Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Security teams face limited resources and a growing attack surface while developers struggle with security responsibilities that feel burdensome, annoying, or seem to conflict with their first priorities. AppSec teams turn to static application security testing (SAST) tools to identify vulnerabilities in first-party code early in the software development lifecycle while developers can still fix issues before the code is old and forgotten about.

We’re thrilled to announce the general availability of Spark, an AI Test Agent that lowers the entry barrier to white-box fuzz testing. In this blog, we explain how Spark works and share the main results from its beta testing that prove its effectiveness.

Modern software applications operate within increasingly complex ecosystems, spanning multiple layers of the stack—from the user interface and application logic to APIs, databases, and third-party dependencies. Each layer introduces unique vulnerabilities, often requiring specialized domain expertise to identify and mitigate.

Below, we’ll take a look at how both DAST as a methodology and DAST as a tool relate to what we do at Detectify. More specifically, we’ll explain how Detectify’s solution applies DAST methodology to the full breadth of an attack surface, automating the heck out of application security testing. With these methods, we cover millions of domains before you’ve even had breakfast.

Below, we’ll take a look at how both DAST as a methodology and DAST as a tool relate to what we do at Detectify. More specifically, we’ll explain how Detectify’s solution applies DAST methodology to the full breadth of an attack surface, automating the heck out of application security testing. With these methods, we cover millions of domains before you’ve even had breakfast.

At the 6th de:hub Pitch Night, an event celebrating innovation and digital transformation, Code Intelligence was honored with the prestigious German AI Award. This recognition, awarded by a distinguished jury of industry experts and investors, underscores Code Intelligence’s pivotal role in advancing software security through artificial intelligence.

Fuzz testing is most commonly classified based on its analysis approach. The two main categories of fuzz testing solutions are white-box and black-box fuzzers. In this blog, we explain the difference between approaches as well as their main features.

We are super excited to announce the discovery of a heap-based use-after-free vulnerability in wolfSSL, identified through a fuzz test automatically generated by an AI Test Agent. This marks another milestone in advancing automated security testing and demonstrates the power of AI-driven tools to improve software reliability and safety.