
umbra [path]               # path defaults to the current directoryumbra [path] --json        # machine-readable outputumbra [path] --offline     # skip npm registry checks, fully localumbra [path] --deep        # verify RUNS and HONEST in a Docker sandboxumbra [path] --report      # write UMBRA.md: an agent-actionable task listumbra [path] --fix         # apply provably-safe fixes and re-scan (score before → after)umbra [path] --dry-run     # preview --fix without writing anythingumbra [path] --baseline-write  # write .umbra-baseline.json: grandfather current findings, gate only on new onesumbra [path] --baseline <path> # use an explicit baseline file ("write" is shorthand for --baseline-write)umbra [path] --publish     # self-report the score to the hosted badge service (live README badge)umbra setup                # install everything (hooks + Action + agent guards)umbra init                 # only the pre-commit hook + GitHub Actionumbra protect              # only the agent PreToolUse hooks (--remove uninstalls)umbra guard --stdin        # hook entrypoint (agents call this, not humans)umbra mcp                  # run the MCP server (bin: umbra-mcp)

The canonical package is @elberacasa/umbra; umbra-scan is the short alias. Same engine either way.

How it works

repo in   │   ▼  Layer 0 · static rules (17 SAFE + CLEAN rules, 0 tokens, <1s)   ▼  Layer 1 · evidence gating (confidence-scored, low never moves the score)   ▼  Layer 2 · --deep sandbox (Docker: build, boot, HTTP probe, claim replay)   │   ▼  deterministic Trust Score + verdict + badge

Every finding carries a confidence level and file:line evidence. Only high and medium confidence findings move the score; hunches go to a notes section. The rubric is versioned (currently v3), so the same repo always gets the same score. Full math in RUBRIC.md.

The immune layer: guard the write, not just the repo

Scanning finds problems after they land. The immune layer checks every file your agent writes before it lands. umbra protect installs PreToolUse hooks into Claude Code and Kimi Code (auto-detected, one command); the same engine backs the umbra-mcp server for MCP-native agents.

[Umbra blocking an agent's attempt to write a live key into .env]

mermaid
flowchart LR    CC[Claude Code hook] --> E    KC[Kimi Code hook] --> E    MCP["umbra-mcp: guard_content"] --> E    E{"guardContent(file, content)<br/>file rules + path guard"} -->|allow / warn| W[write lands]    E -->|"block (exit 2)"| B["reason fed back:<br/>agent fixes the root cause"]
bash
npx umbra-scan protect   # install the hooks; --remove uninstalls cleanly

A leaked Stripe key or an alg: none JWT never reaches the file. The path guard hard-blocks agent writes into .git/hooks and .git/config (CVE-2026-26268, the agent-planted git hook escape), and live credentials going into .env. Blocking is reserved for high-confidence critical/high findings; everything else warns, and every failure fails open. Verdicts land in ~0.2 ms, so the guard never slows the agent down. Full story: docs/immune-layer.md.

--deep: verify AI code, don't trust it

The fast scan is static. --deep is LLM code verification with evidence. Umbra copies the repo into a throwaway Docker container (no network at runtime, 512 MB / 1 CPU hard limits, 120-second kill switch), builds it, boots it, HTTP-probes its endpoints, and replays every claim found in READMEs and agent artifacts against what actually happens. Slower (minutes, not seconds) and needs a running Docker daemon. Without Docker the sandboxed axes are skipped and left out of the score; unverifiable is never punished.

Real output, deep-scanning a repo whose README lies (fixtures/claims-app, capped at 49/100 by the liar cap):

$ npx @elberacasa/umbra ./fixtures/claims-app --deep
UMBRA TRUST SCORE: 49/100  🔴
SAFE   ✅ 100/100 — 0 findingsCLEAN  ✅ 100/100 — 2 findingsRUNS   — not measured — No detectable run path (no Dockerfile, no package.json start script or main entry)HONEST ⚠️ 50/100 — 2 claims failed, 2 verified, 1 unverifiable
Score computed over measured axes only (full rubric: SAFE 35%, RUNS 25%, HONEST 25%, CLEAN 15%). Rubric v4.Score capped below passing: a documented claim was verified false. Trust is the product.
Claim receipts:  CLAIM FAILED: "14 tests pass" — README.md:7 — actually 3 tests pass, 0 fail  CLAIM FAILED: "build passes" — README.md:9 — actually build exits 1  CLAIM VERIFIED: "All tests pass" — CLAUDE.md:3 — 3 tests pass  CLAIM VERIFIED: "All tests are passing" — README.md:8 — 3 tests pass

Any claim verified false caps the total at 49: a repo caught lying does not get a passing trust score. For contrast, a genuinely working app (fixtures/runnable-app) scores 100/100 under --deep.

