humanbound-cli 1.0.1

Humanbound CLI - command line interface for AI agent security testing.

Humanbound CLI

AI agent security testing — adversarial attacks, posture scoring, guardrails export, and firewall training. Runs locally or on the platform. No login required.

pip install humanbound-cli

---

Quick Start

# Configure your LLM provider
export HB_PROVIDER=openai
export HB_API_KEY=sk-...

# Run a security test
hb test --endpoint ./bot-config.json --repo . --wait

# View results
hb posture                         # Security score (0-100, A-F)
hb logs                            # Conversation logs
hb report -o report.html           # HTML report
hb guardrails -o rules.yaml        # Firewall rules

Full isolation with ollama — zero external API calls:

export HB_PROVIDER=ollama
export HB_MODEL=llama3.1:8b
hb test --endpoint ./bot-config.json --scope ./scope.yaml --wait

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What It Does

Humanbound runs multi-turn adversarial attacks against your AI agent's live endpoint, evaluates responses using LLM-as-a-Judge, and produces structured findings aligned with OWASP Top 10 for LLM Applications and OWASP Agentic AI Threats.

Feature	Local	Platform
Multi-turn adversarial testing (OWASP)	Yes	Yes
Behavioral/QA testing	Yes	Yes
Posture score (0-100, A-F)	Yes	Yes + trends
HTML/JSON reports	Yes	Yes
Guardrails export	Yes	Yes (richer)
Firewall training	Yes	Yes (richer)
Finding lifecycle tracking	—	Yes
Continuous monitoring	—	Yes
Cross-session leakage detection	—	Yes
Managed LLM (no key needed)	—	Yes

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Agent Configuration

Create a JSON file describing how to talk to your agent:

{
  "streaming": false,
  "thread_auth": {"endpoint": "", "headers": {}, "payload": {}},
  "thread_init": {
    "endpoint": "https://your-bot.com/sessions",
    "headers": {"Authorization": "Bearer token"},
    "payload": {}
  },
  "chat_completion": {
    "endpoint": "https://your-bot.com/chat",
    "headers": {"Authorization": "Bearer token"},
    "payload": {"message": "$PROMPT"}
  }
}

$PROMPT is where Humanbound injects test prompts.

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Test Modes

# Default: threaded, progress spinner (~20 min)
hb test --endpoint ./config.json --wait

# Verbose: live progress bar + final results table
hb test --endpoint ./config.json --wait --verbose

# Debug: single-threaded, full turn-by-turn output
hb test --endpoint ./config.json --wait --debug

Test Categories

Category	Flag	Description
OWASP Agentic	-t owasp_agentic (default)	Multi-turn adversarial with score-guided escalation
OWASP Single-Turn	-t owasp_single_turn	Maximum-strength single prompts
Behavioral QA	--qa	Intent boundary + response quality testing

Testing Levels

Level	Flag	Duration
Unit (default)	-l unit	~20 min
System	--deep	~45 min
Acceptance	--full	~90 min

Scope Discovery

hb test --endpoint ./config.json --repo . --wait            # Scan code for scope + tools (recommended)
hb test --endpoint ./config.json --scope ./scope.yaml --wait # Explicit scope file
hb test --endpoint ./config.json --prompt ./system.txt --wait # Extract from system prompt
hb test --endpoint ./config.json --wait                      # Auto-probe the bot

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Defence

Guardrails Export

hb guardrails -o rules.yaml
hb guardrails --vendor openai -o openai_rules.json

Firewall Training

Train a Tier 2 classifier from test results:

hb firewall train                                  # From local test data
hb firewall train --import pyrit_results.json      # From PyRIT
hb firewall train --import results.json:promptfoo  # From promptfoo

Use with hb-firewall for runtime protection.

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CI/CD

# .github/workflows/security.yml
- run: pip install humanbound-cli
- run: hb test --endpoint ./config.json --repo . --wait --fail-on high
  env:
    HB_PROVIDER: openai
    HB_API_KEY: ${{ secrets.OPENAI_KEY }}

---

Platform (With Login)

For posture tracking, finding lifecycle, continuous monitoring, and team collaboration:

hb login
hb connect --endpoint ./bot-config.json    # Create project + first test
hb test --wait                              # Re-test (project remembered)
hb posture --history                        # Posture trends
hb findings                                 # Finding lifecycle
hb monitor enable --schedule daily          # Continuous monitoring

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Providers

Provider	HB_PROVIDER	Notes
OpenAI	openai	GPT-4o, GPT-4.1
Anthropic	claude	Claude 3.5, Claude 4
Google	gemini	Gemini Pro
Azure OpenAI	azureopenai	Requires HB_ENDPOINT
Grok (xAI)	grok
Ollama	ollama	Full local isolation

hb config set provider openai
hb config set api-key sk-...

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pytest Integration

import pytest

@pytest.mark.hb
def test_prompt_injection(hb):
    result = hb.test("llm001")
    assert result.passed

@pytest.mark.hb
def test_posture_threshold(hb_posture):
    assert hb_posture["score"] >= 70

pytest --hb tests/ --hb-fail-on=high

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MCP Server

Expose CLI capabilities as tools for AI assistants:

pip install humanbound-cli[mcp]

# Claude Code
claude mcp add humanbound -- hb mcp

# Cursor (.cursor/mcp.json)
{"mcpServers": {"humanbound": {"command": "hb", "args": ["mcp"]}}}

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Links

• Documentation
• Firewall (hb-firewall)
• PyPI