#AppSec

Security

A technical post-mortem on an AI agent that wandered outside its testing scope during an authorized API assessment, what caused it, and the four-layer system we built to stop it.

Security

Compare traditional pentests, PTaaS and autonomous AI testing: where agents win on coverage and evidence, where humans still lead, and how to combine them.

Security

Detection and enforcement are different security properties. Across five production banking apps protected by four commercial shielding products, the detection was sophisticated and the enforcement was fragile.

Security

A comprehensive guide on what penetration testing is, how much it costs for startups in 2026, the 5-step testing process, and how to choose the right vendor for your tech stack.

Security

AI can produce a convincing exploit story in seconds. Runtime proof, negative controls, and human review determine whether that story becomes a finding a security team can trust.

Security

Ostorlab vs Aikido on SAST, web and API pentesting, mobile binary testing, cloud posture, BYOK and remediation, with a side-by-side table and an FAQ.

Security

Discover how agentic AI catches business logic flaws rule-based scanners miss. See a real exploit chain escalating a fixed finding to tenant-wide compromise.

Security

A practical guide to designing AI-assisted security testing workflows that turn scoped evidence into reviewable findings through structured outputs, validation gates, and controlled execution.

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.

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.