Engineering

Learn how source code security testing works, why traditional SAST creates false positives, and how agentic analysis turns scanner signals into actionable findings.

Security

A technical assessment of the latest version of GoPhish that examines how the platform handles trust: identity, untrusted content, object ownership, credential lifecycle, and outbound requests. Source-code analysis with Ostorlab Agentic Deep Scan established the eight report-level findings, PoCs, and remediation priorities.

Security

A technical comparison of Ostorlab and Quokka Q-mast Mobile Application Security Testing (MAST) tools, highlighting their foundational DAST capabilities and advanced AI agentic features for DevSecOps.

Product

Source Code Scanning helps you identify security vulnerabilities directly in your source code before they reach production. Connect your repositories, run scans on demand, and review actionable findings from within Ostorlab.

Product

This release introduces App Vetting, Scan Coverage Heatmap, Mobile Shielding Scan, Deep Agentic Scan improvements, Cyber Models, additional model support, and source code scanning.

Product

The latest Deep Agentic Scan release introduces faster mobile testing, improved reverse engineering, stronger vulnerability detection, incremental coverage through historical scan processing, improved vulnerability chaining, and managed Cyber Models for mobile and web assessments.

Product

Ostorlab has launched Mobile Shielding Scan, an automated, AI-powered testing solution designed specifically for shielding detection and validation. It gives security teams streamlined, continuous validation of critical iOS and Android runtime protections, identifying whether security shields are actually present and if they can withstand real-world attacks. This empowers organizations with an automated, scalable, and powerful way to continuously validate RASP tools and mobile self-defense layers across every release.

Engineering

Agentic harnesses change what an LLM can do in mobile app security testing. On its own, a model can name likely risks such as insecure storage, exposed secrets, risky permissions, vulnerable SDKs, backend issues, and privacy exposure, but the app may remain untouched. With the right tools, context, memory, prompts, execution loops, and runtime feedback around it, the model can inspect the app package, observe behavior, follow traffic, connect signals, and leave behind evidence a security team can review. From permission analysis to GEF-powered native exploitation, the difference is visible in the trace: app evidence, tool output, runtime proof, and reproducible steps instead of report-shaped text.