The Four Axes

AxisQuestionHow it's measured
SAFE (35%)Is it vulnerable?13 deterministic static rules, every scan, fully offline.
RUNS (25%)Does it actually build and boot?Docker sandbox: install, build, start, HTTP probe. (--deep)
HONEST (25%)Is the agent lying about tests or the build?Claims extracted from READMEs and agent files, replayed against sandbox reality, receipts emitted. (--deep)
CLEAN (15%)How much is slop?Static rules: dead exports, unused deps, mega-files, duplication.

The SAFE rules cover the failures AI-generated code security actually ships: hardcoded secrets (Stripe keys, JWTs, connection strings), Supabase service-role keys exposed client-side and missing Supabase RLS, missing auth on API routes, injection sinks, rate-limit hints, hallucinated and typosquatted dependencies, CORS wildcard with credentials, JWT misconfig (alg: none, no expiry, decode-as-authorization), debug flags and stack-trace leaks, committed sensitive files (.pem, id_rsa, SQL dumps), and default credentials.

It also lints the agent's own setup — the surface nobody else covers: prompt-injection payloads in instruction files (CLAUDE.md, .cursor/rules, skills: zero-width Unicode, override phrases in HTML comments) and dangerous MCP configs (literal API keys in .mcp.json, unpinned npx -y servers, curl | sh installers). These run in the guard too, so an agent editing its own config gets checked mid-write.

Umbra vs. existing tools

UmbraTraditional SAST (Semgrep, Snyk Code)Secret scanners (trufflehog, Gitleaks)Agent review bots
Built for AI-generated code✅generic rulesetssecrets only✅
Verifies the app builds, boots, and answers HTTP✅ (sandbox)———
Replays agent claims, caps liars below passing✅———
Deterministic score, versioned rubric✅findings listfindings listprose review
Agent-native surfaces (skill, Action, MCP)✅——partial

Existing tools answer "is this code pattern dangerous?" Umbra answers the question vibe coding actually raises: "the AI wrote this, can I trust it?"

The badge

Every scan prints badge markdown. Paste it in your README and your repo advertises its own trust score:

markdown
[![Umbra Trust Score](https://img.shields.io/badge/Umbra_Trust_Score-30-red)](https://github.com/elberacasa/umbra)

[Umbra Trust Score]

Live badges are one flag away: run with --publish (or the Action's publish: true) and your score reports to the hosted badge service, so your README always shows the current number with a full report page behind the click — self-reported by your CI, labeled as such:

markdown
[![Umbra Trust Score](https://umbra-badge.umbrabadge.workers.dev/badge/OWNER/REPO.svg)](https://umbra-badge.umbrabadge.workers.dev/OWNER/REPO)

One engine, every surface

  • CLI (npx @elberacasa/umbra): the core, available today. Short alias: npx umbra-scan.
  • Agent skill: a trust-review skill installable into Claude Code, Cursor, Copilot, and Windsurf, so the agent checks its own work before you do. Claude Code / Cursor / Copilot security, from inside the agent.
  • GitHub Action: uses: elberacasa/umbra@v1 comments the Trust Score on every PR. Trust gating in CI, zero local setup.
  • umbra setup: the one-word installer — pre-commit gate, PR score comments, and PreToolUse guard hooks for detected agents, all idempotent and clobber-free. (init and protect remain for piecemeal installs.)
  • umbra protect: installs PreToolUse hooks into Claude Code and Kimi Code (auto-detected, idempotent, --remove to uninstall) so Umbra reviews every agent write mid-stream and blocks dangerous ones before they land.
  • MCP server (umbra-mcp): agents call Umbra mid-stream and catch their own mistakes before the code lands. Add it with npx --yes -p @elberacasa/umbra umbra-mcp.

