bash-vet-mcp

MCP server that vets LLM-emitted shell commands BEFORE execution — detects rm -rf nested deep in chains, package-manager glob removal (apt remove '*nvidia*'), dd/mkfs/wipefs filesystem destruction, chmod 777 / chown -R privilege blast, network-exfil via curl | bash, chained shutdown/reboot, and git destructive ops. Sub-second, local, free, MCP-native — designed to be called inline by Claude Code / Cursor / Cline / OpenClaw before approving any agent-proposed command. Defensive complement to MCP shell-execution servers (MCPShell, mcp-shell, mcp-bash).

What it does

Production AI agents have a quiet failure mode in shell-command execution: the agent emits a chained command, the operator pattern-matches the start of the line, and a destructive fragment nested deep in the chain (&&, ;, |) gets executed by accident.

A working engineer (@chiefofautism, 158↑ / 135 RTs / 11.5K views) puts it more bluntly:

"claude code runs shell commands with YOUR permissions. it can rm -rf your repo. it can force push to main. it can drop your database. and it will do it confidently while telling you that he cleaned up the project structure"

The danger isn't just the destructive command — it's the confident misreport that follows. bash-vet attacks the first half of that pair (the "rm -rf / force-push / drop database" part); pair it with openclaw-output-vetter-mcp for the second half (the "while telling you he cleaned up the project structure" part).

• Buried rm -rf. r/LocalLLaMA "One bash permission slipped" (1,512↑) — operator approved a long chained command after recognizing the lede; the chain ended with rm -rf $UNSET_VAR/* which expanded to rm -rf /* because the variable was empty. The classic xornullvoid wipeout was apt remove '*nvidia*595*' cascading into critical-package removal.

• CVSS 10.0 in agent harnesses. r/devops "AI coding tools are now a CVSS 10.0 supply-chain risk" (130↑) cites Cursor CVE-2026-26268 and Gemini CLI CVSS 10.0 — both featuring --yolo modes that ignore allowlists entirely and execute LLM-emitted commands without operator review.

• Network-exfil via curl-pipe-bash. Agents trained on installer documentation pattern-match curl https://x.com/install.sh | bash as legitimate. Once the agent is the one fetching the URL, the operator has no way to inspect the script before it runs.

• Production-database deletion via API mutation. HN: "An AI agent deleted our production database. The agent's confession is below" (859↑ / 1,030 comments, May 2026) — jeremyccrane documented an agent issuing curl -X POST .../graphql/v2 -d '{"query":"mutation { volumeDelete(volumeId: \"3d2c42fb-...\") }"}' against Railway with a token that had production-volume-delete privilege. No two-step confirmation, no environment scoping, no privilege boundary. Top community reply: "It's a privilege issue, not an execution issue." bash-vet catches the destructive curl-with-GraphQL-mutation pattern at command-approval time; pair with output-vetter's verify_action_outcome for the "agent's confession" half (the post-action misreport).

This MCP server runs the vetting inline before the command executes — no API key, no LLM-as-judge cost, sub-second:

> claude: vet this command before I run it: sudo apt remove '*nvidia*' && reboot
[MCP tool: vet_command_chain]

verdict: BLOCK
risk_score: 30
finding_count: 2
findings:
  [HIGH] PACKAGE.APT_REMOVE_GLOB
    snippet: sudo apt remove '*nvidia*'
    description: apt removing packages by glob pattern — likely cascades into
      critical-dependency removal
    recommendation: Use exact package names. xornullvoid's nvidia-driver
      wipeout was apt remove '*nvidia*595*'.

  [HIGH] SHUTDOWN.CHAINED_REBOOT
    snippet: && reboot
    description: Chained reboot/shutdown after another command — cuts off the
      operator's ability to react if anything went wrong (escalated MEDIUM→HIGH
      because chain mode)
    recommendation: Run shutdown/reboot as a separate command after manual
      review.

summary: BLOCK — 2 finding(s); worst is HIGH (PACKAGE.APT_REMOVE_GLOB):
apt removing packages by glob pattern — likely cascades into critical-dependency
removal

Why bash-vet-mcp

Three things existing MCP shell-execution servers don't do:

1. Defensive complement, not yet-another-shell-executor. MCPShell, mcp-shell, mcp-bash all give the agent a run_command tool. bash-vet-mcp is the opposite shape: vet before execute. Pair it with one of those servers (or with Claude Code's built-in Bash tool) — the agent calls vet_command before asking the operator to approve the run. If the verdict is BLOCK, the operator sees the destructive fragment surfaced before they pattern-match-approve.

2. Sub-second + local + free. Pure-Python: bashlex AST parse + regex pattern bank. No LLM-as-judge call, no API key, no per-call cost. Runs in CI, runs offline, runs at every agent turn without budget pressure.

3. 30 detection rules across 8 families, each with stable rule_id + severity + recommendation. Not "is this dangerous?" — exactly which rule fired, what severity, what the operator should do. This makes the response actionable at the agent loop level (block + retry with a different command) and at the human-review level (audit trail for compliance).

Built for the production AI operator who's already using Claude Code / Cursor / Cline / OpenClaw with shell access enabled, who's seen the failure mode at least once, and who wants the agent to vet its own emitted commands before asking for approval.

