Riccardo Schirone 761dc6c13f Add compose.prebuilt.yaml overlay for prebuilt GHCR images (#548)
Adds an overlay for dev/docker-compose that resets the `build:` block
of every locally-built component and points it at the matching prebuilt
image from GHCR, so developers can run the stack without local builds.
Image tag defaults to "main" and is overridable via BUTTERCUP_IMAGE_TAG.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* ci: allow Docker Compose !reset/!override tags in YAML check

The static-checks YAML step uses yaml.safe_load_all, which rejects the
Compose-specific !reset/!override merge tags used by
compose.prebuilt.yaml. Register no-op constructors for those tags so
compose overlays validate without masking real YAML errors elsewhere.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-18 16:15:53 +02:00
2025-08-16 00:33:20 -04:00
2026-01-27 09:35:30 -05:00
2025-01-17 14:57:33 +01:00
2025-11-25 18:07:29 +01:00
2025-01-13 13:48:03 +01:00

Buttercup Cyber Reasoning System (CRS)

Tests Component Integration Tests System Integration

Buttercup is a Cyber Reasoning System (CRS) developed by Trail of Bits for the DARPA AIxCC (AI Cyber Challenge). Buttercup finds and patches software vulnerabilities in open-source code repositories like example-libpng. It starts by running an AI/ML-assisted fuzzing campaign (built on oss-fuzz) for the program. When vulnerabilities are found, Buttercup analyzes them and uses a multi-agent AI-driven patcher to repair the vulnerability. Buttercup system consists of several components:

  • Orchestrator: Coordinates the overall task process and manages the workflow
  • Seed Generator: Creates inputs for vulnerability discovery
  • Fuzzer: Discovers vulnerabilities through intelligent fuzzing techniques
  • Program Model: Analyzes code structure and semantics for better understanding
  • Patcher: Generates and applies security patches to fix vulnerabilities

System Requirements

Minimum Requirements

  • CPU: 8 cores
  • Memory: 16 GB RAM
  • Storage: 100 GB available disk space
  • Network: Stable internet connection for downloading dependencies

Note: Buttercup uses third-party AI providers (LLMs from companies like OpenAI, Anthropic and Google), which cost money. Please ensure that you manage per-deployment costs by using the built-in LLM budget setting.

Note: Buttercup works best with access to models from OpenAI and Anthropic, but can be run with at least one API key from one third-party provider. You can use a combination of OpenAI, Anthropic, and Google LLMs.

Supported Systems

  • Linux x86_64 (fully supported)
  • macOS (local development supported via Docker Desktop or Colima)
  • ARM64 (partial support for upstream Google OSS-Fuzz projects)

Required System Packages

Before setup, ensure you have these packages installed:

# Ubuntu/Debian
sudo apt-get update
sudo apt-get install -y make curl git

# RHEL/CentOS/Fedora
sudo yum install -y make curl git
# or
sudo dnf install -y make curl git

# MacOS
brew install make curl git

Supported Targets

Buttercup works with:

  • C source code repositories that are OSS-Fuzz compatible
  • Java source code repositories that are OSS-Fuzz compatible
  • Projects that build successfully and have existing fuzzing harnesses

Quick Start

  1. Clone the repository with submodules:
git clone --recurse-submodules https://github.com/trailofbits/buttercup.git
cd buttercup
  1. Run automated setup (Recommended)
make setup-local

This script will install all dependencies, configure the environment, and guide you through the setup process.

Note: If you prefer manual setup, see the Manual Setup Guide.

  1. Ensure Docker is running before deploying:
# Docker Desktop: start the application
# Colima (macOS/Linux alternative):
colima start --cpu 6 --memory 10 --disk 80
  1. Start Buttercup locally
make deploy
  1. Verify local deployment:
make status

When a deployment is successful, you should see all pods in "Running" or "Completed" status.

  1. Send Buttercup a simple task

Note: When tasked, Buttercup will start consuming third-party AI resources.

This command will make Buttercup pull down an example repo example-libpng with a known vulnerability. Buttercup will start fuzzing it to find and patch vulnerabilities.

make send-libpng-task
  1. Access Buttercup's web-based GUI

Run:

make web-ui

Then navigate to http://localhost:31323 in your web browser.

In the GUI you can monitor active tasks and see when Buttercup finds bugs and generates patches for them.

  1. Stop Buttercup

Note: This is an important step to ensure Buttercup shuts down and stops consuming third-party AI resources.

make undeploy

Accessing Logs

Buttercup includes local SigNoz deployment by default for comprehensive system observability. You can access logs, traces, and metrics through the SigNoz UI:

make signoz-ui

Then navigate to http://localhost:33301 in your web browser to view:

  • Distributed traces
  • Application metrics
  • Error monitoring
  • Performance insights

If you configured LangFuse during setup, you can also monitor LLM usage and costs there.

For additional log access methods, see the Quick Reference Guide.

Additional Resources

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