hound

Language-agnostic AI auditor that autonomously builds and refines adaptive knowledge graphs for deep, iterative code reasoning

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Hound is a security audit automation pipeline for AI-assisted code review that mirrors how expert auditors think, learn, and collaborate. It features graph-driven analysis, sessionized audits, provider-agnostic models, belief system and hypotheses, precise code grounding, and adaptive planning. The system employs a senior/junior auditor pattern where the Scout actively navigates the codebase and annotates knowledge graphs while the Strategist handles high-level planning and vulnerability analysis. Hound is optimized for small-to-medium sized projects like smart contract applications and is language-agnostic.

README:

Hound

Autonomous agents for code security auditing

_{Overview
• Configuration
• Workflow
• Chatbot
• Contributing}

Overview

Hound is a Language-agnostic AI auditor that autonomously builds and refines adaptive knowledge graphs for deep, iterative code reasoning.

Key Features

Graph-driven analysis – Flexible, agent-designed graphs that can model any aspect of a system (e.g. architecture, access control, value flows, math, etc.)
Relational graph views – High-level graphs support cross-aspect reasoning and precise retrieval of the code snippets that back each subsystem investigated.
Belief & hypothesis system – Observations, assumptions, and hypotheses evolve with confidence scores, enabling long-horizon reasoning and cumulative audits.
Dynamic model switching – Lightweight "scout" models handle exploration; heavyweight "strategist" models provide deep reasoning, mirroring expert workflows while keeping costs efficient.
Strategic audit planning - Balances broad code coverage with focused investigation of the most promising aspects, ensuring both depth and efficiency.

Codebase size considerations: While Hound can analyze any codebase, it's optimized for small-to-medium sized projects like typical smart contract applications. Large enterprise codebases may exceed context limits and require selective analysis of specific subsystems.

Installation

pip install -r requirements.txt

Configuration

Set up your OpenAI API key and optional base URL:

export OPENAI_API_KEY=your_key_here
# Optional: override the base URL (defaults to https://api.openai.com)
export OPENAI_BASE_URL=https://api.openai.com

Copy the example configuration and edit as needed:

cp hound/config.yaml.example hound/config.yaml
# then edit hound/config.yaml to select providers/models and options

Notes:

Defaults work out-of-the-box; you can override many options via CLI flags.
Keep API keys out of the repo; API_KEYS.txt is gitignored and can be sourced.

Note: Audit quality scales with time and model capability. Use longer runs and advanced models for more complete results.

Complete Audit Workflow

Step 1: Create a Project

Projects organize your audits and store all analysis data:

# Create a project from local code
./hound.py project create myaudit /path/to/code

# List all projects
./hound.py project ls

# View project details and coverage
./hound.py project info myaudit

Step 2: Build Knowledge Graphs

Hound analyzes your codebase and builds aspect‑oriented knowledge graphs that serve as the foundation for all subsequent analysis.

Recommended (one‑liner):

# Auto-generate a default set of graphs (up to 5) and refine
# Strongly recommended: pass a whitelist of files (comma-separated)
./hound.py graph build myaudit --auto \
  --files "src/A.sol,src/B.sol,src/utils/Lib.sol"

# View generated graphs
./hound.py graph ls myaudit

Alternative (manual guidance):

# 1) Initialize the baseline SystemArchitecture graph
./hound.py graph build myaudit --init \
  --files "src/A.sol,src/B.sol,src/utils/Lib.sol"

# 2) Add a specific graph with your own description (exactly one graph)
./hound.py graph custom myaudit \
  "Call graph focusing on function call relationships across modules" \
  --iterations 2 \
  --files "src/A.sol,src/B.sol,src/utils/Lib.sol"

# (Repeat 'graph custom' for additional targeted graphs as needed)

Operational notes:

--auto always includes the SystemArchitecture graph as the first graph. You do not need to run --init in addition to --auto.
If --init is used and a SystemArchitecture graph already exists, initialization is skipped. Use --auto to add more graphs, or remove existing graphs first if you want a clean re‑init.
When running --auto and graphs already exist, Hound asks for confirmation before updating/overwriting graphs (including SystemArchitecture). To clear graphs:

./hound.py graph rm myaudit --all                 # remove all graphs
./hound.py graph rm myaudit --name SystemArchitecture  # remove one graph

For large repos, you can constrain scope with --files (comma‑separated whitelist) alongside either approach.

