
Dependency Drift: How AI Tools Expand Your Attack Surface
AI-assisted development quietly introduces new dependencies, version bumps, and build hooks. Here is how to govern dependency changes before they reach production.
Analysis and field notes from the Cyblox team.

AI-assisted development quietly introduces new dependencies, version bumps, and build hooks. Here is how to govern dependency changes before they reach production.

If you cannot measure outcomes and explain enforcement decisions, bot defense becomes a permanent tuning exercise. Here is the KPI model that holds up.

How sensitive data, internal context, and credentials leak from prompts into AI-generated code — and where to catch them before merge.

Scraping is no longer just copying pages. It is pricing intelligence, inventory monitoring, and model training — executed continuously and quietly.

AI is removing humans from the cyber attack loop faster than it is removing them from the cyber defense loop. That asymmetry is becoming one of the biggest security problems.

A practical checklist for reviewing AI-assisted pull requests without slowing teams down: fast checks, deep checks, and what to automate pre-merge.

Defenders protect the login box. Attackers optimize the entire journey: login, reset, MFA fatigue, session reuse, and downstream abuse.

Most risky outcomes in AI-assisted development aren’t triggered by obviously dangerous actions. They hide behind routine steps whose meaning changes with the diff.

CAPTCHA is easy to deploy and easy to justify. But in high-value channels it often becomes a recurring UX tax while adaptive automation keeps moving.