Skillget

shisad

agent
SUMMARY

Security-first AI agent daemon — the model proposes actions, the runtime decides what execute

README.md

ShisaD (shisad)

Security-first AI agent daemon framework.

ShisaD is a long-running daemon that sits between an LLM and external systems (tools, files, network, messaging channels). The model proposes actions; the runtime decides what actually executes — every tool call passes through policy enforcement, taint tracking, and audit before anything happens.

The core question at every action is: who asked for it? ShisaD is the user's agent — it exists to do what the user asks with the highest possible fidelity, and to prevent anything else (prompt injection, hallucination, attacker-controlled input) from taking control.

Rather than ignoring the elephant in the room, our design targets the lethal trifecta head-on: agents that access private data, process untrusted content, and take consequential actions are inherently high-risk. Most agent security research solves this by removing capabilities until the agent is safe but useless. ShisaD takes the opposite approach: keep the agent fully capable and build enforcement infrastructure that makes each capability safe to use at runtime. If a tool is insecure, the goal is to fix the enforcement, not disable the tool.

                       ┌─────────────────┐
                       │   LLM (Planner) │
   Untrusted ────────▶│   [tokens mix]  │─────────▶ Proposed
   Content             │                 │            Actions
                       └─────────────────┘
                                                       │
   ═══════════════════════════════════════════════════════════
   ║               ARCHITECTURAL BOUNDARY                    ║
   ═══════════════════════════════════════════════════════════
                                                       │
                                                       ▼
                        ┌─────────────────┐     ┌─────────────┐
                        │ Trusted Config  │     │  Security   │
                        │ (policies,      │     │  Analyzers  │
                        │  goals)         │     │ (metadata   │
                        └─────────────────┘     │  only)      │
                                                └──────┬──────┘
                                                       ▼
                                                APPROVE / REJECT

Features

  • Per-call policy enforcement — 8-layer PEP pipeline (registry, schema, capability, DLP, resource authorization, egress allowlisting, credential scoping, taint sink enforcement) runs on every tool call, not just at session start
  • Taint-aware content handling — ingress/egress content firewalls track provenance of untrusted input through the execution path
  • Confirmation gates, not blanket denial — user-requested actions proceed; ambiguous or tainted actions route to confirmation; only genuine anomalies trigger lockdown
  • Behavioral anomaly detection — control plane consensus (5 independent voters) for runtime anomaly detection, rate limiting, and lockdown escalation
  • Destructive command protection — enforced at the sandbox policy layer before execution, not by LLM judgment; structurally incapable of rm -rf / regardless of prompt injection or misconfiguration
  • Clean-room workflows — admin operations run in a taint-isolated session mode with no auto-apply
  • Multi-channel messaging — Matrix, Discord, Telegram, Slack (Socket Mode), with default-deny identity allowlisting per channel
  • Assistant primitives — notes/todos, scheduler with shared delivery, web search/fetch, filesystem/git helpers, evidence references for large untrusted output
  • Provider routing — pluggable LLM provider presets (Shisa, OpenAI, OpenRouter, Google, local vLLM) with per-route auth, model selection, and mixed-mode deployment
  • Observability — comprehensive audit trail, TUI dashboard (pending actions, tasks, channel health, alerts), and doctor diagnostics

Status

This repo is currently an initial development release (pre-alpha). This repo is being made public as of v0.5.

Version Focus
v0.5 First public release — evidence references, repo split, zero-config SHISA provider
v0.4 Self-modification, coding-agent runtime, COMMAND/TASK isolation
v0.3 Provider routing, channels, assistant tools, destructive command protection
v0.2 Structural refactor (typed handlers, decomposed runtime, coverage)
v0.1 Core daemon, PEP security pipeline, control API

See docs/ROADMAP.md for more details.

Getting Started

Users and agents looking to set up ShisaD on their own system should see docs/DEPLOY.md for the full bring-up guide — host bootstrap, provider configuration, channel setup (Discord, Telegram, Slack), and troubleshooting. ShisaD is designed to run on a dedicated instance or container, not inside your development environment.

Quick Start

git clone https://github.com/shisa-ai/shisad.git
cd shisad
uv sync --dev

Configuration

Environment variables use SHISAD_ prefixes. Full reference: docs/ENV-VARS.md.

Recommended: use the runner harness for local development. It handles env isolation, secret loading, and policy bootstrapping:

bash runner/harness.sh start       # background (requires tmux)
bash runner/harness.sh start --fg  # foreground
bash runner/harness.sh status

See runner/README.md for details. Secrets go in runner/.env (gitignored) or SHISAD_ENV_FILE.

