MoorAI vs Miggo Security
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Miggo watches what an application does at runtime. MoorAI reads what an AI agent’s action contains. Miggo is application detection and response (ADR) built around exploitable vulnerabilities: “Mitigate Now. Patch When You're Ready.” (miggo.io) Its sensor sits inside production: “The Miggo Kubernetes Sensor is a lightweight, eBPF-powered and OpenTelemetry Collector based agent that unlocks deep visibility into both your infrastructure and application behavior.” (miggo.io blog, Kubernetes Sensor) MoorAI is runtime security for AI agents on endpoints and AI servers. For agents that run as production services (MoorAI for AI Servers) it is server mode of its hook, an in-process SDK, a localhost sidecar, an MCP gateway and a model proxy; on employee endpoints it is a PreToolUse hook inside coding agents and an MCP stdio proxy. It reads the prompt, the file read into context, the shell command and the Model Context Protocol (MCP) arguments and results, and coaches, masks or blocks on what it finds.
They meet at one place: AI agents running in production. Since March 2026 Miggo extends its runtime view to them: “Miggo monitors AI applications from the inside out, tracing model loading, tool invocation, file access, network behavior, and system actions across agents and MCP toolchains.” (miggo.io, Agentic Detection & Response) It judges an agent by behaviour, and says so: “We don't read the chat; we see the functions and traces.” (miggo.io blog, Miggo Security Extends Runtime Defense for AI) MoorAI judges each tool call before it runs, below the agent’s decision and above the operating system. In production that is server mode, @moorai/agent-sdk, the moorai-serve sidecar, the MCP gateway and the model proxy, judging a call by its content, a high-impact call by the person’s task (task-bound tool calls) and a service by its declared workload profile, against a 85-threat matrix, and by default sends only category · risk · keyed one-way hash (plus the login and hostname, which the console pseudonymises on arrival).
Miggo covers far more application-security ground than MoorAI does; on AI agents, the two overlap. Runtime vulnerability prioritization, exploit blocking inside the application, CVE-specific rules written for the customer’s own WAF and sold as managed rule sets in AWS WAF, and a vulnerability intelligence feed are Miggo’s core, and MoorAI does none of them. AI agents are MoorAI’s whole job, in production services and on employee endpoints, and Miggo’s public material does not describe the coding agents on employee machines. The rows where Miggo covers more ground are further down.
Three things this page does not state. (1) Whether Miggo reads prompt text. Its AI platform page says “AI Detection and Response (AIDR) platforms monitor how AI and agents operate inside applications, including prompts and model interactions.” (miggo.io, AI Runtime Defense Platform) Its launch post says it does not read the chat. The tables follow the launch post’s description of the mechanism and mark prompt content unconfirmed. (2) What the product documentation says. docs.miggo.io asks for a login, so the product documentation was not read. (3) Platform pricing. Miggo’s pages lead to a demo; only the AWS WAF rule sets carry a public price model. Every Miggo statement below is quoted from Miggo’s own site, and the page is named.
The core difference is the layer: the application and the operating system underneath an agent, or the agent’s own tool calls.
@moorai/agent-sdk in process, the moorai-serve sidecar, moorai-mcp-gateway in front of remote MCP servers, or moorai-model-proxy between the agent and its model API. It sees the call the agent is about to make, with its arguments, before it runs.Where MoorAI holds ground Miggo does not
These rows follow from sitting at the agent’s tool-call boundary, in a production service or inside a coding agent, and reading the content of each action, with nothing sent anywhere to decide. Most Miggo marks here are unconfirmed, because its product documentation is behind a login.
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| MoorAI | Miggo | |
|---|---|---|
| Coding agents on developer machines (Claude Code, Codex, Copilot CLI, Gemini CLI, Cursor, Factory Droid) | ✓the agents' own hooks | —not in public material; production applications only |
| Scans the content of prompts, files read, shell commands, MCP arguments and results, and output | ✓on-device threat matrix | —"functions and traces", not the chat |
| Blocks a secret at the file read, before it enters the model’s context | ✓hooks the agent's file-reading tools | — |
| Masks a secret or PII span and lets the call proceed | ✓mask action | — |
| Coaching with the safer alternative, shown to the developer and the agent | ✓ | — |
| Flags a risky action aimed at something the user’s request never named | ✓intent alignment; lexical, keyed hashes | — |
| Scans a model file without loading it | ✓moorai-model-scan: pickle, Keras, TF, ONNX, GGUF, SafeTensors | —catches malicious model code when it runs |
| By default only category · risk · keyed one-way hash (plus login and hostname, pseudonymised on arrival) leave the device | ✓capture tiers are opt-in, admin-enabled | —collects traces and telemetry; contents not public |
| Open source, so the data claim can be checked in code | ✓agent MIT | —Helm charts, Terraform integrations and an MCP server are public; no sensor or platform source found |
| Public product documentation, no demo call | ✓ | ✗docs.miggo.io asks for a login |
| Free to start and to enforce, without talking to sales | ✓agent free; console free self-hosted, no user limit | ✗platform demo-led; AWS WAF rule sets paid per region and per million requests |
Where Miggo covers ground MoorAI does not
This list is longer. Miggo is an application security platform for every first- and third-party application in production, with AI applications as one part of it. MoorAI governs what AI agents do, and knows nothing about the application code they run next to.
