Henrik Brodin
130d6576ce
The tracer bot failed to reproduce crashes on ARM64 due to incorrect parameter passing in the reproduce_impl patch. The err_result value was being passed as a positional argument to docker_run(), which interpreted it as print_output=False, redirecting all output to /dev/null. Changes: - Fix reproduce_impl to pass architecture as keyword arg only - Refactor ARM64 patching into separate dockerfile and runner functions - Add comprehensive helper.py patching for ARM64: * Patch image_name variables to use :manifest-arm64v8 tag * Patch BASE_RUNNER_IMAGE assignment with tag stripping * Fix debug mode tag insertion (insert -debug before tag, not after) * Prevent double-tagging in _get_base_runner_image() - Default CLI architecture parameters to ARCHITECTURE constant This enables the tracer bot to see fuzzer output and successfully detect crash reproduction on ARM64 systems. Co-authored-by: Riccardo Schirone <562321+ret2libc@users.noreply.github.com>
Buttercup Cyber Reasoning System (CRS)
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)
- 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
- Clone the repository with submodules:
git clone --recurse-submodules https://github.com/trailofbits/buttercup.git
cd buttercup
- 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.
- Start Buttercup locally
make deploy
- Verify local deployment:
make status
When a deployment is successful, you should see all pods in "Running" or "Completed" status.
- 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
- 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.
- 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
- Quick Reference Guide - Common commands and troubleshooting
- Manual Setup Guide - Detailed manual installation steps
- AKS Deployment Guide - Production deployment on Azure
- Contributing Guidelines - Development workflow and standards
- Deployment Documentation - Advanced deployment configuration
- Writing Custom Challenges - Custom project configuration and setup
- Unscored rounds - Running unscored round challenges
- Scored round - Parsing post-final round results