bandit VS safety

Bandit is a tool designed to find common security issues in Python code. It was developed by the OpenStack Security Project and is a great addition to any serious Python project.

For the SAST stage, I used SonarQube tool. SonarQube is an open-source platform developed by SonarSource for continuous inspection of code quality to perform automatic reviews with static analysis of code to detect bugs and code smells on more than 30 programming languages. I preferred SonarQube instead of other SAST tools because it has a detailed documentation and plugins about integration with Jenkins and SonarQube works with Java projects pretty well. Of course you can similar multi-language-supported tools such as Semgrep or language-specific tools such as Bandit.

repos: - repo: https://github.com/PyCQA/bandit rev: 1.7.7 hooks: - id: bandit args: ["-c", "pyproject.toml", "-r", "."] additional_dependencies: ["bandit[toml]"]

On the other hand, Bandit is a dedicated security scanner designed to target critical security concerns such as SQL injection and cross-site scripting exploits. It meticulously scrutinizes the codebase to identify and alert developers about possible security breaches or vulnerabilities, thus fortifying the code against potential exploitation.

Bandit: is a tool designed for Python applications to analyse your code for potential security issues like insecure use of functions, hardcoded password and much more.

Bandit (for Python, open-source and free)

Add security checks (Bandit) and dependency checks (safety)

bandit

flake8-bandit which uses bandit for security linting.

Safety and Dependabot complement these security tools by focusing on external dependencies. Safety takes charge of examining your dependencies, ensuring they are up-to-date and free from any known vulnerabilities. Dependabot works similarly, scanning dependencies, verifying if they're current and assessing them for potential security flaws. This function is crucial as weaknesses in external dependencies can compromise the security of the entire codebase.

https://pyup.io/safety/ for security checks of dependencies

I'm trying this tool at the moment: https://pyup.io/safety/

Checking could be done if something like this eventually shows up in safety or pip-audit.

A 2021 security report by Snyk, states 47% of Python projects contain known vulnerabilities. On the bright hand side, almost 87% of known vulnerabilities can be resolved by upgrading the vulnerable package. Safety checks your installed dependencies for known security vulnerabilities.

safety: Safety checks your installed dependencies for known security vulnerabilities.

There is a python project called "safety". https://github.com/pyupio/safety If you run this after every package install or update and on a regular cadence it should catch the worst offenders much easier than having someone manually review every package.