Tool surface

Resources:

• bash-vet://demo/clean — sample CLEAN verdict (ls -la /home/user/projects && cat README.md)

• bash-vet://demo/dangerous — sample BLOCK verdict (apt-glob + chained reboot + curl|bash)

• bash-vet://demo/sneaky — sample SNEAKY chain mimicking the r/LocalLLaMA failure mode

Prompts:

• vet-this-command(chain?) — diagnostic walkthrough; agent calls vet_command (or chain variant) on the most recent command + explains each finding

• audit-script — line-by-line vet of a multi-line shell script + per-line verdict + overall script verdict

Detection rules (30 across 8 families)

Each rule has a stable rule_id (e.g., DESTRUCTIVE.RM_RECURSIVE_ROOT) + severity + pattern-kind tag + plain-English description + concrete recommendation. Call list_detection_rules to retrieve the full catalog.

Quickstart

Install

pip install bash-vet-mcp

Configure for Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "bash-vet": {
      "command": "python",
      "args": ["-m", "bash_vet_mcp"]
    }
  }
}

Restart Claude Desktop. Test:

Resource bash-vet://demo/dangerous — read it back to me.

The demo resource returns a sample CommandVetReport so you can verify the protocol wiring without authoring inputs.

Use it inline

> me: cd /tmp/build && make clean && rm -rf $BUILD_DIR/cache && git reset --hard HEAD~3
> claude: let me vet this before running.
[MCP tool: vet_command_chain]
verdict: BLOCK
findings:
  [HIGH] DESTRUCTIVE.RM_RECURSIVE_VAR
    snippet: rm -rf $BUILD_DIR/cache
    description: rm -rf with environment variable — if the variable is empty
      or unset, this becomes `rm -rf /cache` (or `rm -rf /` depending on glob).
    recommendation: Replace with absolute path; or guard with [[ -n "$VAR" ]]
      check before the rm. (escalated MEDIUM→HIGH because chain mode.)
  [HIGH] GIT.RESET_HARD
    snippet: git reset --hard HEAD~3
    description: git reset --hard discards uncommitted local changes irreversibly.
    recommendation: Confirm there are no uncommitted changes you want to keep.
      (escalated MEDIUM→HIGH because chain mode.)

I'd recommend not running this as-is. Suggested rewrite:
  cd /tmp/build && make clean
  [[ -n "$BUILD_DIR" ]] && rm -rf "$BUILD_DIR/cache"
  # commit/stash any work, THEN: git reset --hard HEAD~3

Roadmap

Need this adapted to your stack?

If your AI deployment uses a different shell harness, custom allowlists, language other than bash (PowerShell / fish / nushell), or specific compliance / auditing requirements — that's a Custom MCP Build engagement.

To engage:

1. Email temur@pixelette.tech with subject Custom MCP Build inquiry — bash-vet

2. Include: 1-paragraph description of your stack + which tier

3. Reply within 2 business days with a 30-min discovery call slot

This server is part of a production-AI infrastructure MCP suite — companion to silentwatch-mcp (cron silent-failure detection), openclaw-health-mcp (deployment health), openclaw-cost-tracker-mcp (token-cost telemetry + 429 prediction), openclaw-skill-vetter-mcp (skill security vetting), openclaw-upgrade-orchestrator-mcp (upgrade safety), and openclaw-output-vetter-mcp (response grounding + swallowed-exception detection). Install all seven for full operational visibility.

How this fits in the agent-shell-execution ecosystem

Each layer is complementary. A command can pass shellcheck (no scripting bugs), pass bash-vet-mcp (no destructive patterns), and still need sandboxing if the agent's intent is unclear. We're aiming at the failure mode that's the most pattern-matchable and the most preventable: agent emits a chain with a destructive fragment buried in it, and the operator approves the chain because the lede looks fine.

Production AI audits

If you're running production AI and want an outside practitioner to score readiness, find the failure patterns already present (LLM-emitted shell commands being pattern P5.x in the catalog), and write the corrective-action plan:

Same email channel: temur@pixelette.tech with subject AI audit inquiry.

Contributing

PRs welcome. The detection rules are intentionally pluggable — every rule is a tuple in the _RULES list in src/bash_vet_mcp/scanner.py. Adding a new rule is one tuple + one test case. The pattern-matching engine handles regex compilation, deduplication, severity scoring, and chain-mode escalation automatically.

Bug reports + feature requests: open a GitHub issue.

License

MIT — see LICENSE.

Related

• Production-AI MCP Suite (Gumroad bundle) — this server plus 6 others in one curated 7-pack bundle

• silentwatch-mcp — cron silent-failure detection

• openclaw-health-mcp — deployment health

• openclaw-cost-tracker-mcp — token-cost telemetry + 429 prediction

• openclaw-skill-vetter-mcp — skill security vetting

• openclaw-upgrade-orchestrator-mcp — upgrade safety + provider-side regression detection

• openclaw-output-vetter-mcp — response grounding + swallowed-exception detection

• AI Production Discipline Framework — Notion template, $29 — the methodology these MCP tools implement

• SPEC.md — full server design

Built by Temur Khan — independent practitioner on production AI systems. Contact: temur@pixelette.tech