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| MoorAI | Miggo | |
|---|---|---|
| Detection and response for attacks on any production application, from function-level behaviour | ✗ | ✓"Covering the entire OWASP Top 10 through real-time detection." |
| Blocks exploitation inside the application, such as template injection or unsafe deserialization, before a CVE exists | ✗ | ✓in-app sensor; EPERM at the syscall |
| Runtime vulnerability prioritization: is the vulnerable function loaded, reachable and exposed to the internet | ✗ | ✓Miggo Prove |
| Writes CVE-specific rules for the customer’s existing WAF | ✗ | ✓WAF Copilot / Miggo Shield |
| Managed rule sets in AWS WAF, including CVEs in AI frameworks, LLM gateways and model servers | ✗ | ✓AI/ML Application Protection package |
| Vulnerability intelligence feed tracking newly disclosed and undisclosed threats | ✗ | ✓Miggo Pulse |
| Catches a malicious model file when its code runs in production | ◐static scan before load, on the device; not at runtime | ✓ |
| AI bill of materials for production AI applications, built from what actually executes | ◐on developer devices; does not apply in server mode | ✓ |
| Internet exposure of AI services in the customer’s environment | ◐local model servers reachable on this device only | ✓ |
| Agentless deployment from traces already in the customer’s observability stack | ✗a hook, SDK or sidecar beside the agent | ✓including Grafana Cloud traces and profiles |
Same capability, different mechanism
Both products do each of these for AI agents, so the table describes how rather than scoring.
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| MoorAI does it by… | Miggo does it by… | |
|---|---|---|
| Watching an agent’s tool calls in production | Judging each call before it runs, in process through @moorai/agent-sdk, through the moorai-serve sidecar, at moorai-mcp-gateway or in server mode’s hook, against the threat matrix and the signed console policy. The arguments are read on the machine; the console gets which rule fired, the risk and a keyed hash. |
“Execution-Level Detection for AI Agents: Traces tool calls, model and artifact loading, system actions, file access, and network behavior to pinpoint agent-driven compromise paths.” and “MCP-Aware Monitoring for Toolchains: Monitors MCP-mediated tool use to detect abnormal access, risky chaining patterns, and high-impact execution paths.” (miggo.io blog, Miggo Security Extends Runtime Defense for AI) |
| Prompt injection | Scanning the prompt and what comes back into the agent (command output, MCP results, sub-agent reports, fetched pages) for injected instructions where the agent runs, on the device or in the service, and raising scrutiny on the actions that follow an outside read. | By the execution it causes: “The new engine distinguishes between an Agent using a tool legitimately and an Agent that has been hijacked via prompt injection.” The same post bounds it to “Prompt Injection from various inputs: When it actually changes code execution.” (miggo.io blog, Miggo Security Extends Runtime Defense for AI) |
| Noticing that an agent’s behaviour changed | Declared workload profiles (the tools, MCP servers and hosts a service is expected to use; anything else is reported as PROFILE_DRIFT or denied), an entitlement envelope per agent, and alerts or blocks when an approved MCP tool’s description or schema changes. |
“Behavioral Drift Detection: Baselines agent behavior and highlights changes over time with full security context.” and “Runtime Guardrails: Allows security teams to enforce allowed models, tools, and permissions by approving or rejecting detected drift.” (miggo.io blog, Miggo Security Extends Runtime Defense for AI) |
| Malicious model files | Static scanning with moorai-model-scan, without loading the file: dangerous pickle imports, Lambda-layer bytecode, Python-calling TensorFlow ops and more. Model downloads an agent runs are reported and scanned in the background. Input-triggered backdoors and trojaned weights are out of its reach. |
Watching the model load at runtime: “We know if a model is executing a pickle bomb, or if an agent is using a legitimate tool to access a database it has never touched before.” (miggo.io blog, Miggo Security Extends Runtime Defense for AI) |
| Knowing which AI is running | An on-device AI bill of materials: models, MCP servers, editor extensions, running local model servers and AI keys at rest, checked against your allow-list. | “Miggo automatically maps what’s actually running in production, from agents, models, tools, to frameworks, MCP integrations, and connected data sources based on real execution.” (miggo.io, AI Runtime Observability) |
MITRE ATLAS, as documented. In our reading of vendor material against the 76 ATLAS techniques tagged Agentic AI, Miggo’s published material documents 6 of the 76: AML.T0010 AI Supply Chain Compromise, AML.T0051 LLM Prompt Injection, AML.T0053 AI Agent Tool Invocation, AML.T0103 Deploy AI Agent, AML.T0112 Machine Compromise and AML.T0132 Misconfigured or Publicly Exposed AI Services, each with a limit on what it covers (read 28 October 2026); the AML.T0103 cell is the agent inventory in its AI bill of materials, which is discovery rather than detection of an adversary launching an agent. Most of Miggo’s product is application security, which ATLAS does not score, so the count reflects the category as much as the product. It measures what the documentation says, not a test of the product, and Miggo’s product documentation was not read. The method and every quote behind the number are in MITRE ATLAS agentic-technique coverage.
