RedFlag leverages AI to determine high-risk code changes. Run it in batch mode to scope manual security testing of release candidates, or run it in your CI pipelines to flag PRs and add the appropriate reviewers. Despite being a security tool, RedFlag can be leveraged for almost any team as it's configuration makes it infinitely flexible.
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Table of Contents

• Batch Mode
• CI Mode
• Evaluation Mode
• Advanced Configuration
• Contributing
• License
• Contact

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Batch Mode

RedFlag is able to analyze a large number of commits in a single run. These commits can be specified using commit hashes, branch names, or tags. This is useful for scoping manual security testing of logical groups of code, such as release candidates.

Workflow

Getting Started

Installation

Use a Virtual Environment

1. Create a virtual environment:

   python -m venv redflag-venv
   source redflag-venv/bin/activate

2. Install RedFlag:

   pip install addepar-redflag

Alternatively, if you'd like to use Poetry, clone the repo and use poetry install and then poetry run redflag.

Setup Credentials

Credentials can be set using...

1. Environment variables
2. A .env file
3. CLI parameters
4. Configuration file (This is not recommended for security reasons!)

AWS Credentials

RedFlag uses Boto3-Compatible Credentials using Profiles or Env Vars. Ensure that your AWS IAM policy has InvokeModel and InvokeModelWithResponseStream permissions to Amazon Bedrock. Lastly, make sure you've requested the necessary Claude models!

GitHub PAT

Use a Personal Access Token with repo permissions. Set the token as an environment variable:

export RF_GITHUB_TOKEN=your-token-here

Jira API Token (Optional)

First, set a Jira URL (https://your-org.atlassian.net) in the configuration file (jira_url), as a CLI parameter (--jira-url), or as an environment variable (RF_JIRA_URL).

Then create a Jira API Token and set it as an environment variable:

export RF_JIRA_USER=your-username-here
export RF_JIRA_TOKEN=your-token-here

Usage

Here are some examples on how to run RedFlag in batch mode.

# Using branch names:
redflag --repo YourOrg/SomeRepo --from main --to dev
# Using commit hashes:
redflag --repo YouOrg/SomeRepo --from a1b2c3 --to d4e5f6
# With a custom configuration file:
redflag --config custom-config.yml

Report Output

By default, RedFlag produces an HTML report that can be opened in a browser.

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CI Mode

RedFlag can be run in CI pipelines to flag PRs and add the appropriate reviewers. This mode uses GitHub Actions to run RedFlag on every PR and post a comment if the PR requires a review.

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Evaluation Mode

RedFlag can be run in evaluation mode to evaluate the performance of the AI model using your own custom dataset. This mode is useful for understanding how the model and prompts perform on your codebase and aids in security risk evaluation.

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Advanced Configuration

Order of Precedence

1. CLI Parameters
2. Environment Variables
3. Configuration File
4. Default Values

On each execution, RedFlag will load the configuration in the order of precedence above and then output a table that shows the final configuration and where each parameter was set.

Configuration Options and Defaults

The following table shows configuration options for each parameter:

General Settings

Parameter	CLI Param	Env Var	Config File	Default
Configuration File	--config	-	-	-
Repository	--repo	RF_REPO	repo	-
Branch/Commit From	--from	RF_FROM	from	-
Branch/Commit To	--to	RF_TO	to	-

Integration Settings

Parameter	CLI Param	Env Var	Config File	Default
GitHub Token	--github-token	RF_GITHUB_TOKEN	github_token	-
Jira URL	--jira-url	RF_JIRA_URL	jira.url	-
Jira Username	--jira-user	RF_JIRA_USER	jira.user	-
Jira Token	--jira-token	RF_JIRA_TOKEN	jira.token	-

LLM Settings

Parameter	CLI Param	Env Var	Config File	Default
Debug LLM	--debug-llm	-	-	False
Bedrock Model ID	--bedrock-model-id	RF_BEDROCK_MODEL_ID	bedrock.model_id	anthropic.claude-3-sonnet-20240229-v1:0
Bedrock Profile	--bedrock-profile	RF_BEDROCK_PROFILE	bedrock.profile	-
Bedrock Region	--bedrock-region	RF_BEDROCK_REGION	bedrock.region	-
Review Prompt (Role)	-	-	prompts.review.role	Security review (see sample.config.yaml)
Review Prompt (Question)	-	-	prompts.review.question	Security review (see sample.config.yaml)
Test Plan Prompt (Role)	-	-	prompts.test_plan.role	Security review (see sample.config.yaml)
Test Plan Prompt (Question)	-	-	prompts.test_plan.question	Security review (see sample.config.yaml)

Input/Output Settings

Parameter	CLI Param	Env Var	Config File	Default
Output Directory	--output-dir	RF_OUTPUT_DIR	output_dir	results
Maximum Commits	--max-commits	RF_MAX_COMMITS	max_commits	0 (∞)
Don't Output HTML	--no-output-html	-	-	-
Don't Output JSON	--no-output-json	-	-	-
Don't Show Progress Bar	--no-progress-bar	-	-	-
Don't Strip HTML Comments	--no-strip-html-comments	-	-	-
Filter Commit Titles	-	-	filter_commits.title	-
Filter Commit Users	-	-	filter_commits.user	-
Strip Description Lines	-	-	strip_description_lines	-

Evaluation Parameters (eval Command)

Parameter	CLI Param	Env Var	Config File	Default
Evaluation Dataset	--dataset	RF_DATASET	dataset	-

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.md for more information.

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Contact

Say hi to Addepar Security Engineering at security-engineering@addepar.com.

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