惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

WordPress大学
WordPress大学
小众软件
小众软件
MongoDB | Blog
MongoDB | Blog
Hugging Face - Blog
Hugging Face - Blog
Jina AI
Jina AI
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Stack Overflow Blog
Stack Overflow Blog
L
LangChain Blog
大猫的无限游戏
大猫的无限游戏
量子位
A
About on SuperTechFans
G
Google Developers Blog
雷峰网
雷峰网
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
IT之家
IT之家
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园_首页
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Vercel News
Vercel News
V
Visual Studio Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 聂微东
U
Unit 42
Apple Machine Learning Research
Apple Machine Learning Research

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
How to Get Ongoing Security Advice While Building on Lovable
George Psist · 2026-04-30 · via DEV Community

Building on Lovable is fast. You go from idea to working product in hours. And Lovable's built-in security covers the fundamentals: safe defaults, low-level vulnerability scans, solid infrastructure.

But as you move from prototype to real product, security questions start coming up that those defaults don't answer. Is this endpoint properly protected? Am I handling user data correctly? Did this new feature introduce something? Is my application actually secure? Not just "no obvious vulnerabilities," but secure?

These questions don't come up once. They come up continuously as your app evolves. And the existing options aren't great: hire a pentester (expensive, point-in-time, tells you about problems after you've already shipped them) or become a security expert yourself (you're building a product, not studying for a certification).

What we built

Trent's Security Advisor for Lovable is a security agent that continuously reviews your application as you build it. Not a one-time scan. Ongoing analysis that keeps up with your changes.

Under the hood, multiple agents work together: scanning your code, filtering what actually matters from the noise, building a prioritized plan to fix what they find. When you approve a fix, Trent connects directly to Lovable via MCP and implements it. No manual triaging, no copy-pasting patches.

You can also ask security questions whenever they come up. "Is this API endpoint safe?" "Am I storing user data correctly?" "What should I tell my investor about security?" You get specific answers grounded in your actual codebase, not generic advice.

How it works

  1. Connect your GitHub repo to Trent and install the Trent MCP server in Lovable's settings.
  2. Start your first security assessment. Trent scans your project and builds a prioritized plan.
  3. Review the plan and approve fixes. Trent implements them directly in Lovable via MCP.

That's the whole setup. You build with Lovable. You secure with Trent.

What makes this different from a pentest

A pentest is a snapshot. It tells you what's wrong at one point in time, after you've already built it. Over 75% of vulnerabilities are introduced during design and development. A pentest just tells you about them after the fact.

Trent runs continuously. Every change you make, every feature you add, the assessment updates. You catch issues while you're still building, not after you've shipped.

And you don't need security expertise to use it. The findings come in plain language with specific fixes. "Your RLS policies don't cover this table" is more useful than "finding: authorization bypass, severity: high."

Get started

Set up takes a few minutes: trent.ai/solutions/lovable-security

You build. Trent secures.


Built by Trent AI. AI security for your agents.