SiteIQ – LLM and Web security testing tool (built by a high schooler)

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Hi HN! I'm an 11th grade student learning cybersecurity and web development. I built SiteIQ as a hands-on way to understand security vulnerabilities, SEO, and how to test them. I used AI as my coding partner throughout this project – it helped me understand concepts, debug issues, and write code. Building with AI felt like having a patient tutor available 24/7. I learned way more than I would have just following tutorials. What it does: - Security Testing: OWASP Top 10 (SQL injection, XSS, CSRF, etc.) - SEO Analysis: Meta tags, schema markup, Core Web Vitals - GEO Testing: Multi-region accessibility and latency - LLM Security: Prompt injection, jailbreaking, system prompt leakage, and "Denial of Wallet" attacks The LLM security part was the most interesting to build. With everyone adding AI to their apps, I wanted to understand how prompt injection actually works and how to test for it. Features: - Web UI with real-time console output - CLI for automation - Self-hosted (no data leaves your machine) Tech: Python, Flask, pytest GitHub: https://github.com/sastrophy/siteiq I'd love feedback – are there vulnerabilities I'm missing? Any suggestions for the LLM attack payloads? This is my first open source project, so any advice is welcome!

Visit product View on Hacker NewsNov 28, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, apps, coding · Missing: agents, macos, agent

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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