Manual baseline

export SHISAD_DATA_DIR="$HOME/.local/share/shisad"
export SHISAD_SOCKET_PATH="/tmp/shisad/control.sock"
export SHISAD_POLICY_PATH="$PWD/.local/policy.yaml"
export SHISAD_LOG_LEVEL="INFO"

Provider routing

Default (Shisa.AI):

# Planner route remote-enables implicitly when SHISA key resolves.
export SHISA_API_KEY="<shisa-api-key>"

OpenAI:

export SHISAD_MODEL_REMOTE_ENABLED=true
export OPENAI_API_KEY="<openai-api-key>"
export SHISAD_MODEL_PLANNER_PROVIDER_PRESET="openai_default"
export SHISAD_MODEL_PLANNER_MODEL_ID="gpt-5.4-2026-03-05"
# Optional: export SHISAD_MODEL_PLANNER_REQUEST_PARAMETERS='{"max_completion_tokens":512}'

OpenRouter:

export SHISAD_MODEL_REMOTE_ENABLED=true
export OPENROUTER_API_KEY="<openrouter-api-key>"
export SHISAD_MODEL_PLANNER_PROVIDER_PRESET="openrouter_default"
export SHISAD_MODEL_PLANNER_MODEL_ID="qwen/qwen3.5-397b-a17b"
export SHISAD_MODEL_PLANNER_EXTRA_HEADERS='{"HTTP-Referer":"https://example.com","X-Title":"shisad"}'

Google (OpenAI-compatible):

export SHISAD_MODEL_REMOTE_ENABLED=true
export GEMINI_API_KEY="<gemini-api-key>"
export SHISAD_MODEL_PLANNER_PROVIDER_PRESET="google_openai_default"
export SHISAD_MODEL_PLANNER_MODEL_ID="gemini-3.1-pro-preview"

Local vLLM:

export SHISAD_MODEL_PLANNER_PROVIDER_PRESET="vllm_local_default"
export SHISAD_MODEL_PLANNER_BASE_URL="http://127.0.0.1:8000/v1"
export SHISAD_MODEL_PLANNER_REMOTE_ENABLED=true
export SHISAD_MODEL_PLANNER_AUTH_MODE="none"

Mixed mode (planner remote, embeddings local, monitor remote):

export SHISAD_MODEL_REMOTE_ENABLED=true

export SHISAD_MODEL_PLANNER_PROVIDER_PRESET="openrouter_default"
export SHISAD_MODEL_PLANNER_MODEL_ID="qwen/qwen3.5-397b-a17b"
export SHISAD_MODEL_PLANNER_API_KEY="<planner-openrouter-key>"

export SHISAD_MODEL_EMBEDDINGS_PROVIDER_PRESET="vllm_local_default"
export SHISAD_MODEL_EMBEDDINGS_BASE_URL="http://127.0.0.1:8000/v1"
export SHISAD_MODEL_EMBEDDINGS_REMOTE_ENABLED=true
export SHISAD_MODEL_EMBEDDINGS_AUTH_MODE="none"
export SHISAD_MODEL_EMBEDDINGS_MODEL_ID="text-embedding-3-small"

export SHISAD_MODEL_MONITOR_PROVIDER_PRESET="openai_default"
export SHISAD_MODEL_MONITOR_API_KEY="<monitor-openai-key>"
export SHISAD_MODEL_MONITOR_MODEL_ID="gpt-5.4-2026-03-05"

Verify provider setup:

uv run shisad doctor check --component provider

Auth notes:

  • Use *_auth_mode=header when custom auth header names are required.
  • *_auth_header_name is not accepted for *_auth_mode=bearer|none.

Channels

export SHISAD_DISCORD_ENABLED=true
export SHISAD_DISCORD_BOT_TOKEN="<token>"

export SHISAD_TELEGRAM_ENABLED=true
export SHISAD_TELEGRAM_BOT_TOKEN="<token>"

export SHISAD_SLACK_ENABLED=true
export SHISAD_SLACK_BOT_TOKEN="<xoxb-token>"
export SHISAD_SLACK_APP_TOKEN="<xapp-token>"

# Default-deny allowlist: channel -> [external_user_id]
export SHISAD_CHANNEL_IDENTITY_ALLOWLIST='{"discord":["1234567890"],"telegram":["11111"],"slack":["U12345"]}'

Assistant surfaces

# web_fetch and web_search are enabled by default
# Configure a search backend to use web_search:
export SHISAD_WEB_SEARCH_BACKEND_URL="https://search.example.com"
export SHISAD_WEB_ALLOWED_DOMAINS='["search.example.com","docs.example.com"]'

export SHISAD_ASSISTANT_FS_ROOTS='["/tmp/shisad-workspace"]'

Usage

Start and verify

uv run shisad start --foreground

In another shell:

uv run shisad status
uv run shisad doctor check --component all
uv run shisad tui --plain

Sessions

uv run shisad session create --user alice --workspace demo
uv run shisad session list
uv run shisad session message <session-id> "summarize current priorities"

Notes and todos

uv run shisad note create --key ops/runbook --content "verify doctor before deploy"
uv run shisad note list
uv run shisad todo create --title "close rollout checklist" --status open
uv run shisad todo list

Web and filesystem

uv run shisad web search "shisad security architecture" --limit 5
uv run shisad web fetch https://example.com
uv run shisad fs read /tmp/shisad-workspace/notes.txt
uv run shisad fs write /tmp/shisad-workspace/out.txt --content "hello" --confirm
uv run shisad git status --repo /tmp/shisad-workspace

Admin clean-room

uv run shisad session mode <session-id> --mode admin_cleanroom
uv run shisad channel pairing-propose --limit 50

Security Model

shisad assumes prompt injection will succeed and builds enforcement outside the model. The LLM is a planner, not an executor — it proposes tool calls, but the runtime pipeline decides whether each call proceeds, requires confirmation, or gets blocked. No amount of prompt injection, jailbreaking, or misconfiguration can override the enforcement layers because they run in a separate trust domain from the model.

