开发者工具1 month ago · 1 次试用
Targe — LLM security scanning & audit reports
用对抗测试检查LLM端点安全，获取OWASP审计报告。
@aryaan_sheth · X
- LLM security for small teams
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用对抗测试检查LLM端点安全，获取OWASP审计报告。
@aryaan_sheth · X
- LLM security for small teams
为GitHub仓库运行安全审计，检测密钥泄露、认证问题和部署漏洞。
@emanueldev4 · X
Building Preflight a simple way to catch issues before your website goes live.
扫描GitHub代码库查找安全漏洞，AI提供修复建议。
u/uwais_ish · Reddit
Three weeks to build an AI security scanner. The scanner was the easy part. Shipped RedFlag this month. Breakdown of where the time actually went, because it was nothing like I estimated. Stack: Next.js 16, Auth.js v5, MongoDB, Stripe, OpenAI. Deployed on Vercel. What I thought would be hard: getting an LLM to find real vulnerabilities. What was actually hard: getting it to stop finding fake ones. First working version flagged 200+ issues on a clean repo. Every one plausible, most of th
AI 应用安全扫描器，检测泄露的密钥和配置问题。
thfothijn · Product Hunt
OpzyAI Finds what your AI-built app leaks — your editor fixes it
上传简历生成证据型作品集，获得AI评分和改进建议。
@Showcase_app1 · X
Building Showcase: turns a résumé into an editable, evidence-first portfolio for final-year students and new grads. AI-assisted drafts, nothing invented. Build/edit/preview free; live publishing is Pro.
用AI审计网站UI/UX设计,优化可用性、无障碍和转化率。
@mydesignaudit · X
监控 AI 工具的状态和元数据问题信号
dobby_lee · HN
Open-Source for AI tool status checker
Tako 在 60 秒内扫描网页应用，发现泄露密钥和开放数据库等安全漏洞。
@guhanvenkaty · X
Tako's free preview is live: Paste your URL, get a security verdict on your vibe-coded app in 60s. Not an AI wrapper: it fires real requests and PROVES exposure, pulling data from your open DB with no login. Scanning puts you on the early-access waitlist.
Run a $9 AI visibility audit across OpenAI, Claude, Gemini, and Grok. See where AI overlooks your brand, who appears instead, and what to fix next.
@kylekane · X
由AI驱动的代码审查工具，在microVM中运行代码来捕获更多错误。
u/dumbfoundded · Reddit
Ito, AI Code Review that Runs Code I've been using AI code review tools but none of them actually run code so I built one: https://www.ito.ai/ The way it works is that it uses microVMs to spin up your environment with all of the services running. Then a bunch of AI agents go and test the application to collect runtime evidence. The result is you get test cases along with evidence about whether or not the test cases pass or fail. The runtime evidence can be videos, request/response curls, db