Whitelists (strongly recommended):

Always pass a whitelist of input files via --files. For the best results, the selected files should fit in the model’s available context window; whitelisting keeps the graph builder focused and avoids token overflows.
If you do not pass --files, Hound will consider all files in the repository. On large codebases this triggers sampling and may degrade coverage/quality.
--files expects a comma‑separated list of paths relative to the repo root.

Examples:

# Manual (small projects)
./hound.py graph build myaudit --auto \
  --files "src/A.sol,src/B.sol,src/utils/Lib.sol"

# Generate a whitelist automatically (recommended for larger projects)
python whitelist_builder.py \
  --input /path/to/repo \
  --limit-loc 20000 \
  --output whitelists/myaudit

# Use the generated list (newline-separated) as a comma list for --files
./hound.py graph build myaudit --auto \
  --files "$(tr '\n' ',' < whitelists/myaudit | sed 's/,$//')"

Refine existing graphs (resume building):

You can resume/refine an existing graph without creating new ones using graph refine. This skips discovery and saves updates incrementally.

# Refine a single graph by name (internal or display)
./hound.py graph refine myaudit SystemArchitecture \
  --iterations 2 \
  --files "src/A.sol,src/B.sol,src/utils/Lib.sol"

# Refine all existing graphs
./hound.py graph refine myaudit --all --iterations 2 \
  --files "src/A.sol,src/B.sol,src/utils/Lib.sol"

Step 3: Run the Audit

The audit phase uses the senior/junior pattern with planning and investigation:

# 1. Sweep all components for shallow bugs, build code understanding
./hound.py agent audit myaudit --mode sweep

# 2. Intuition-guided search to find complex bugs
./hound.py agent audit myaudit --mode intuition --time-limit 300

# Start with telemetry (connect the Chatbot UI to steer)
./hound.py agent audit myaudit --mode intuition --time-limit 30 --telemetry 

# Attach to an existing session and continue where you left off
./hound.py agent audit myaudit --mode intuition --session <session_id>

Tip: When started with --telemetry, you can connect the Chatbot UI and steer the audit interactively (see Chatbot section above).

Audit Modes:

Hound supports two distinct audit modes:

Sweep Mode (--mode sweep): Phase 1 - Systematic component analysis
- Performs a broad, systematic analysis of every major component
- Examines each contract, module, and class for vulnerabilities
- Builds comprehensive graph annotations for later analysis
- Terminates when all accessible components have been analyzed
- Best for: Initial vulnerability discovery and building code understanding
Intuition Mode (--mode intuition): Phase 2 - Deep, targeted exploration
- Uses intuition-guided search to find high-impact vulnerabilities
- Prioritizes monetary flows, value transfers, and theft opportunities
- Investigates contradictions between assumptions and observations
- Focuses on authentication bypasses and state corruption
- Best for: Finding complex, cross-component vulnerabilities

Key parameters:

--time-limit: Stop after N minutes (useful for incremental audits)
--plan-n: Number of investigations per planning batch
--session: Resume a specific session (continues coverage/planning)
--debug: Save all LLM interactions to .hound_debug/

Normally, you want to run sweep mode first followed by intuition mode. The quality and duration depend heavily on the models used. Faster models provide quick results but may miss subtle issues, while advanced reasoning models find deeper vulnerabilities but require more time.

Step 4: Monitor Progress

Check audit progress and findings at any time during the audit. If you started the agent with --telemetry, you can also monitor and steer via the Chatbot UI:

Open http://127.0.0.1:5280 and attach to the running instance
Watch live Activity, Plan, and Findings
Use the Steer form to guide the next investigations

# View current hypotheses (findings)
./hound.py project ls-hypotheses myaudit

# See detailed hypothesis information
./hound.py project hypotheses myaudit --details

# List hypotheses with confidence ratings
./hound.py project hypotheses myaudit

# Check coverage statistics
./hound.py project coverage myaudit

# View session details
./hound.py project sessions myaudit --list

Understanding hypotheses: Each hypothesis represents a potential vulnerability with:

Confidence score: 0.0-1.0 indicating likelihood of being a real issue
Status: proposed (initial), investigating, confirmed, rejected
Severity: critical, high, medium, low
Type: reentrancy, access control, logic error, etc.
Annotations: Exact code locations and evidence

Step 5: Run Targeted Investigations (Optional)

For specific concerns, run focused investigations without full planning:

# Investigate a specific concern
./hound.py agent investigate "Check for reentrancy in withdraw function" myaudit

# Quick investigation with fewer iterations
./hound.py agent investigate "Analyze access control in admin functions" myaudit \
  --iterations 5

# Use specific models for investigation
./hound.py agent investigate "Review emergency functions" myaudit \
  --model gpt-4o \
  --strategist-model gpt-5

When to use targeted investigations:

Following up on specific concerns after initial audit
Testing a hypothesis about a particular vulnerability
Quick checks before full audit
Investigating areas not covered by automatic planning

Note: These investigations still update the hypothesis store and coverage tracking.

