Product

Agentic Scan Knowledge gives Ostorlab security agents persistent application context, allowing every scan to build on previous tests instead of starting again from zero.

Product

Ostorlab Mobile Shielding Scan tests Android and iOS protections against real bypasses, including root detection, anti-tampering, certificate pinning, and obfuscation.

Product

Ostorlab Threat Center now brings EUVD intelligence alongside NVD data, giving security teams broader vulnerability visibility as they prepare for the EU Cyber Resilience Act.

Engineering

Learn what separates AI-powered scanning from AI pentesting and how Ostorlab Deep Agentic Scan follows evidence to validate real attack paths.

Engineering

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

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.

Product

Ostorlab has launched App Vetting, a mobile application risk assessment solution that helps teams evaluate Android and iOS apps before approval. It combines static analysis, dynamic testing, and secure sandbox execution with continuous monitoring, weighted risk scoring, and agentic workflows to identify vulnerabilities, privacy risks, malware indicators, telemetry behavior, and trust issues while helping teams prioritize what matters most.

Engineering

We built an AI-powered pull request reviewer, shut it down after hallucinations and false positives eroded developer trust, then rebuilt it with better models, broader context, and a more conservative agent architecture. This article shares what we learned about automated code review, why trust matters more than coverage, and how AI reviewers can help engineering teams reduce repetitive review work without replacing human judgment.