---

Overview

Qise (pronounced "Cheese" 🧀) is an open-source runtime security framework that protects AI agents from both directions:

• World → Agent: Stops prompt injection, tool poisoning, memory/KB tampering, and supply chain attacks
• Agent → World: Stops dangerous commands, path traversal, SSRF, data exfiltration, and policy violations

Unlike rule-only solutions that are easily bypassed, Qise uses layered AI models (SLM fast-screen + LLM deep analysis) to understand attack intent, with deterministic rules as fast-path and fallback — never fail-open.

System Architecture

Agent (Claude Code / Codex / Gemini CLI / Custom)
    │
    │ API Request (OpenAI-compatible format)
    ▼
┌─────────────────────────────────────────────────────────┐
│                Tauri 2 Desktop App                       │
│  ┌───────────────────────────────────────────────────┐  │
│  │  System Tray │ Guard Dashboard │ Config Editor    │  │
│  │  Agent Panel │ Event Log (WS)  │ Stats Bar        │  │
│  └───────────────────────────────────────────────────┘  │
├─────────────────────────────────────────────────────────┤
│  Rust Proxy (axum, port 8822)                           │
│  • Request/Response interception                        │
│  • SSE streaming passthrough                            │
│  • Guard pipeline integration                           │
│  • Proxy Takeover (env + config file)                   │
├─────────────────────────────────────────────────────────┤
│  Python Bridge (aiohttp, port 8823)                     │
│  • Guard Pipeline execution                             │
│  • SLM/LLM inference (httpx)                            │
│  • WebSocket event push (/v1/bridge/events/stream)      │
│  • 7 HTTP endpoints + 1 WS endpoint                     │
├─────────────────────────────────────────────────────────┤
│  Guard Pipeline (14 Guards)                             │
│  Ingress: Prompt → ToolSanity → Context → SupplyChain  │
│  Egress:  Command → FS → Network → Exfil → Resource    │
│  Output:  Credential → Audit → Output                   │
│  Soft:    SecurityContextProvider + ReasoningGuard       │
├─────────────────────────────────────────────────────────┤
│  Model Layer                                            │
│  SLM: Ollama qwen3:4b (local, <2s)                     │
│  LLM: Cloud API (Claude/GPT/Qwen, <5s)                  │
│  Rules: Deterministic fallback (<1ms)                    │
└─────────────────────────────────────────────────────────┘
    │
    │ Forwarded Request
    ▼
  Upstream LLM API

---

Quick Start

1. Install Python Engine

# Clone and install
git clone https://github.com/morinop/qise.git
cd qise
pip install -e ".[dev]"

2. One-Command Setup

# Generate default config
qise init

# Check a tool call
qise check bash '{"command": "rm -rf /"}'
# → {"verdict": "block", "blocked_by": "command", ...}

qise check bash '{"command": "ls"}'
# → {"verdict": "pass", "blocked_by": null, "warnings": []}

# List all guards and their modes
qise guards

3. Setup Local SLM (Recommended)

Qise works out-of-the-box with rules only, but AI-first guards need an SLM. Local Ollama gives <2s latency:

# One-click: install Ollama + pull qwen3:4b (~2.4GB)
chmod +x scripts/setup_slm.sh
./scripts/setup_slm.sh

Or manually:

curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen3:4b
ollama serve

Then configure shield.yaml:

models:
  slm:
    base_url: "http://localhost:11434/v1"
    model: "qwen3:4b"
    timeout_ms: 5000

4. Zero-Code: Proxy Mode

Start a local HTTP proxy that intercepts all Agent↔LLM traffic:

# Start proxy server
qise proxy start --port 8822 --upstream https://api.openai.com

# Point your agent at the proxy
export OPENAI_API_BASE="http://localhost:8822/v1"

The proxy intercepts requests/responses in real-time, running all 14 guards on tool calls, injection attempts, and output leaks — with SSE streaming support for zero-latency text passthrough.