The problem: any agent with access to private data (files, email), exposure to untrusted content (web pages, API responses), and the ability to take consequential actions (send messages, write files) is exploitable. This is the lethal trifecta. shisad has all three by design — it's meant to be a useful assistant, not a sandboxed demo.

The approach: instead of removing capabilities until the agent is safe (at which point you've rebuilt ChatGPT with extra steps), shisad keeps all capabilities available and enforces safety per-call:

  • 8-layer PEP pipeline on every tool call: registry check, schema validation, capability check, DLP (secret pattern detection), resource authorization, egress allowlisting, credential host-scoping, taint sink enforcement
  • Taint tracking: content firewalls tag untrusted input on ingress and enforce provenance-aware restrictions on egress — the runtime knows who asked for each action (user vs. injected content vs. model hallucination)
  • Confirmation gates: user-requested actions proceed; actions with ambiguous or tainted provenance route to user confirmation with context; only genuine anomalies trigger lockdown
  • Behavioral anomaly detection: control plane consensus (5 independent voters) catches patterns that individual call-level checks miss
  • Destructive command protection: enforced at the sandbox layer before execution, not by LLM judgment — structurally incapable of rm -rf / regardless of what the model is tricked into proposing

Default posture: all tools available out of the box. Operators who need a restrictive posture deploy an explicit policy via SHISAD_POLICY_PATH.

Egress model: allowlists auto-approve known-good destinations. Explicit user requests proceed without confirmation. Destinations suggested only by untrusted content route through confirmation with warning. Unattributed/hallucinated drift is blocked.

See docs/SECURITY.md for the full security architecture and docs/DESIGN-PHILOSOPHY.md for the governing principles.

Architecture

shisad/
├── src/shisad/          # Core source
│   ├── daemon/          # Control API, handlers, runtime implementation
│   ├── security/        # PEP pipeline, content firewalls, taint tracking
│   ├── executors/       # Tool execution, egress proxy
│   ├── channels/        # Matrix, Discord, Telegram, Slack
│   ├── assistant/       # Notes, todos, web, fs/git tools
│   ├── memory/          # Structured storage with semantic search
│   ├── scheduler/       # Task scheduling and delivery
│   ├── cli/             # Click-based CLI
│   ├── ui/              # TUI dashboard
│   ├── skills/          # Hot-reloadable skill plugins
│   └── governance/      # Anomaly voting, consensus
├── tests/
│   ├── unit/            # Component tests
│   ├── integration/     # Cross-component runtime flows
│   ├── behavioral/      # Product-correctness gate
│   └── adversarial/     # Prompt injection, exfil, evasion
├── runner/              # Dev harness (tmux, env isolation, policy bootstrap)
├── scripts/             # Validation, coverage, asset checks
├── docs/                # Design docs, ADRs, runbooks, analysis
└── examples/            # Example configs and skills

Key runtime paths:

  • Policy enforcement: src/shisad/security/pep.py
  • Egress proxy: src/shisad/executors/proxy.py
  • Handler implementation: src/shisad/daemon/handlers/_impl.py (composed from _impl_session.py, _impl_tool_execution.py, _impl_memory.py, etc.)

Development

uv run ruff check src/ tests/ scripts/
uv run mypy src/shisad/
uv run pytest -q

See AGENTS.md for full development process, validation matrix, and commit conventions.

Documentation

Doc Description
docs/DESIGN-PHILOSOPHY.md First-principles reference — read this first
docs/DEPLOY.md Public bring-up and deployment quickstart
docs/SECURITY.md Security architecture — threat model, enforcement layers, trust boundaries
docs/ROADMAP.md Public roadmap and milestone direction
docs/USE-CASES.md Prioritized use cases and capability mapping
docs/ENV-VARS.md Environment variable reference
docs/TOOL-STATUS.md Current tool surface snapshot
docs/adr/ Architectural decision records
docs/analysis/ Security case studies and supply chain analysis
docs/runbooks/ Operator runbooks (incident response, key rotation, rollback, skill revocation)
runner/RUNBOOK.md Dev harness operator runbook
  • agentic-security — literature survey on LLM agent security (78 papers, defense taxonomy, production readiness assessment)
  • agentic-memory — literature survey on agent memory architectures and poisoning defenses (29+ references, attack taxonomy, defense recommendations)

License

Apache License 2.0. See LICENSE.

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