Alternatives
Products that do what TEOS Sovereign Sentinel does
ALLOW / WARN / BLOCK every AI-generated command
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At Deno we've been using OpenClaw and other agents increasingly for addressing production problems in Deno Deploy - when a PagerDuty alert fires, the agent starts researching the cause and making fixes. In order to do this, the agent needs access to real production systems - postgres, kubernetes, gcp, clickhouse, github, etc. But this is dangerous to say the least - we want destructive actions to be reviewed by other LLMs, approved by humans, and logged appropriately. Claw Patrol terminates TCP connections over WireGuard or Tailscale, then parses application protocols (eg http, postgres,…
Jun 2026 · github.com
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- 4AO
This is a small PoC Python project for web server access logs analyzing to classify and dynamically block bad bots, such as L7 (application-level) DDoS bots, web scrappers and so on. We'll be happy to gather initial feedback on usability and features, especialy from people having good or bad experience wit bots. *Requirements* The analyzer relies on 3 Tempesta FW specific features which you still can get with other HTTP servers or accelerators: 1. JA5 client fingerprinting (https://tempesta-tech.com/knowledge-base/Traffic-Filtering-b...). This is a HTTP and TLS layers…
Oct 2025 · github.com
- 5IB
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
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Prompt instructions like 'never do X' don't hold up in production. LLMs ignore them when context gets long or users push hard. Limits sits between your agent and the real world. Every action — database writes, API calls, refunds — gets intercepted and checked against your rules before it executes. Deterministically. No LLM involved in enforcement. Three modes: Conditions: hard rules on structured data Guideance: validate LLM output before it reaches the user and give the agent chance to reason and retry Guardrails: scan for PII, toxicity, prompt injection etc One line to integrate: npm…
Feb 2026 · limits.dev
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An autonomous agent you can hand a shell to. Every tool call passes a gate that grants authority for exact arguments, once, for 30 seconds — and logs the verdict.
9d ago · talos-agent.ch
- 10OS
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…
Oct 2025 · archestra.ai
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- 12AT
In light of recent news about an agent deleting a production database, I thought now would be a good time to share this. As the use of AI tools in production is becoming more common, sadly so will the high profile incidents like the one mentioned. Fewshell is a terminal agent specifically designed to avoid this. There is no setting to enable command auto-approval. This is by-design, so that the user never has to second-guess or worry about accidentally having it enabled. Originally my intention was to build an AI mobile terminal to make typing shell commands easy. But with so many…
Apr 2026 · github.com
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- 15IB
The only way to go fast is full YOLO mode in your coding agent. I've got the local sandbox figured out (pro tip: Incus VMs work great) but I wanted to keep my agents from doing things like inadvertently blowing up my cloud services or chasing a prompt to POST to some random website. I struggle most with this on my side projects where my permission model isn't quite as robust as it is at the office. I started with a firewall on the Incus container but every time the agent needed access to something new, I was poking more holes in it - and it didn't differentiate between HTTP verbs. I've been…
Jul 2026 · trollbridge.dev
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Block bad command execution and keep you away from danger.
May 2026 · github.com
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Block malicious web content before it reaches your AI.
Jan 2026
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A local-first Mac security auditor that roasts your code.
Jun 2026 · accounts.google.com
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Block dangerous AI agent actions before execution
Jul 2026 · relay-security-lemon.vercel.app
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Immutable deterministic security architecture for AI systems
Apr 2026 · sovereign-shield.net
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