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What is Mayhem?

Mayhem is a cutting-edge fuzz testing platform that combines guided fuzzing with symbolic execution, utilizing a patented technology conceived at CMU. This advanced solution greatly reduces the necessity for manual testing by automatically identifying and validating software defects. By promoting the delivery of safe, secure, and dependable software, it significantly cuts down on the time, costs, and effort usually involved. A key feature of Mayhem is its ability to accumulate intelligence about its targets over time; as it learns, it refines its analysis and boosts overall code coverage. Each vulnerability it uncovers represents a confirmed and exploitable risk, allowing teams to prioritize their remediation efforts effectively. Moreover, Mayhem supports the remediation process by offering extensive system-level insights, including backtraces, memory logs, and register states, which accelerate the identification and resolution of problems. Its capacity to create custom test cases in real-time based on feedback from the target eliminates the need for any manual test case generation. Additionally, Mayhem guarantees that all produced test cases are easily accessible, transforming regression testing into a seamless and ongoing component of the development workflow. This remarkable blend of automated testing and intelligent feedback not only distinguishes Mayhem in the field of software quality assurance but also empowers developers to maintain high standards throughout the software lifecycle. As a result, teams can harness Mayhem's capabilities to foster a more efficient and effective development environment.

What is Code Intelligence?

Our platform employs a range of robust security strategies, such as feedback-driven fuzz testing and coverage-guided fuzz testing, to produce an extensive array of test cases that identify elusive bugs within your application. This white-box methodology not only helps mitigate edge cases but also accelerates the development process. Cutting-edge fuzzing engines are designed to generate inputs that optimize code coverage effectively. Additionally, sophisticated bug detection tools monitor for errors during the execution of code, ensuring that only genuine vulnerabilities are exposed. To consistently reproduce errors, you will require both the stack trace and the input data. Furthermore, AI-driven white-box testing leverages insights from previous tests, enabling a continuous learning process regarding the application's intricacies. As a result, you can uncover security-critical bugs with ever-increasing accuracy, ultimately enhancing the reliability of your software. This innovative approach not only improves security but also fosters confidence in the development lifecycle.

Media

Media

Integrations Supported

Docker
Jenkins
Travis CI
Bamboo
Drone
Google Cloud Platform
Okta
Slack

Integrations Supported

Docker
Jenkins
Travis CI
Apache Maven
C++
CLion
CircleCI
GitHub
GitLab
Gradle
JUnit
JavaScript
Jira
Kubernetes
Vim

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

ForAllSecure

Date Founded

2012

Company Location

United States

Company Website

www.forallsecure.com

Company Facts

Organization Name

Code Intelligence

Company Location

Germany

Company Website

www.code-intelligence.com

Categories and Features

Automated Testing

Security Testing

Code Coverage

Not specified

Fuzz Testing

Not specified

Regression Testing

Not specified

Categories and Features

Application Security

Analytics / Reporting
Training Resources
Vulnerability Detection
Vulnerability Remediation

Code Coverage

Not specified

Fuzz Testing

Not specified

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