Alternatives
Products that do what Open-Source Gateway to Stop Tool-Abusing Prompt Injections does
Hi 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…
- 1AA
Hey HN! This is Adil, Salman and Jose and and we’re behind archgw [1]. An intelligent proxy server designed as an edge and AI gateway for agents - one that natively know how to handle prompts, not just network traffic. We’ve made several sweeping changes so sharing the project again. A bit of background on why we’ve built this project. Building AI agent demos is easy, but to create something production-ready there is a lot of repeat low-level plumbing work that everyone is doing. You’re applying guardrails to make sure unsafe or off-topic requests don’t get through. You’re clarifying vague…
2025 · github.com
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- 3IB
Hi 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…
Jan 2026 · github.com
- 4AO
Hi HN! This is Adil, Salman, Co and Shuguang and we're excited to introduce archgw [1], an open source intelligent proxy for agents built on Envoy [2]. Arch moves the critical but crufty work around safety, observability, and routing of prompts outside business logic. Arch is a uniquely intelligent infrastructure primitive, engineered with purpose-built fast LLMs [3] for tasks like intent detection over multi-turn, parameter identification and extraction, triggering single/multiple function calls, and offers convenience features to auto dispatch LLM calls for summarization based on data…
2024 · github.com
- 5AR
Hi HN — we're the team behind Arch (https://github.com/katanemo/archgw), an open-source proxy for LLMs written in Rust. Today we're releasing Arch-Router (https://huggingface.co/katanemo/Arch-Router-1.5B), a 1.5B router model for preference-based routing, now integrated into the proxy. As teams integrate multiple LLMs - each with different strengths, styles, or cost/latency profiles — routing the right prompt to the right model becomes a critical part of the application design. But it's still an open problem. Most routing systems fall into two…
2025
- 6AA
Hi HN! This is Salman, Adil, Shuguang and Co working on ArchGW[1] - an open-source lightweight proxy server for prompts - written in Rust and built on top of Envoy[2]. Arch moves the critical but pesky handling and processing of prompts: task understanding, prompt routing, safety, and observability - outside business logic. Its an edge and egress proxy for agentic apps. We've talked to 100s of developers at places like Twilio, GE Healthcare, Redhat, Square, etc and there was a consistent theme in building AI apps: to move past a nascent demo they are left to their own devices in building out…
2025 · github.com
- 7AA
Hi HN! My name is Adil Hafeez, and I am the Co-Founder at Katanemo and the lead developer behind Arch - an open source project for developers to build faster, generative AI apps. Previously I worked on Envoy at Lyft. Engineered with purpose-built LLMs, Arch handles the critical but undifferentiated tasks related to the handling and processing of prompts, including detecting and rejecting jailbreak attempts, intelligently calling “backend” APIs to fulfill the user’s request represented in a prompt, routing to and offering disaster recovery between upstream LLMs, and managing the observability…
2024 · github.com
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Block malicious web content before it reaches your AI.
