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Products that do what NervePay does
Give AI agents identity, secrets vault & analytics
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AI agents are starting to get real access like GitHub tokens, cloud credentials, customer data, deploy permissions. Not coincidentally, the rate of major cybersecurity incidents is rising rapidly. See for yourself: https://epoch.ai/data/cve?view=graph https://genai.owasp.org/resource/state-of-agentic-ai-securit... My friend and I, both AI researchers, are working on fixing this through an open-source project we've just started called Clay Seal. We've started with a simple version of Identity: an open-source way to issue short-lived, verifiable…
Jul 2026 · github.com
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Hey HN, I’m Chris, a solo dev in Melbourne AU. For the past month I've been spending my after work hours building AgentVisa. I'm both excited (and admittedly nervous) to be sharing it with you all today. I've been spending a lot of time thinking about the future of AI agents and the more I experimented, the more I realized I was building on a fragile foundation. How do we build trust into these systems? How do we know what our agents are doing, and who gave them permission? My long-term vision is to give developers an "Agent Atlas" - a clear map of their agentic workforce, showing where…
2025 · agentvisa.dev
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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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We built PrivateClaw because the hosted OpenClaw platforms on the market today require you to trust them with plaintext. PrivateClaw removes that requirement at the hardware layer. PrivateClaw runs AI agents inside Trusted Execution Environments (TEEs), backed by AMD’s SEV-SNP standard. This means that your data is encrypted at the hardware level, enforced by the AMD Secure Processor outside the host OS trust boundary. PrivateClaw comes with inference that also runs inside TEEs, which means your prompts and completions are private as well. How it works: Each user gets a dedicated CVM…
Apr 2026 · privateclaw.dev
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Every AI agent that does something useful - opening a PR, posting in Slack, updating a ticket - needs to call an API on behalf of a user. That means OAuth. Authorization flows, token storage, refresh logic, per-user credential isolation. Today, most teams solve this with a long-lived API key in an .env file, shared across every user and every session. As everyone in an organization becomes a software engineer - whether they know it or not - you can't expect each of them to roll their own OAuth. Secure credential handling needs to be baked into the infrastructure. The core issue: agents are a…
Mar 2026 · kontext.dev
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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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