Where MoorAI is stronger. AI agents, and what is inside each action. The file read, the shell command, the MCP argument and result, the model’s tool call, the prompt and the output are read where the agent runs and coached, masked or blocked there, in production agent services, CI jobs and containers and on employee machines. A high-impact tool call is held to the person’s task, a service to its declared workload profile, and a build or deploy step can refuse a model file whose hash is not on the approved list. The console receives no content, the code that guarantees that is MIT, and enforcing is free on a self-hosted console.
Where Miggo is stronger. The application underneath. Function-level detection and blocking for any production application, which vulnerabilities are actually reachable and exposed, WAF rules generated for the exploit in front of you and kept current in AWS WAF, a vulnerability intelligence feed, and for AI applications a runtime view of the models and tools that really execute, down to a pickle payload firing as a model loads. If the job is application security in production, Miggo is the larger product and MoorAI is not one.
Which to run where. Run Miggo on production services, AI applications included, where the question is whether an exploit reaches code that runs, and how to stop it before the patch ships. Run MoorAI on the AI agents themselves, in production (server mode, the Agent SDK, the moorai-serve sidecar, the MCP gateway and the model proxy) and on employee endpoints, where the question is what an agent is about to read, run or send, and where the record should hold no prompt content. For an AI agent in production the two watch different layers of the same call, Miggo the execution underneath it and MoorAI the call itself. Nothing in either’s public material says they conflict, and we have not run them together.
Questions about MoorAI and Miggo Security
Is Miggo Security a competitor to MoorAI?
In part. Most of Miggo is application detection and response for production applications: runtime vulnerability prioritization, exploit blocking inside the application and WAF rules for the patch gap. MoorAI is runtime security for AI agents, on AI servers (server mode, the Agent SDK, the moorai-serve sidecar, the MCP gateway and the model proxy) and on employee endpoints. They overlap on AI agents running in production, where Miggo watches the execution underneath the agent and MoorAI judges each tool call’s content before it runs.
Does Miggo Security cover coding agents on developer laptops?
Not in its public material. Miggo’s AI pages describe AI applications and agents running in production, observed by its sensor or from ingested traces. MoorAI runs inside Claude Code, Codex, Copilot CLI, Gemini CLI, Cursor and Factory Droid through their hooks, and in front of local MCP servers.
Does Miggo Security read prompts?
Its pages say both. The AI platform page says its AI detection and response monitors how AI and agents operate inside applications, including prompts and model interactions; its launch post says it does not read the chat and sees the functions and traces. MoorAI reads prompts, files, commands, MCP traffic and model calls where the agent runs, on a server or an endpoint, and sends a category, a risk level and a keyed one-way hash by default.
How does each handle a malicious model file?
Miggo watches the model load in production and stops malicious code, such as a pickle payload, when it runs. MoorAI scans model files statically without loading them, on the device, including downloads an agent runs, and a build or deploy step can refuse a model file whose hash is not on the approved list. The two are complementary: one before the load, one during it.
Who backs Miggo Security?
Miggo came out of stealth in April 2024 with YL Ventures among its investors, and announced a $17 million Series A led by SYN Ventures with continued participation from YL Ventures in April 2025.
Miggo capabilities are taken from Miggo’s own published material, checked on 28 October 2026: miggo.io, AI Runtime Defense Platform, Agentic Detection & Response, AI Runtime Observability, Why ADR, Runtime Detection and Response, Miggo Rulesets for AWS WAF, and the blog posts Miggo Security Extends Runtime Defense for AI, An Autonomous Agent Ran 17,600 Actions Against Hugging Face, RCE in LiteLLM, Kubernetes Sensor, Grafana Labs partnership, Miggo Pulse and Series A, and its GitHub organisation. Miggo’s product documentation is behind a login and was not read. Every quoted phrase is Miggo’s. ◐ = partial: present but narrower than the other column. — = unconfirmed, not necessarily absent. MoorAI marks reflect shipped capability: hooks run in Claude Code, Codex, Copilot CLI, Gemini CLI, Cursor and Factory Droid; server mode, @moorai/agent-sdk, moorai-serve, moorai-mcp-gateway and moorai-model-proxy cover agents that run as services. The agent is MIT and runs on macOS, Windows and Linux; the console is source-available under the Elastic License 2.0, free self-hosted with no user limit, and paid when hosted by MoorAI. Miggo Security is a trademark of its owner; this page is independent and is not affiliated with or endorsed by Miggo Security. Both products change, so check specifics against current documentation.