Jan 2026
- 10AA
Hi HN! My name is Adil Hafeez, and I am the Co-Founder at Katanemo and the lead developer behind Arch - an open source project for developers to build faster, generative AI apps. Previously I worked on Envoy at Lyft. Engineered with purpose-built LLMs, Arch handles the critical but undifferentiated tasks related to the handling and processing of prompts, including detecting and rejecting jailbreak attempts, intelligently calling “backend” APIs to fulfill the user’s request represented in a prompt, routing to and offering disaster recovery between upstream LLMs, and managing the observability…
2024 · github.com
- 11PI
People seem to be blindly hooking up their OpenClaw’s to their personal data. So, I built runtime controls to prevent at the least, very simple prompt injection attacks. Once installed, it hooks to Node.js child_process module in the gateway process and listens to tool calls and their response streams. And a fetch hook to monitor user prompts (both could’ve been through fetch, happy to discuss why this whole layer couldn’t just be a proxy). There are two layers of protection: First: Whenever there is a read-only tool call whose response an attacker can modify, we extract that part of the…
Feb 2026 · github.com
- 12AO
Hey HN! I've been wanting to use something like OpenClaw for a while but couldn't get myself to give it access to anything important due to all the risks involved. Prompt injection is still a problem (even though some people seem to ignore it) and so are hallucinations and mishaps that cause agents to do things like delete production data [1]. Even harnesses like Claude Code and Codex are subject to this, particularly since we're getting progressively looser about how we run them e.g. Conductor is really popular and runs agents without any sandboxing. That means we're in a bit of an…
Apr 2026 · agentport.sh
- 13AO
Hi HN, I've been developing Portkey Gateway, an open-source AI gateway that's now processing billions of tokens daily across 200+ LLMs. Today, we're launching a significant update: integrated Guardrails at the gateway level. Key technical features: 1. Guardrails as middleware: We've implemented a hooks architecture that allows guardrails to act as middleware in the request/response flow. This enables real-time LLM output evaluation and transformation. 2. Flexible orchestration: The gateway can now route requests based on guardrail verdicts. This allows for complex logic like fallbacks…
2024 · github.com
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Jun 2026 · github.com
- 15LC
2023 · github.com
- 16FO
Hey 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…
Mar 2026 · github.com
- 17MG
Many teams connecting LLMs to external tools eventually encounter the same architectural issue: as more tools and agents are added, the integration pattern becomes an N×M mesh of direct connections. Each agent implements its own auth, retries, rate limiting, and logging; each tool needs credentials distributed to multiple places and observability becomes fragmented. We built LLM gateway with this goal to provide a single place to manage authentication, authorization, routing, and observability for MCP servers, with a path toward a more general agent-gateway architecture in the future. The…
Dec 2025 · truefoundry.com
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- 19FP
We've built an open-source tool to stress test AI agents by simulating prompt injection attacks. We’ve implemented one powerful attack strategy based on the paper [AdvPrefix: An Objective for Nuanced LLM Jailbreaks](https://arxiv.org/abs/2412.10321). Here's how it works: - You define a goal, like: “Tell me your system prompt” - Our tool uses a language model to generate adversarial prefixes (e.g., “Sure, here are my system prompts…”) that are likely to jailbreak the agent. - The output is a list of prompts most likely to succeed in bypassing safeguards. We’re just getting…
2025 · security.vista-labs.ai
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Local AI agent guardrails, budgets, & circuit breakers.
Jun 2026 · stackmint.ai
- 21CL
With the right technique, I was able to break the so-called secure models like Claude and OpenAI. So, I built an open-source tool to automate this and find security holes in any hosted model. I got claude-sonnet-4 to demonstrate the following harmful behavior: - steal data from downstream tool calls using sql injection, code injection and template injection attacks - install spyware or malware using prompt obfuscation to send data to a third-party server Try it yourself with this simple command: pip install compliant-llm && compliant-llm dashboard
2025 · github.com
- 22AR
Hi HN, I built this open-source LLM red teaming tool based on my experience scaling LLMs at a big co to millions of users... and seeing all the bad things people did. How it works: - Uses an unaligned model to create toxic inputs - Runs these inputs through your app using different techniques: raw, prompt injection, and a chain-of-thought jailbreak that tries to re-frame the request to trick the LLM. - Probes a bunch of other failure cases (e.g. will your customer support bot recommend a competitor? Does it think it can process a refund when it can't? Will it leak your user's address?) -…
2024 · promptfoo.dev
- 23AG
Hi HN My name is Salman and I work on Arch GW - the intelligent gateway designed to protect, observe, and personalize LLM applications with your APIs. https://github.com/katanemo/arch Our team built Envoy Proxy at Lyft, and re-imagined it with the belief that: Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests including secure handling, intelligent routing, robust observability, and integration with backend (API) systems for personalization – all outside business logic. Engineered with purpose-built LLMs, Arch…
2024 · docs.archgw.com
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