5. Desktop App (One-Click Security)

The Qise desktop app provides a one-click security toggle with real-time monitoring:

# Prerequisites: Node.js + Rust
cd src-tauri && cargo tauri dev

Features:

• System Tray: One-click protection toggle, status indicator, menu text auto-switches
• Guard Dashboard: Real-time guard events, mode switching (observe/enforce/off)
• Config Editor: Visual shield.yaml editing (SLM, LLM, Guards, Integration)
• Agent Panel: Detect installed agents, one-click proxy takeover with crash recovery
• Event Log: WebSocket real-time push, filter by All/Blocked/Warnings
• Stats Bar: Blocked/warning counts, SLM status (local/cloud/unavailable), latency

Proxy Takeover: Click "Take Over" to redirect your agent's API endpoint to the Qise proxy. Original config is backed up and auto-restored on exit or crash.

Agent	Method	Redirect Target
Generic OpenAI	OPENAI_API_BASE env var	http://localhost:8822/v1
Claude Code	ANTHROPIC_BASE_URL env var + ~/.claude/settings.json	http://localhost:8822/v1

6. Zero-Code: MCP Mode

Add to your agent's MCP configuration:

{
  "mcpServers": {
    "qise": {
      "command": "python",
      "args": ["-m", "qise.mcp_server"]
    }
  }
}

7. SDK Mode: Framework Adapters

Nanobot:

from qise import Shield
from qise.adapters.nanobot import QiseNanobotHook

shield = Shield.from_config()
hook = QiseNanobotHook(shield)
loop = AgentLoop(hooks=[hook])

LangGraph:

from qise import Shield
from qise.adapters.langgraph import QiseLangGraphWrapper

shield = Shield.from_config()
wrapper = QiseLangGraphWrapper(shield)
safe_tools = [wrapper.wrap_tool_call(tool) for tool in my_tools]

NexAU:

from qise import Shield
from qise.adapters.nexau import QiseNexauMiddleware

shield = Shield.from_config()
middleware = QiseNexauMiddleware(shield)
agent = NexAUAgent(middlewares=[middleware])

OpenAI Agents SDK:

from qise import Shield
from qise.adapters.openai_agents import QiseOpenAIAgentsGuardrails

shield = Shield.from_config()
guardrails = QiseOpenAIAgentsGuardrails(shield)
agent = Agent(guardrails=[guardrails.input_guardrail, guardrails.output_guardrail])

Hermes:

from qise import Shield
from qise.adapters.hermes import QiseHermesPlugin

shield = Shield.from_config()
plugin = QiseHermesPlugin(shield)
plugin.register(ctx)

---

Why Qise

Problem	Qise's Approach
Keyword rules easily bypassed	AI understands attack semantics, not just pattern matching
Single model bottleneck	Layered models: SLM <50ms fast-screen + LLM deep analysis
Fail-open on model errors	Rule fallback — never fail-open
No exfiltration detection	ExfilGuard: AI-first data exfiltration detection
No tool poisoning detection	ToolSanityGuard: hash baseline + AI semantic analysis
Static safety instructions	Dynamic SecurityContextProvider + Guard enforcement
Requires code changes	Proxy mode / MCP mode: zero-code integration
Cloud SLM latency 14-30s	Local Ollama qwen3:4b: <2s per call

Three-Layer Decision Flow

Every guard uses the same decision flow — rules first for speed, AI for semantics, rules last for safety:

  ┌──────────────────┐
  │ Rule Fast-Path   │  <1ms — deterministic BLOCK or PASS
  │ (regex, hash,    │  e.g., "rm -rf /" → BLOCK
  │  patterns)       │  For AI-first guards, only BLOCK short-circuits
  └────────┬─────────┘  (rule PASS flows to SLM for final say)
           │ uncertain or PASS (AI-first guards)
           ▼
  ┌──────────────────┐
  │ SLM Fast-Screen  │  <2s (local Ollama) — semantic classification
  │ (≤4B model)      │  e.g., obfuscated command → BLOCK
  └────────┬─────────┘  e.g., paraphrased injection → ESCALATE
           │ SLM can override low-confidence rule WARNs (<0.65)
           │ but not high-confidence ones (≥0.65)
           ▼
  ┌──────────────────┐
  │ LLM Deep Analysis│  <2s — full trajectory reasoning
  │ (8B-70B model)   │  e.g., multi-turn attack chain → BLOCK
  └────────┬─────────┘
           │ model unavailable
           ▼
  ┌──────────────────┐
  │ Rule Fallback    │  <1ms — conservative defaults
  │ (never fail-open)│  e.g., WARN on uncertain + network tool
  └──────────────────┘

