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Built firewall for LLMs after prompt injection bypass GPT-4s guardrails

Hacker News

Built firewall for LLMs after prompt injection bypass GPT-4s guardrails

After watching prompt injection attacks bypass every major LLM's safety systems, I built TrustLayer. It's a simple API that: 1. Scans prompts for injection attacks in real-time (heuristic + OpenAI moderation) 2. Detects when agents drift from their intended behavior 3. Has a kill switch for production incidents Built for production use. Free tier available. Open source docs and examples. The problem: Prompt injection is still breaking LLM apps in production. Most guardrails can be bypassed with simple tricks. The solution: API-first security layer that sits between your app and the LLM.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, apps · Missing: mac, macos, cursor
90%90% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal
57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% 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, ide, io · Missing: https docs, excited, just released
36%36% 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
19%19% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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