6 articles
Open-source AI pentesting has split into distinct categories: autonomous application pentesters, multi-agent orchestration platforms, verification-first agents, and MCP tool layers. We reviewed 10 actively developed projects and explained what each one is genuinely good at.
Josuanstya Lovdianchel · 
A complete guide to application security in 2026, from secure design and software supply chains to testing, exploit validation, remediation, and Proof-Driven AppSec.
José Palanco · 
Jev AI returns typed decisions instead of prose. That could change where semantic judgment fits in security workflows, if teams keep policy, fallbacks, verification, and human review.
José Palanco · 
AI 编码工具正让开发者变得更快——但更快的开发也需要更好的可见性、更强的审查工作流以及更可靠的修复措施。这是一份面向采用 Codex、Claude Code、Cursor、Windsurf 及其他 AI 编码代理的团队的实用治理指南。
Josuanstya Lovdianchel · 
仅靠检测已无法跟上AI驱动的开发速度。AI原生修复是下一层防线——帮助团队在SDLC的每个阶段修复、验证并追踪AI生成代码中的漏洞。
Josuanstya Lovdianchel · 
AI 编码工具正在编写近一半的新代码。其中 45% 的代码在发布时至少包含一个漏洞。Vibe Coding 安全是一种保护由 AI 创建的软件安全的实践——在风险到达生产环境前进行检测、优先级排序和修复。
Josuanstya Lovdianchel · Plexicus is Proof-Driven AppSec: validated findings, contextual understanding, and reviewed remediation — anchored in evidence, scoped with you.