Defense in Depth

  Layer 0: SecurityContextProvider  →  Agent follows voluntarily (~80%)
  Layer 1: ReasoningGuard          →  Chain-of-thought monitoring + threshold adjustment
  Layer 2: Guard Pipeline (14)     →  Rule → SLM → LLM → Rule fallback
  Layer 3: OutputGuard + CredentialGuard  →  Data leak detection

14 Guards at a Glance

Ingress Pipeline (World → Agent)

Guard	Strategy	Detects
PromptGuard	AI-first (80/20)	Indirect injection, multi-turn attacks, context poisoning
ToolSanityGuard	AI-first (80/20)	Tool description poisoning, rug pulls, name shadowing
ContextGuard	AI+hash (70/30)	Memory/KB poisoning, data tampering, hash integrity
SupplyChainGuard	AI+rules (60/40)	Malicious Skills, MCP tampering, source verification

Egress Pipeline (Agent → World)

Guard	Strategy	Detects
ReasoningGuard *	AI-only (100/0)	Manipulation traces in chain-of-thought, threshold adjustment
CommandGuard	Rules+AI (70/30)	Shell injection, dangerous commands, privilege escalation
FilesystemGuard	Rules (90/10)	Path traversal, workspace violations, system dir access
NetworkGuard	Rules (90/10)	SSRF, forbidden domains, internal network scanning
ExfilGuard	AI-first (80/20)	Data exfiltration, covert channels, DNS exfil
ResourceGuard	Rules+AI (60/40)	Infinite loops, budget overruns, circuit breaker
ToolPolicyGuard	Rules (100/0)	Unauthorized tool access, deny/approval/owner-only

Output Pipeline (Audit)

Guard	Strategy	Detects
CredentialGuard	Rules (100/0)	API keys, secrets, tokens in output
AuditGuard	AI+rules (50/50)	Attack chain reconstruction, session risk scoring
OutputGuard	AI+rules (70/30)	PII exposure, KB content leaks, credential leaks

* ReasoningGuard is cross-cutting — detects manipulation in chain-of-thought and adjusts thresholds of other guards.

5 Framework Adapters

Framework	Ingress	Egress	Output	SecContext
Nanobot	✅	✅	✅	✅
Hermes	✅	✅	✅	—
NexAU	✅	✅	✅	✅
LangGraph	—	✅	—	✅
OpenAI Agents	✅	✅	✅	—

All adapters use IngressCheckMixin + EgressCheckMixin — no monkey-patching, only official Hook/Plugin/Middleware APIs.

Configuration (shield.yaml)

version: "1.0"

integration:
  mode: proxy          # proxy | mcp | sdk
  proxy:
    port: 8822
    auto_takeover: true
    crash_recovery: true

models:
  slm:
    base_url: "http://localhost:11434/v1"   # Ollama
    model: "qwen3:4b"
    timeout_ms: 5000
  llm:
    base_url: "https://api.anthropic.com"
    model: "claude-sonnet-4-5"
    timeout_ms: 5000

guards:
  enabled: [prompt, command, credential, reasoning, filesystem, network,
            exfil, resource, audit, tool_sanity, context, output, tool_policy, supply_chain]
  config:
    prompt:
      mode: observe           # observe | enforce | off
      slm_confidence_threshold: 0.7
      skip_slm_on_rule_pass: false
    command:
      mode: enforce
    exfil:
      mode: observe
      skip_slm_on_rule_pass: true
      slm_override_rule_warn_threshold: 0.8

Environment variable overrides: QISE_SLM_BASE_URL, QISE_SLM_MODEL, QISE_SLM_API_KEY, QISE_LLM_BASE_URL, QISE_PROXY_PORT, QISE_MODE.

