prismor

The Problem

AI agents execute shell commands, read and write files, access credentials, and call external APIs. They do this autonomously, often across many steps, with limited checkpoints.

This creates risks that traditional security tooling isn't designed for:

• Prompt injection - malicious content in a file, issue, or web page can redirect the agent mid-task
• Unintended destructive actions - an agent misinterprets an instruction and runs something irreversible
• Secret exfiltration - an agent reads .env or credential files as part of a debugging task and sends the content outbound
• Lack of visibility and identity - an agent can spawn subagents and lack complete visibility for the end user
• Privilege escalation - an agent modifies sudoers, CI pipelines, or file permissions to resolve a permission error
• Dependency manipulation - an agent installs or rewrites a package at the direction of injected input
• Supply chain risk - an agent installs a vulnerable or 0-day package while optimizing for code velocity

Standard OS-level and endpoint security tools monitor the kernel and filesystem. But they lack the context to make AI usable

Quick Start (30s)

pip install prismor
prismor setup

For the Skill, curl, and git-clone alternatives, plus PEP 668 systems and secret-cloaking setup, see the full installation guide.

Capabilities

• 🛡️ Prismor covers the policy engine, session logs, security audit, and CLI reference
• 📦 Supply Chain covers install-time enforcement, IOC matching, and risk scoring
• 🛜 Network Isolation covers policy-driven egress control, raw IP detection, and tunnel blocking
• 🔍 Skill Scanner covers MCP server and skill risk scanning across supported agents
• 🚦 MCP Guardrails let you block a specific MCP server or tool, or require human approval before the agent calls it, with a policy rule you write yourself
• 🛰️ MCP Gateway is a single MCP connector that fronts every other MCP server you use — each tools/call is policy-evaluated before it forwards and each response is injection-scanned before the model sees it, so a poisoned tool result never becomes context. prismor mcp-gateway install moves an existing .mcp.json behind it
• 🛤️ LLM Proxy governs an agent Prismor cannot hook, through the one thing every agent has: its model traffic. Point it at prismor proxy with ANTHROPIC_BASE_URL, OPENAI_BASE_URL, or the Google Gen AI SDK's HttpOptions(base_url=...) and nothing else about the agent changes. Every tool call the model proposes — Anthropic tool_use, OpenAI tool_calls, Gemini functionCall — is reshaped into the same event a Bash hook produces and judged by the same rule, with streamed calls held until they can be judged
• Claude Inference Hooks makes Prismor the AI security server behind Claude Enterprise: Anthropic sends every governed prompt from claude.ai, Claude Code and Cowork to prismor inference-hook serve, which runs your policy on the transcript and answers allow/deny before the model runs — signed requests (Standard Webhooks), fail-closed, shadow mode, nothing to install on user devices. prismor inference-hook test sends signed sample frames to check the wiring
• 🏷️ Tool Tags classify tools by capability (read, write, network, exec) so a rule can say "nothing that reads private data may also reach the network" instead of naming every tool one by one — MCP tools self-declare via _meta, and prismor tags lists, tests, and lints the rule expressions
• 🔐 Sweep and Cloak covers secret prevention at tool boundaries, practical setup, best practices, threat model, and cleanup for leaked secrets
• 🦞 OpenClaw Integration covers runtime hooks, prompt-injection scanning, and project or user-scope setup for OpenClaw
• 🤖 Hermes Agent Cloaking covers Hermes-specific secret cloaking with pip entry-point auto-discovery, filesystem install, and pre_gateway_dispatch paste guard
• ☁️ Hosted Agent VMs governs agents that run on a machine you never log into (Claude Code on the web, Cursor cloud agents, Copilot coding agent, OpenHands Cloud, Codex cloud, Replicas): the platform's setup script installs the binary, and install-hooks --portable writes a hook config that is safe to commit
• 🧠 Semantic Guard: opt-in hybrid layer that adds an LLM-assisted intent check for paraphrased prompt-injection attempts the regex rules cannot catch
• 🪤 Canary plants honeytoken credential files that trip a CRITICAL finding the moment an agent reads them, catching recon behavior
• 🪪 IAM gives each agent a named identity and least-privilege permission profile when several agents share a workspace
• 🧩 Framework Agents guards production agents (OpenAI Agents SDK, LangChain/LangGraph in Python and JS, CrewAI, browser-use, Pydantic AI, AutoGen Core, Agno, Semantic Kernel, Google ADK, BeeAI, Claude Agent SDK, Vercel AI SDK, Mastra) with one call — wrap each request in use_subject("user:alice") and a multi-tenant agent gets per-user attribution, per-user IAM profiles, and per-user suspension
• 🎯 Scoped Agent synthesizes minimal, task-specific rules per session so an injected pivot off-task gets blocked
• 🧬 Learning mines session history to propose new rules, flag false positives, and detect evasion
• ⚖️ Layered Policy & Exemptions covers per-rule observe/enforce, the non-overridable floor, and admin-granted, time-boxed exemptions across org / project / repo layers
• 🔐 Explicit enforce selection & the unlock window — enforce setup starts with nothing blocking and you pick the set; prismor allow makes narrow exceptions, and prismor unlock opens a password-gated, time-boxed window in which an agent may edit policy (never the self-protection rules that guard Prismor itself)
• 📡 Live Telemetry covers the optional enterprise control-plane link — device enrollment, signed remote policy, and redacted telemetry streamed to a self-hosted org dashboard