Step 6: Quality Assurance

A reasoning model reviews all hypotheses and updates their status based on evidence:

# Run finalization with quality review
./hound.py finalize myaudit
# Re-run all pending (including below threshold)
./hound.py finalize myaudit --include-below-threshold

# Customize confidence threshold
./hound.py finalize myaudit -t 0.7 --model gpt-4o

# Include all findings (not just confirmed)
# (Use on the report command, not finalize)
./hound.py report myaudit --include-all

What happens during finalization:

A reasoning model (default: GPT-5) reviews each hypothesis
Evaluates the evidence and code context
Updates status to confirmed or rejected based on analysis
Adjusts confidence scores based on evidence strength
Prepares findings for report generation

Important: By default, only confirmed findings appear in the final report. Use --include-all to include all hypotheses regardless of status.

Step 7: Generate Proof-of-Concepts

Create and manage proof-of-concept exploits for confirmed vulnerabilities:

# Generate PoC prompts for confirmed vulnerabilities
./hound.py poc make-prompt myaudit

# Generate for a specific hypothesis
./hound.py poc make-prompt myaudit --hypothesis hyp_12345

# Import existing PoC files
./hound.py poc import myaudit hyp_12345 exploit.sol test.js \
  --description "Demonstrates reentrancy exploit"

# List all imported PoCs
./hound.py poc list myaudit

The PoC workflow:

make-prompt: Generates detailed prompts for coding agents (like Claude Code)
- Includes vulnerable file paths (project-relative)
- Specifies exact functions to target
- Provides clear exploit requirements
- Saves prompts to poc_prompts/ directory
import: Links PoC files to specific vulnerabilities
- Files stored in poc/[hypothesis-id]/
- Metadata tracks descriptions and timestamps
- Multiple files per vulnerability supported
Automatic inclusion: Imported PoCs appear in reports with syntax highlighting

Step 8: Generate Professional Reports

Produce comprehensive audit reports with all findings and PoCs:

# Generate HTML report (includes imported PoCs)
./hound.py report myaudit

# Include all hypotheses, not just confirmed
./hound.py report myaudit --include-all

# Export report to specific location
./hound.py report myaudit --output /path/to/report.html

Report contents:

Executive summary: High-level overview and risk assessment
System architecture: Understanding of the codebase structure
Findings: Detailed vulnerability descriptions (only confirmed by default)
Code snippets: Relevant vulnerable code with line numbers
Proof-of-concepts: Any imported PoCs with syntax highlighting
Severity distribution: Visual breakdown of finding severities
Recommendations: Suggested fixes and improvements

Note: The report uses a professional dark theme and includes all imported PoCs automatically.

Session Management

Each audit run operates under a session with comprehensive tracking and per-session planning:

Planning is stored in a per-session PlanStore with statuses: planned, in_progress, done, dropped, superseded.
Existing planned items are executed first; Strategist only tops up new items to reach your --plan-n.
On resume, any stale in_progress items are reset to planned; completed items remain done and are not duplicated.
Completed investigations, coverage, and hypotheses are fed back into planning to avoid repeats and guide prioritization.

# View session details
./hound.py project sessions myaudit <session_id>

# List and inspect sessions
./hound.py project sessions myaudit --list
./hound.py project sessions myaudit <session_id>

# Show planned investigations for a session (Strategist PlanStore)
./hound.py project plan myaudit <session_id>

# Session data includes:
# - Coverage statistics (nodes/cards visited)
# - Investigation history
# - Token usage by model
# - Planning decisions
# - Hypothesis formation

Sessions are stored in ~/.hound/projects/myaudit/sessions/ and contain:

session_id: Unique identifier
coverage: Visited nodes and analyzed code
investigations: All executed investigations
planning_history: Strategic decisions made
token_usage: Detailed API usage metrics

Resume/attach to an existing session during an audit run by passing the session ID:

# Attach to a specific session and continue auditing under it
./hound.py agent audit myaudit --session <session_id>

When you attach to a session, its status is set to active while the audit runs and finalized on completion (completed or interrupted if a time limit was hit). Any in_progress plan items are reset to planned so you can continue cleanly.