Performance

Operation	Target	Measured (p95)
Rule fast-path (single guard)	<1ms	~0.02ms
Full egress pipeline (6 guards)	<10ms	~0.02ms
Local SLM (Ollama qwen3:4b)	<2s	~500ms-2s (M1+)
Full pipeline + local SLM	<3s	~1-2s

Scripts

Script	Description
scripts/setup_slm.sh	Install Ollama + pull qwen3:4b (one-click local SLM)
scripts/benchmark_slm.py	Compare local Ollama vs cloud SLM latency
scripts/e2e_tauri_test.py	End-to-end verification: Proxy → Bridge → Guard → API

Development

pip install -e ".[dev]"     # Install with dev dependencies
pytest tests/ -v             # Run 461+ tests
ruff check .                 # Lint
ruff format .                # Format
mypy src/qise               # Type check
cd src-tauri && cargo tauri dev    # Run desktop app
cd src-tauri && cargo tauri build  # Build desktop app

Evaluation Results

R12-tuned evaluation (SLM + rules pipeline vs rules-only baseline):

Metric	Rules-Only	SLM + Rules	Delta
Precision	0.643	1.000	+0.357
Recall	0.973	1.000	+0.027
F1	0.774	1.000	+0.226
FPR	0.400	0.000	+0.400 (lower=better)

Architecture

qise/
├── src/qise/
│   ├── core/              # GuardContext, AIGuardBase, Pipeline, Shield, Config
│   ├── guards/            # 14 Guard implementations
│   ├── models/            # ModelRouter (httpx-based OpenAI-compatible client)
│   ├── data/              # ThreatPatternLoader + BaselineManager + SQLite
│   ├── providers/         # SecurityContextProvider (DSL template rendering)
│   ├── adapters/          # 5 Framework adapters
│   ├── proxy/             # HTTP proxy server (aiohttp + SSE)
│   ├── bridge/            # Python Bridge (7 HTTP + 1 WS endpoint)
│   └── mcp_server.py      # MCP Server (4 security check tools)
├── src-tauri/             # Desktop App (Tauri 2 + React)
│   ├── src/               #   Rust backend (proxy, bridge, takeover, 12 IPC commands)
│   └── icons/             #   App icons
├── src-ui/                # React frontend (Raycast-style dark UI)
│   └── src/components/    #   StatusIndicator, ProxyToggle, GuardList, EventLog,
│                          #   AgentPanel, ConfigPanel
├── scripts/               # setup_slm.sh, benchmark_slm.py, e2e_tauri_test.py
├── data/                  # threat_patterns/, security_contexts/, prompts/
├── tests/                 # 461+ tests
├── eval/                  # Evaluation datasets and results
└── docs/                  # Architecture, Guards, Threat Model, Integration

CLI Reference

qise check bash '{"command": "rm -rf /"}'  # Single security check
qise serve                                  # Start MCP Server
qise proxy start --port 8822                # Start Rust HTTP proxy
qise bridge start --port 8823               # Start Python Bridge
qise init                                   # Generate shield.yaml
qise guards                                 # List registered guards
qise adapters                               # List framework adapters
qise context bash                           # Get security context
qise version                                # Print version

Integration Modes

Mode	Code Required	Defense Depth	Best For
Proxy Mode	0 lines	Full (4 layers)	Desktop users, non-developers
MCP Mode	0 lines	Hard defense (14 guards)	MCP ecosystem users
SDK Mode	1-5 lines	Full (4 layers) + lowest latency	Agent developers

Status

Component	Status
Core engine (AIGuardBase, Pipeline, Shield)	✅ Complete
14 Guards (Ingress + Egress + Output)	✅ Complete
ModelRouter (httpx-based SLM/LLM client)	✅ Complete
Ollama SLM integration (qwen3:4b, <2s)	✅ Complete
Rust Proxy (axum + hyper + rustls, SSE streaming)	✅ Complete
Python Bridge (aiohttp, 7 HTTP + 1 WS endpoint)	✅ Complete
Desktop App (Tauri 2, System Tray, Config Editor)	✅ Complete
Proxy Takeover (env + config file + crash recovery)	✅ Complete
Real-time Frontend (GuardList, EventLog, AgentPanel, ConfigPanel)	✅ Complete
5 Framework Adapters (Nanobot, Hermes, NexAU, LangGraph, OpenAI Agents)	✅ Complete
MCP Server (4 security check tools)	✅ Complete
SecurityContextProvider (DSL template rendering)	✅ Complete
BaselineManager (SHA-256 hash + SQLite)	✅ Complete
461+ unit + integration + performance tests	✅ Complete
OpenAI Agents SDK E2E Test (10/10 passed)	✅ Complete

License

CC BY-NC-SA 4.0 — Free for personal, academic, and non-commercial use. Commercial use requires separate permission.