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Alternatives

Products that do what Detect and Deny (D2) does

Simple Deterministic Guardrails for LLM/Agent

  1. 1
    Venn.ai337

    Delegate real work to AI agents with safety guardrails

    Mar 2026 · venn.ai

  2. 2

    Configurable safety control for enterprise agent deployment.

    Apr 2026 · elevenlabs.io

  3. 3WP

    Anthropic and OpenAI's publicly available models are explicitly guard-railed so that they refuse offensive tasks. And their cyber-focussed models are gated for enterprises. This leaves SMEs and mid market open to major vulnerabilities. AI can be used as both an adversarial and defensive tool in the world of cyber. A worst case outcome is if only the adversaries have access. Meanwhile, most existing AI cyber tools are just wrappers. The problem is that they still have all the guardrails on from the foundation model where they will inherit its refusals. For this project we've post-trained a…

    Jun 2026 · argusred.com

  4. 4FL

    Hi HN! We just launched Codacy Guardrails, an IDE extension with a CLI for code analysis and MCP server that enforces security & quality rules on AI-generated code in real-time. It hooks into AI coding assistants (like VS Code Agent Mode, Cursor, Windsurf), silently scanning and fixing AI-suggested code that has vulnerabilities or violates your coding standards, while the code it’s being generated. We built this because coding agents can be a double-edged sword. They do boost productivity, but can easily introduce insecure or non-compliant code. One recent research team at NYU found that 40%…

    2025

  5. 5
    Verdic1

    Deterministic Guardrails for AI Systems

    Jan 2026

  6. 6

    Stop AI agents from installing malicious packages.

    Jul 2026 · agentinel.habitwala.in

  7. 7
    Avery16

    Create a deterministic agent that runs on your hardware

    Jul 2026 · avery.software

  8. 8LC

    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

  9. 9AC

    Hello, looking for some users interested using a devtool that allows developers to centrally manage AI Coding Agent tools that supports all AI Coding Agent tools like Claude Code, Codex, Antigravity, etc. Try it free! https://www.producthunt.com/products/sigma-shake-governance-...

    Apr 2026 · sigmashake.com

  10. 10OS

    We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don't think like you. So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly. Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use…

    Mar 2026 · github.com

  11. 11IB

    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

  12. 12OS

    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

  13. 13

    The TLS for autonomous agent state.

    Jul 2026 · memora.optitransfer.ch

  14. 14

    Full visibility into your AI spend, before it goes rogue

    26d ago · neatproxy.com

  15. 15WI

    At Laminar (https://github.com/lmnr-ai/lmnr) we're building open source AI observability platform in Rust. We obsess over instrumentation DX for our Python and TS SDKs and in this new blog we outline how we made the most seamless way of instrumenting recently released claude agent sdk

    Dec 2025 · laminar.sh

  16. 16CA

    TL;DR: we built a framework-agnostic agent runtime that uses gVisor for isolation and runs on k8s. It’s open-source under AGPLv3 Recently we’ve been working on a customer support “AI assistant” - essentially an interactive knowledge base/L1 support but with an option to touch resources that belong to a customer it’s talking to. We found existing tools to be lacking in these aspects: 1. Fully intercepted i/o. We wanted to trace out LLM calls as well as any other networking calls attempted by the harness so that guardrails and audit trails apply to all current and future systems…

    Jul 2026 · github.com

  17. 17RV

    Cryptographically secure and verifiably robust protection with drop-in integration and outcome-based pricing

    May 2026 · retroguard.ai

  18. 18

    Guard+Memory+Eval—reliable, auditable, self-improving Agents

    Jun 2026 · github.com

  19. 19

    Cryptographic guardrails for AI Agents

    Mar 2026 · docs.icme.io

  20. 20

    7 layers between your AI agents and disaster.

    Feb 2026 · takeinterest.ai

  21. 21

    Deterministic offline release evidence for AI agents

    Jul 2026 · iisacc-justmoong.github.io

  22. 22LA

    We combined Stanford's ACE (agents learning from execution feedback) with the Reflective Language Model pattern. Instead of reading traces in a single pass, an LLM writes and runs Python in a sandbox to programmatically explore them - finding cross-trace patterns that single-pass analysis misses. The framework achieved 2x consistency improvement on τ2-bench.

    Mar 2026 · github.com

  23. 23

    AI agent spend firewall

    26d ago · agentshield.fly.dev

  24. 24

    Real-time guardrails for agentic AI and enterprise workflows

    Jul 2026 · lineation.ai

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