Simple Planning Examples

# Start an audit (creates a session automatically)
./hound.py agent audit myaudit

# List sessions to get the session id
./hound.py project sessions myaudit --list

# Show planned investigations for that session
./hound.py project plan myaudit <session_id>

# Attach later and continue planning/execution under the same session
./hound.py agent audit myaudit --session <session_id>

Chatbot (Telemetry UI)

Hound ships with a lightweight web UI for steering and monitoring a running audit session. It discovers local runs via a simple telemetry registry and streams status/decisions live.

Prerequisites:

Set API keys (at least OPENAI_API_KEY, optional OPENAI_BASE_URL for custom endpoints): source ../API_KEYS.txt or export manually
Install Python deps in this submodule: pip install -r requirements.txt

Start the agent with telemetry enabled

# From the hound/ directory
./hound.py agent audit myaudit --telemetry --debug

# Notes
# - The --telemetry flag exposes a local SSE/control endpoint and registers the run
# - Optional: ensure the registry dir matches the chatbot by setting:
#   export HOUND_REGISTRY_DIR="$HOME/.local/state/hound/instances"

Launch the chatbot server

# From the hound/ directory
python chatbot/run.py

# Optional: customize host/port
HOST=0.0.0.0 PORT=5280 python chatbot/run.py

Open the UI: http://127.0.0.1:5280

Select the running instance and stream activity

The input next to “Start” lists detected instances as project_path | instance_id.
Click “Start” to attach; the UI auto‑connects the realtime channel and begins streaming decisions/results.
The lower panel has tabs:
- Activity: live status/decisions
- Plan: current strategist plan (✓ done, ▶ active, • pending)
- Findings: hypotheses with confidence; you can Confirm/Reject manually

Steer the audit

Use the “Steer” form (e.g., “Investigate reentrancy across the whole app next”).
Steering is queued at <project>/.hound/steering.jsonl and consumed exactly once when applied.
Broad, global instructions may preempt the current investigation and trigger immediate replanning.

Troubleshooting

No instances in dropdown: ensure you started the agent with --telemetry.
Wrong or stale project shown: clear the input; the UI defaults to the most recent alive instance.
Registry mismatch: confirm both processes print the same Using registry dir: or set HOUND_REGISTRY_DIR for both.
Raw API: open /api/instances in the browser to inspect entries (includes alive flag and registry path).

Managing Hypotheses

Hypotheses are the core findings that accumulate across sessions:

# List hypotheses with confidence scores
./hound.py project hypotheses myaudit

# View with full details
./hound.py project hypotheses myaudit --details

# Update hypothesis status
./hound.py project set-hypothesis-status myaudit hyp_12345 confirmed

# Reset hypotheses (creates backup)
./hound.py project reset-hypotheses myaudit

# Force reset without confirmation
./hound.py project reset-hypotheses myaudit --force

Hypothesis statuses:

proposed: Initial finding, needs review
investigating: Under active investigation
confirmed: Verified vulnerability
rejected: False positive
resolved: Fixed in code

Advanced Features

Model Selection

Override default models per component:

# Use different models for each role
./hound.py agent audit myaudit \
  --platform openai --model gpt-4o-mini \           # Scout
  --strategist-platform anthropic --strategist-model claude-3-opus   # Strategist

Debug Mode

Capture all LLM interactions for analysis:

# Enable debug logging
./hound.py agent audit myaudit --debug

# Debug logs saved to .hound_debug/
# Includes HTML reports with all prompts and responses

Coverage Tracking

Monitor audit progress and completeness:

# View coverage statistics
./hound.py project coverage myaudit

# Coverage shows:
# - Graph nodes visited vs total
# - Code cards analyzed vs total
# - Percentage completion

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

Apache 2.0 with additional terms:

You may use Hound however you want, except selling it as an online service or as an appliance - that requires written permission from the author.

See LICENSE for details.

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Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.2k

hound

README:

Hound

Overview

Key Features

Links

Installation

Configuration

Complete Audit Workflow

Step 1: Create a Project

Step 2: Build Knowledge Graphs

Step 3: Run the Audit

Step 4: Monitor Progress

Step 5: Run Targeted Investigations (Optional)

Step 6: Quality Assurance

Step 7: Generate Proof-of-Concepts

Step 8: Generate Professional Reports

Session Management

Simple Planning Examples

Chatbot (Telemetry UI)

Managing Hypotheses

Advanced Features

Model Selection

Debug Mode

Coverage Tracking

Contributing

License

For Tasks:

For Jobs:

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