Trust score for AI-generated code: scan repos, guard agent file writes, get a 0-100 score.
io.github.elberacasa/umbra MCP Server
The MCP server io.github.elberacasa/umbra provides a “trust score for AI-generated code.” It can scan repositories to evaluate code quality and security signals, and it supports guarding agent file writes to help prevent unsafe changes. It returns a 0–100 score based on its checks.
🛠️ Key Features
Repository scanning for AI-generated code trust evaluation
Guard agent file writes
Output: a 0–100 trust score
🚀 Use Cases
Assessing trustworthiness of AI-generated code before adoption
Performing static-analysis and security checks on repo changes
Supporting developer workflows for code quality verification
⚡ Developer Benefits
Programmatic scoring of code trust (0–100)
Integration with developer tools and LLM coding workflows
Alignment with static-analysis, SAST, and security-scanner needs
⚠️ Limitations
Scope appears limited to trust scoring, scanning, and write-guard behavior; other actions and tool interfaces are not specified in the provided data
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 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:
Finding
Repos hit
Hardcoded-secret findings (committed .env, service keys in source)
25%
API routes with no auth check
26%
Injection sinks (SQL interpolation, unsafe HTML injection)
49%
Entire databases / SQL dumps committed to git
13%
At least one critical finding
10%
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).
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
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):
code
$ npx @elberacasa/umbra ./fixtures/claims-app --deep
UMBRA TRUST SCORE: 49/100 🔴
SAFE ✅ 100/100 — 0 findings
CLEAN ✅ 100/100 — 2 findings
RUNS — 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
Axis
Question
How it's measured
SAFE (35%)
Is it vulnerable?
13 deterministic static rules, every scan, fully offline.
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
Umbra
Traditional SAST (Semgrep, Snyk Code)
Secret scanners (trufflehog, Gitleaks)
Agent review bots
Built for AI-generated code
✅
generic rulesets
secrets only
✅
Verifies the app builds, boots, and answers HTTP
✅ (sandbox)
—
—
—
Replays agent claims, caps liars below passing
✅
—
—
—
Deterministic score, versioned rubric
✅
findings list
findings list
prose 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:
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:
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.
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 install
npm run build
npm 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.