Raven - CI/CD Security Analyzer

RAVEN (Risk Analysis and Vulnerability Enumeration for CI/CD) is a powerful security tool designed to perform massive scans for GitHub Actions CI workflows and digest the discovered data into a Neo4j database.

With Raven, we were able to identify and report security vulnerabilities in some of the most popular repositories hosted on GitHub, including:

• FreeCodeCamp (the most popular project on GitHub)
• Storybook (One of the most popular frontend frameworks)
• Fluent UI by Microsoft
• and much more

We listed all vulnerabilities discovered using Raven in the tool Hall of Fame.

What is Raven

The tool provides the following capabilities to scan and analyze potential CI/CD vulnerabilities:

• ⏬ Downloader: You can download workflows and actions necessary for analysis. Workflows can be downloaded for a specified organization or for all repositories, sorted by star count. Performing this step is a prerequisite for analyzing the workflows.
• 📊 Indexer: Digesting the downloaded data into a graph-based Neo4j database. This process involves establishing relationships between workflows, actions, jobs, steps, etc.
• 📚 Query Library: We created a library of pre-defined queries based on research conducted by the community.
• ❗ Report: Raven has a simple way of reporting suspicious findings. As an example, it can be incorporated into the CI process for pull requests and run there.

Possible usages for Raven:

• Scanner for your own organization's security
• Scanning specified organizations for bug bounty purposes
• Scan everything and report issues found to save the internet
• Research and learning purposes

This tool provides a reliable and scalable solution for CI/CD security analysis, enabling users to query bad configurations and gain valuable insights into their codebase's security posture.

Why Raven

In the past year, Cycode Labs conducted extensive research on fundamental security issues of CI/CD systems. We examined the depths of many systems, thousands of projects, and several configurations. The conclusion is clear – the model in which security is delegated to developers has failed. This has been proven several times in our previous content:

• A simple injection scenario exposed dozens of public repositories, including popular open-source projects.
• We found that one of the most popular frontend frameworks was vulnerable to the innovative method of branch injection attack.
• We detailed a completely different attack vector, 3rd party integration risks, the most popular project on GitHub, and thousands more.
• Finally, the Microsoft 365 UI framework, with more than 300 million users, is vulnerable to an additional new threat – an artifact poisoning attack.
• Additionally, we found, reported, and disclosed hundreds of other vulnerabilities privately.

Each of the vulnerabilities above has unique characteristics, making it nearly impossible for developers to stay up to date with the latest security trends. Unfortunately, each vulnerability shares a commonality – each exploitation can impact millions of victims.

It was for these reasons that Raven was created, a framework for CI/CD security analysis workflows (and GitHub Actions as the first use case). In our focus, we examined complex scenarios where each issue isn't a threat on its own, but when combined, they pose a severe threat.

Setup

Clone Raven repository.

git clone https://github.com/CycodeLabs/Raven.git
cd Raven

Build and run neo4j && redis containers.

make setup

Usage

The tool contains two main functionalities, download and index.

Download

usage: main.py download [-h] --token TOKEN [--output OUTPUT]
                        [--max_stars MAX_STARS] [--min_stars MIN_STARS]

optional arguments:
  -h, --help            show this help message and exit
  --token TOKEN         GITHUB_TOKEN to download data from Github API
                        (Needed for effective rate-limiting)
  --output OUTPUT, -o OUTPUT
                        Output directory to download the workflows
  --max-stars MAX_STARS
                        Maximum number of stars for a repository
  --min-stars MIN_STARS
                        Minimum number of stars for a repository

Index

usage: main.py index [-h] [--input INPUT] [--neo4j-uri NEO4J_URI]
                     [--neo4j-user NEO4J_USER]
                     [--neo4j-pass NEO4J_PASS] [--threads THREADS]
                     [--clean]

optional arguments:
  -h, --help            show this help message and exit
  --input INPUT, -i INPUT
                        Input directory with the downloaded workflows
  --neo4j-uri NEO4J_URI
                        Neo4j URI endpoint
  --neo4j-user NEO4J_USER
                        Neo4j username
  --neo4j-pass NEO4J_PASS
                        Neo4j password
  --threads THREADS, -t THREADS
                        Number of threads
  --clean, -c           Whether to clean cache, and index from scratch

Rate Limiting

For effective rate limiting, you should supply a Github token. For authenticated users, the next rate limiting applies:

• Code search - 30 queries per minute
• Any other API - 5000 per hour

Functionalities

Downloader

• If the workflow contains an action, the downloader will also download it.
• If the workflow references a reusable workflow, the downloader will also download it.

Indexer

• If the indexer finds workflow uses an action, it will create a proper connection to it in the graph
• Same applies to reusable workflows
• Same applies to workflow triggered through workflow_call

Knowledge Base

• Issue Injections
• Pull Request Injections
• Workflow Run Injections
• CodeSee Injections

Hall of Fame - Vulnerabilities Found and Disclosed Using Raven

Name	Stars	Fix	Additional Sources
freeCodeCamp/freeCodeCamp		CodeSee package update, 0871341	Blog
storybookjs/storybook		ffb8558	Blog
tiangolo/fastapi		9efab1b	LinkedIn
withastro/astro		650fb1a	Blog
statelyai/xstate		CodeSee package update, bb84664	Blog
docker-slim/docker-slim		CodeSee package update	Blog
microsoft/fluentui		2ea6195	Blog
tiangolo/sqlmodel		cf36b2d	LinkedIn
tiangolo/typer		0c106a1	LinkedIn
autogluon/autogluon		ca18fa9
liquibase/liquibase		3278525	Blog
ossf/scorecard		c9f582b
Ombi-app/Ombi		5cc0d77	Blog
wireapp/wire-ios		9d39d6c	Blog
cloudscape-design/components		2921d2d
DynamoDS/Dynamo		Disabled workflow	Blog
fauna/faunadb-js		ee6f53f	Blog
apache/incubator-kie-kogito-runtimes		53c18e5	Blog

Current Limitations

• It is possible to run external action by referencing a folder with a Dockerfile (without action.yml). Currently, this behavior isn't supported.
• It is possible to run external action by referencing a docker container through the docker://... URL. Currently, this behavior isn't supported.
• It is possible to run an action by referencing it locally. This creates complex behavior, as it may come from a different repository that was checked out previously. The current behavior is trying to find it in the existing repository.
• We aren't modeling the entire workflow structure. If additional fields are needed, please submit a pull request according to the contribution guidelines.

Future Research Work

• Implementation of taint analysis. Example use case - a user can pass a pull request title (which is controllable parameter) to an action parameter that is named data. That action parameter may be used in a run command: - run: echo ${{ inputs.data }}, which creates a path for a code execution.
• Expand the research for findings of harmful misuse of GITHUB_ENV. This may utilize the previous taint analysis as well.
• Research whether actions/github-script has an interesting threat landscape. If it is, it can be modeled in the graph.

Contribution

TODO

License

Apache License 2.0