Tag Archives: automated

CAN YOU EXPLAIN THE PROCESS OF DEVELOPING AUTOMATED PENETRATION TESTS AND VULNERABILITY ASSESSMENTS

The development of automated penetration tests and vulnerability assessments is a complex process that involves several key stages. First, the security team needs to conduct an initial assessment of the systems, applications, and environments that will be tested. This includes gathering information about the network architecture, identifying exposed ports and services, enumerating existing hosts, and mapping the systems and their interconnections. Security tools like network scanners, port scanners, and vulnerability scanners are used to automatically discover as much as possible about the target environment.

Once the initial discovery and mapping is complete, the next stage involves defining the rulesets and test procedures that will drive the automated assessments. Vulnerability researchers carefully review information from vendors and data sources like the Common Vulnerabilities and Exposures (CVE) database to understand the latest vulnerabilities affecting different technology stacks and platforms. For each identified vulnerability, security engineers will program rules that define how to detect if the vulnerability is present. For example, a rule might check for a specific vulnerability by sending crafted network packets, testing backend functions through parameter manipulation, or parsing configuration files. All these detection rules form the core of the assessment policy.

In addition to vulnerability checking, penetration testing rulesets are developed that define how to automatically simulate the tactics, techniques and procedures of cyber attackers. For example, rules are created to test for weak or default credentials, vulnerabilities that could lead to privilege escalation, vulnerabilities enabling remote code execution, and ways that an external attacker could potentially access sensitive systems in multi-stage attacks. A key challenge is developing rules that can probe for vulnerabilities while avoiding any potential disruption to production systems.

Once the initial rulesets are created, they must then be systematically tested against sample environments to ensure they are functioning as intended without false positives or negatives. This involves deploying the rules against virtual or isolated physical systems with known vulnerability configurations. The results of each test are then carefully analyzed by security experts to validate if the rules are correctly identifying and reporting on the intended vulnerabilities and vulnerabilities. Based on these test results, the rulesets are refined and tuned as needed.

After validation testing is complete, the automation framework is then deployed in the actual target environment. Depending on the complexity, this process may occur in stages starting with non-critical systems to limit potential impact. During the assessments, results are logged in detail to provide actionable data on vulnerabilities, affected systems, potential vectors of compromise, and recommendations for remediation.

Simultaneously with the deployment of tests, the need for ongoing maintenance of the assessment tools and rulesets must also be considered. New vulnerabilities are constantly being discovered requiring new detection rules to be developed. Systems and applications in the target environment may change over time necessitating ruleset updates. Therefore, there needs to be defined processes for ongoing monitoring of vulnerability data sources, periodic reviews of effectiveness of existing rules, and maintenance releases to keep the assessments current.

Developing robust, accurate, and reliable automated penetration tests and vulnerability assessments is a complex and iterative process. With the proper resources, skilled personnel and governance around testing and maintenance, organizations can benefit from the efficiency and scalability of automation while still gaining insight into real security issues impacting their environments. When done correctly, it streamlines remediation efforts and strengthens security postures over time.

The key stages of the process include: initial discovery, rule/test procedure development, validation testing, deployment, ongoing maintenance, and integration into broader vulnerability management programs. Taking the time to systematically plan, test and refine automated assessments helps to ensure effective and impactful results.

HOW CAN I SET UP CONTINUOUS INTEGRATION FOR AUTOMATED TESTING

Continuous integration (CI) is a development practice that requires developers to integrate code into a shared repository several times a day. Each check-in is then verified by an automated build, allowing teams to detect problems early. Setting up CI enables automated testing to run with every code change, catching bugs or issues quickly.

To set up CI, you will need a source code repository to store your code, a CI server to run your builds, and configuration to integrate your repository with the CI server. Some popular open source options are GitHub for the repository and Jenkins, GitLab CI, or Travis CI for the CI server. You can also use hosted CI/CD services that provide these tools together.

The first step is to store your code in a version control repository like GitHub. If you don’t already have one, create a new repository and commit your initial project code. Make sure all developers on the team have push/pull access to this shared codebase.

Next, you need to install and configure your chosen CI server software. If using an on-premise solution like Jenkins, install it on a build server machine following the vendor’s instructions. For SaaS CI tools, sign up and configure an account. During setup, connect the CI server to your repository via its API so it can detect new commits.

Now you need to set up a continuous integration pipeline – a series of steps that will run automated tests and tasks every time code is pushed. The basic pipeline for automated testing includes:

Checking out (downloading) the code from the repository after every push using the repository URL and credentials configured earlier. This fetches the latest changes.

Running automated tests against the newly checked out code. Popular unit testing frameworks include JUnit, Mocha, RSpec etc depending on your language/stack. Configure the CI server to execute npm test, ./gradlew test etc based on your project.

Reporting test results. Have the CI server publish success/failure reports to provide feedback on whether tests passed or failed after each build.

Potentially deploying to testing environments. Some teams use CI to also deploy stable builds to testing systems after tests pass, to run integration or UI tests.

Archiving build artifacts. Save logs, test reports, packages/binaries generated by the build for future reference.

Email notifications. Configure the CI server to email developers or operations teams after each build with its status.

You can define this automated pipeline in code using configuration files specific to your chosen CI server. Common formats include Jenkinsfile for Jenkins, .travis.yml for Travis etc. Define stages for the steps above and pin down the commands, scripts or tasks required for each stage.

Trigger the pipeline by making an initial commit to the repository that contains the configuration file. The CI server should detect the new commit, pull the source code and automatically run your defined stages one after the other.

Developers on the team can now focus on development and committing new changes without slowing down to run tests manually every time. As their commits are pushed, the automated pipeline will handle running tests without human involvement in between. This allows for quicker feedback on issues and faster iterations.

Some additional configuration you may want to add includes:

Caching node_modules or other dependencies between builds for better performance

Enabling parallel job execution to run unit/integration tests simultaneously

Defining environments and deploy stages to provision and deploy to environments like staging automatically after builds

Integrating with slack/teams for custom notifications beyond email

Badge status widgets to showcase build trends directly on READMEs

Gating deployment behind all tests passing to ensure quality

Code quality checks via linters, static analysis tools in addition to tests

Versioning and tagging releases automatically when builds are stable

Continuous integration enables teams to adopt test-driven development processes through automation. Bugs are found early in the commit cycle rather than late. The feedback loop is tightened and iteration speeds up considerably when testing happens seamlessly with every change. This paves the way for higher code quality, fewer defects and faster delivery of working software.