Day-to-day recipes (CI gating, JSON parsing, hooks): docs/daily-use.md.

Roadmap

  • v0.1 (shipped): CLI, SAFE + CLEAN static axes, deterministic score, verdict output, badge markdown.
  • v0.2 (shipped): the surfaces. Agent skill, GitHub Action, umbra init.
  • v0.3 (shipped, current): RUNS axis (sandbox build, boot, HTTP probe) and HONEST axis (claim receipts plus the liar cap).
  • v1.0 (shipped): the immune layer. Umbra sits between the agent and your codebase, intercepting writes mid-stream and scoring them before they land. Full story in docs/immune-layer.md.
  • Beyond: attack graphs across your dependency tree, a security twin of your app that gets probed so production doesn't, hosted report permalinks behind every badge.

The wedge is a score. The destination is the verification layer every AI-built repo runs through.

FAQ

How do I adopt Umbra in a repo that already has findings? Run npx umbra-scan --baseline-write once. Umbra writes .umbra-baseline.json into the repo root, and from then on the gate only blocks new issues — existing findings are grandfathered (the verdict shows baseline: N existing findings grandfathered (M new)), so you fix forward instead of boiling the ocean. Commit the baseline file so the whole team and CI share it.

How is Umbra different from Semgrep, Snyk, or trufflehog? They scan code patterns; Umbra verifies outcomes. Static rules are one input to the SAFE axis. Umbra additionally boots the app in a sandbox to prove it runs, and replays the agent's documented claims to prove it isn't lying. "README says 14 tests pass, actually 3 do" costs the repo a passing grade.

Does Umbra send my code anywhere? No. Scanning is fully local; --offline skips even the npm registry checks. --deep runs your repo in a local Docker container with no network at runtime. Nothing leaves your machine.

Does it need Docker? Only for --deep (RUNS and HONEST). The default fast scan is pure static analysis. Without Docker the sandboxed axes are skipped and excluded from the score, never punished.

What languages does it support? JavaScript and TypeScript (including Next.js and Supabase apps) have the deepest coverage today, which is where most vibe-coded repos live. The rule engine is extensible; new rules need a fixture and a test.

Is the score reproducible? Yes. Same repo, same rubric version, same score, every time. The rubric is versioned (v2) and printed in every report, and low-confidence findings never affect it. Skipped axes are excluded and renormalized over, never punished.

What does it catch that my AI agent won't mention? The classics of AI-generated code: a Supabase service_role JWT shipped to the browser (bypasses all row level security), live Stripe keys in .env, API routes with no auth check, alg: none JWTs, CORS * with credentials, hallucinated dependencies that don't exist on npm, and whether its own claims about tests and builds are true.

Can Umbra stop my agent mid-write? Yes, via hooks. Run npx @elberacasa/umbra protect and Umbra installs a PreToolUse hook into Claude Code and/or Kimi Code that reviews every Write/Edit/MultiEdit before it lands. Only high-confidence critical and high severity findings block (a wrong block gets tools uninstalled, so when in doubt Umbra warns), the .git/hooks path guard blocks git-hook planting (CVE-2026-26268) outright, and the guard fails open on its own errors so it never breaks your flow. Hooks are a guardrail, not a sandbox; details in docs/immune-layer.md.

Can my AI coding agent use Umbra directly? Yes, that is the design. The repo ships an AGENTS.md and llms.txt so assistants know exactly when and how to run it, and the agent skill makes Claude Code, Cursor, Copilot, and Windsurf scan their own work before declaring a task done.

Contributing

Issues and PRs welcome. See CONTRIBUTING.md. The highest-value contributions right now: new SAFE/CLEAN rules with fixtures and tests, false-positive reports (severity-one bugs here), renders against real AI-generated repos, and new harness adapters for umbra protect.

Build and test before submitting:

bash
npm installnpm run buildnpm test

Ethical use

Umbra is a defensive tool. Scan repos you own, repos you are about to depend on, or repos you have permission to audit. Findings point at weaknesses; they are not exploits, and publishing someone else's low score to shame them is not the point. The point is that "the AI wrote it" stops being the end of the verification conversation.

License

MIT

来源:README.md,提交 ba2807a

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v1.2.0最新Sep 16, 2026

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