trailofbits/buttercup

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# Buttercup Cyber Reasoning System (CRS) [![Tests](https://github.com/trailofbits/buttercup/actions/workflows/tests.yml/badge.svg)](https://github.com/trailofbits/buttercup/actions/workflows/tests.yml) [![Component Integration Tests](https://github.com/trailofbits/buttercup/actions/workflows/comp-integration.yml/badge.svg)](https://github.com/trailofbits/buttercup/actions/workflows/comp-integration.yml) [![System Integration](https://github.com/trailofbits/buttercup/actions/workflows/integration.yml/badge.svg)](https://github.com/trailofbits/buttercup/actions/workflows/integration.yml) **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](https://github.com/tob-challenges/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: ```bash # 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: ```bash git clone --recurse-submodules https://github.com/trailofbits/buttercup.git cd buttercup ``` 1. Run automated setup (Recommended) ```bash 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](guides/MANUAL_SETUP.md). 1. Ensure Docker is running before deploying: ```bash # Docker Desktop: start the application # Colima (macOS/Linux alternative): colima start --cpu 6 --memory 10 --disk 80 ``` 1. Start Buttercup locally ```bash make deploy ``` 1. Verify local deployment: ```bash 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](https://github.com/tob-challenges/example-libpng) with a known vulnerability. Buttercup will start fuzzing it to find and patch vulnerabilities. ```bash make send-libpng-task ``` 1. Access Buttercup's web-based GUI Run: ```bash 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. ```bash 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: ```bash 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](guides/QUICK_REFERENCE.md). ## Additional Resources - [Quick Reference Guide](guides/QUICK_REFERENCE.md) - Common commands and troubleshooting - [Manual Setup Guide](guides/MANUAL_SETUP.md) - Detailed manual installation steps - [AKS Deployment Guide](guides/AKS_DEPLOYMENT.md) - Production deployment on Azure - [Contributing Guidelines](CONTRIBUTING.md) - Development workflow and standards - [Deployment Documentation](deployment/README.md) - Advanced deployment configuration - [Writing Custom Challenges](guides/CUSTOM_CHALLENGES.md) - Custom project configuration and setup - [Unscored rounds](guides/UNSCORED.md) - Running unscored round challenges - [Scored round](guides/SCORED.md) - Parsing post-final round results

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debug-buttercup

مديرو الشبكات وأنظمة الحاسوب

Debugs the Buttercup CRS (Cyber Reasoning System) running on Kubernetes. Use when diagnosing pod crashes, restart loops, Redis failures, resource pressure, disk saturation, DinD issues, or any service misbehavior in the crs namespace. Covers triage, log…

لغة النص الأصلي: الإنجليزية

2026-08-03

buttercup-langfuse

مطوّرو البرمجيات

Buttercup-specific Langfuse wiring. Use when adding LLM tracing to a new LangChain/LangGraph entry point in this repo, attaching tags/metadata to existing chains, troubleshooting "no traces in Langfuse" against this codebase, or touching the Helm/values…

لغة النص الأصلي: الإنجليزية

2026-05-19

langfuse

مطوّرو البرمجيات

Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts,…

لغة النص الأصلي: الإنجليزية

2026-05-19

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