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.

Product

Ostorlab has launched Cyber Models, a managed, prepaid AI infrastructure tier for Deep Agentic Scans. It gives security teams streamlined access to specialized models like GPT-5.5 Cyber and Opus 4.8 with Cyber Verification Program through approved provider channels. This bypasses the need to manage external API keys, provider rate limits, or fragmented billing dashboards.

Security

Walk the floor of any major cybersecurity conference today and you will hear about the promise of autonomous AI-powered platforms. But AI-only testing doesn't scale. A resilient AppSec program requires a cost-aware, tiered stack combining rapid traditional scanners, private semantic reviews, and selective orchestration of frontier models.

Security

This comprehensive guide covers the architecture, risk methodologies, and deployment frameworks required to architect an enterprise mobile app vetting strategy that protects corporate data assets without creating operational friction.

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.