Add CodeQL Workflow for Code Security Analysis

[b4yuan]

Pull Request Template

Description

Summary

This pull request introduces a CodeQL workflow to enhance the security analysis of this repository.

What is CodeQL

CodeQL is a static analysis tool that helps identify and mitigate security vulnerabilities. It is primarily intra-function but does provide some support for inter-function analysis. By integrating CodeQL into a GitHub Actions workflow, it can proactively identify and address potential issues before they become security threats.

For more information on CodeQL and how to interpret its results, refer to the GitHub documentation and the CodeQL documentation (https://codeql.github.com/ and https://codeql.github.com/docs/).

What this PR does

We added a new CodeQL workflow file (.github/workflows/codeql.yml) that

• Runs on every pull request (functionality to run on every push to main branches is included as a comment for convenience).
• Runs daily.
• Excludes queries with a high false positive rate or low-severity findings.
• Does not display results for git submodules, focusing only on our own codebase.

Validation

To validate the functionality of this workflow, we have run several test scans on the codebase and reviewed the results. The workflow successfully compiles the project, identifies issues, and provides actionable insights while reducing noise by excluding certain queries and third-party code.

Using the workflow results

If this pull request is merged, the CodeQL workflow will be automatically run on every push to the main branch and on every pull request to the main branch. To view the results of these code scans, follow these steps:

1. Under the repository name, click on the Security tab.
2. In the left sidebar, click Code scanning alerts.

Is this a good idea?

We are researchers at Purdue University in the USA. We are studying the potential benefits and costs of using CodeQL on open-source repositories of embedded software.

We wrote up a report of our findings so far. The TL;DR is “CodeQL outperforms the other freely-available static analysis tools, with fairly low false positive rates and lots of real defects”. You can read about the report here: https://arxiv.org/abs/2310.00205

Review of engineering hazards

License: see the license at https://github.com/github/codeql-cli-binaries/blob/main/LICENSE.md:

Here's what you may also do with the Software, but only with an Open Source Codebase and subject to the License Restrictions provisions below:

Perform analysis on the Open Source Codebase.

If the Open Source Codebase is hosted and maintained on GitHub.com, generate CodeQL databases for or during automated analysis, CI, or CD.

False positives: We find that around 20% of errors are false positives, but that these FPs are polarized and only a few rules contribute to most FPs. We find that the top rules contributing to FPs are: cpp/uninitialized-local, cpp/missing-check-scanf, cpp/suspicious-pointer-scaling, cpp/unbounded-write, cpp/constant-comparison, and cpp/inconsistent-null-check. Adding a filter to filter out certain rules that contribute to a high FP rate can be done simply in the workflow file.

Fixes # (issue)

Type of change

Please delete options that are not relevant.

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested?

We have built and ran CodeQL on a fork of libc and verified that all functionality works as expected. Vulnerabilities can be reflected in Code scanning under the Security tab:

[b4yuan]

I want to clarify that fail_on_error.py is a script that forces the step to fail if an error is found within the code (which in turn makes the entire workflow seem like it failed). However, looking at the steps, the workflow successfully built libc, analyzed it, and uploaded the results.