AgentShield
Prompt injection detection API for AI agents
What it does
AgentShield is a prompt injection classifier that sits between untrusted input and your AI agent. One API call classifies any text — user messages, RAG documents, tool outputs — and returns a verdict before it reaches the model. Think of it as a WAF for LLMs. Why we built it: Johns Hopkins researchers hijacked Claude Code, Gemini CLI, and GitHub Copilot through prompt injection. The three biggest AI companies couldn't stop it. We built an external security layer that does.
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Agent Browser ShieldJun 2026 · github.com · ▲109Block prompt inject & cut token costs for AI browser agents
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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…
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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