Prompt-injection firewall for AI agents
Block malicious web content before it reaches your AI.
Prompt-injection firewall for AI agents launched on January 3, 2026 with 3 votes, #5,632 of 14,584 launches that month and more than 50% of that year's launches.
What it does
Hey Product Hunt! 👋 Instead of relying on clever prompts (which can be bypassed), SafeBrowse enforces a hard security boundary between untrusted content and your LLMs. Key features: • 50+ prompt injection patterns detected • Policy engine for login/payment forms • RAG sanitization for vector DBs • Python SDK with sync/async support • Fail-closed: security cannot be bypassed We'd love your feedback! What security challenges are you facing with AI agents?
Does a similar job
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Open-Source Gateway to Stop Tool-Abusing Prompt Injections2025 · archestra.ai · ▲9Hi HN, Matvey, Ildar, Joey, and Dominik here. If you're building LLM agents that use tools, you're probably worried about prompt injection attacks that can hijack those tools. We were too, and found that solutions like prompt-based filtering or secondary "guard" LLMs can be unreliable. Our thesis is that agent security should be handled at the network level between the agent and the LLM, just like a traditional web application firewall. So we built Archestra Platform: an open-source gateway that acts as a secure proxy for your AI agents. It's designed to be a deterministic firewall against…
Agent Browser ShieldJun 2026 · github.com · ▲109I've been experimenting with Claude Code, ChatGPT Agent, and OpenClaw to perform more open-ended tasks for me online. A big blocker I've hit on shopping and research tasks is the agent getting a key piece of info wrong. For example, in one case, my agent decided to add a brand I don't like to the cart because the site flagged it as almost sold out The HN crowd is probably pretty aware of the threats and can avoid them while browsing. But what about their agents? I tried prompting, but it was ineffective, because once the AI saw the threat, it polluted/distracted its context Looking at…
I built a firewall for agents because prompt engineering isn't securityJan 2026 · github.com · ▲7Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…
LLMs can be susceptible to a new kind of malware2023 · github.com · ▲17New ways of breaking app-integrated LLMs . Contribute to greshake/llm-security development by creating an account on GitHub.
PromptGuardDec 2025 · promptguard.co · ▲3Every other gateway is detect-and-block. You don't get a security grade. You don't get red team in CI. We built the one that does. PromptGuard is a proxy: change your base URL (one line with our SDK, or a few lines without), and every request gets checked for prompt injection, PII leaks, jailbreaks, and abuse. 20+ threat vectors, 39+ PII types. We also ship a red team suite and an autonomous agent that mutates attacks to find bypasses - you get a grade (A-F) and a GitHub Action that runs these tests on every PR. So you see where you're weak before you ship. Works with OpenAI, Anthropic,…
FireClaw – Open-source proxy defending AI agents from prompt injectionMar 2026 · github.com · ▲5Hey HN, We built FireClaw because we kept watching AI agents get owned by prompt injection through web content. The agent fetches a page, the page says "ignore previous instructions," and suddenly your agent is leaking data or running commands it shouldn't. The existing solutions detect injection after the fact. We wanted to prevent it. FireClaw is a security proxy that sits between your AI agent and the web. Every fetch passes through a 4-stage pipeline: 1. DNS blocklist check (URLhaus, PhishTank, community feed) 2. Structural sanitization (strip hidden CSS, zero-width Unicode, encoding…
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