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A Prompt-Injection Firewall for AI Agents and RAG Pipelines

Hacker News

A Prompt-Injection Firewall for AI Agents and RAG Pipelines

We built SafeBrowse — an open-source prompt-injection firewall for AI systems. Instead of relying on better prompts, SafeBrowse enforces a hard security boundary between untrusted web content and LLMs. It blocks hidden instructions, policy violations, and poisoned data before the AI ever sees it. Features: • Prompt injection detection (50+ patterns) • Policy engine (login/payment blocking) • Fail-closed by design • Audit logs & request IDs • Python SDK (sync + async) • RAG sanitization PyPI: pip install safebrowse Looking for feedback from AI infra, security, and agent builders.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, open · Missing: mac, macos, cursor
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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: builder · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released
28%28% 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
25%25% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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