MoorAI vs Straiker
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Straiker is the closest direct competitor on this site. Straiker does not sit at a gateway watching AI traffic go by and call that agent security. They hook the same place we hook. Their own page for the Anthropic integration says it in one line: “Execution-path hooks stop the risky tool call before it fires.” (straiker.ai/anthropic-claude) Their product page says Defend AI “provides runtime security for coding agents including Cursor, GitHub Copilot, and Claude Code” and that it “detects and blocks destructive actions like file deletion and config changes, prevents data exfiltration of proprietary code and secrets, and identifies malicious MCP server and Skills connections in development environments.” (straiker.ai/products/defend-ai) That is the same list MoorAI works through.
What differs is where the decision gets made. MoorAI decides on the machine, from a local threat matrix, and emits category · risk · keyed one-way hash (plus the login and hostname, which the console pseudonymises on arrival) and nothing else. Straiker decides in a detection service. Their integration blog is explicit about the shape of it: “Route model traces or proxy logs to Straiker’s Detection Cloud.” (straiker.ai/blog, Nov 2025) That is the architecture their product needs, and it buys them things a local matrix cannot.
“On-prem” does not close that gap. Their on-prem installer is public, and we read it. It provisions EKS (--install-eks), and its inference chart serves Straiker’s own vLLM build against a model catalogue of seven profiles (antman, hulk, quicksilver, thor, thanos, vision, wanda) scheduled onto GPU node pools whose instance families default to ["g4dn","g5","g6e"], with p4d and p3 profiles alongside. (github.com/straiker-ai/onprem-installer, Apache-2.0) Self-hosted Straiker is a GPU Kubernetes cluster. It is serious engineering, and it does not run on a laptop.
MoorAI is the laptop. A PreToolUse hook inside the coding agent and an MCP stdio gateway in front of the tool servers, evaluating a 85-entry threat matrix locally, blocking before execution, failing closed when the control plane is unreachable, and landing an ed25519-signed, content-free record of every decision. The agent is MIT, so “no content leaves the device” is a claim you can check by reading the code rather than a sentence you have to trust.
Three things are deliberately absent from this page. (1) How their hooks work. “Execution-path hooks” is asserted on their marketing site with no public technical documentation behind it; whether that is a PreToolUse hook, a shell wrapper, an MCP shim or a local proxy, we do not know, and we won’t guess. (2) Their accuracy numbers. Defend AI is marketed as “6-21x lower false positive rates than frontier model judges with 98.1% detection accuracy at <300ms latency”; no methodology, dataset or third-party validation is published, so those are vendor claims and appear here only as vendor claims. (3) Company facts. Funding round, founding year, headcount, headquarters and patents are reported inconsistently across sources we checked, so none of them appear at all. Every Straiker statement below is either a direct quote from their site or a line from a repository they publish, with the source named.
The core difference is where detection runs, and Straiker’s own published artifacts show it.
fail_closed with a default of false, described as: “Reject traffic when Straiker cannot be reached.” Left at the default, the README’s verdict table says what happens instead: “Straiker was unreachable or errored, and fail_closed is false, so traffic passed uninspected”, stamped x-straiker-verdict: degraded. It advises operators: “Alert on degraded and unknown. Both mean the control is not running while the traffic looks healthy.” (github.com/straiker-ai/kong, README) Failing closed is a setting, off by default. That is a defensible availability choice for a gateway, and they document it clearly. MoorAI’s position differs: with no control plane and no network, the local evaluation still runs, and if it cannot run, the administrator has already chosen per surface whether the action proceeds or is refused, in the signed policy or the root-owned machine config, and every refusal raises a FAIL_CLOSED alert.get_raw_body() returns nil, and traffic proxies uninspected behind a 200.” On plugin composition with ai-proxy: at a plugin priority of 1000 the response phase never ran and nothing reported it, and the plugin now runs at 760, where “the flag is re-set when this plugin is collected, after AI Proxy cleared it, and the response phase runs normally.” (github.com/straiker-ai/kong, README) Publishing that is to their credit; most vendors would not. It is also the structural cost of enforcing in a proxy tier you have to configure correctly. A hook that runs in-process has fewer places to be misconfigured into silence.Their strongest argument against us. Straiker publishes a whitepaper titled “No Hard Boundaries: The Case for Semantic Detection in Agentic AI”, and its argument is that pattern matching and small classifiers are the wrong primitive against modern semantic attacks. Aimed at a 85-entry, largely deterministic threat matrix, that argument is correct: a multi-turn, politely-phrased, role-played jailbreak will walk straight past a pattern. MoorAI’s answer is architectural. It does not try to detect the persuasion; it stands at the point where the persuasion has to become an action. A PreToolUse hook sees rm -rf /, a read of ~/.aws/credentials, or an MCP call to a server that is not on the allow-list, and it sees the same thing regardless of how eloquently the model was talked into it. You do not have to detect the argument to block the act. That is a different bet from theirs. MoorAI also ships an opt-in, default-off second opinion: on an ambiguous case the regex can escalate to a local model (loopback Ollama), or, in the provider setting, to Anthropic’s API with an API key already on the device, which sends the text to Anthropic. It is advisory either way. That narrows the gap without closing it. Where the act itself is ambiguous (a prompt going out, a document being summarised), their fine-tuned semantic detection is still the better bet, and the second table below says so.
Where MoorAI holds ground Straiker does not
Nearly all of these rows come from one design decision: the decision is computed where the action happens, with nothing sent anywhere to compute it. That is what makes offline enforcement, fail-closed behaviour, stdio MCP coverage and a content-free evidence trail possible at once. All four follow from the same architecture.
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| MoorAI | Straiker | |
|---|---|---|
| Detection itself runs on the device — no trace, log or payload sent to compute a verdict | ✓85-threat matrix, local | ✗"Route model traces or proxy logs to Straiker's Detection Cloud" |
| Only category · risk · keyed one-way hash (plus login and hostname, pseudonymised on arrival) leave the device | ✓ | ✗payloads and conversation content in scope by design |
| Fails closed when the control plane is unreachable | ✓local evaluation; no verdict, no action | ◐Kong plugin: fail_closed option, off by default |
| Full enforcement offline and on air-gapped machines | ✓ | ✗detection service or a GPU cluster required |
| No cloud tenant, gateway tier or GPU cluster to stand up | ✓one agent, one binary | ✗EKS + vLLM on g4dn/g5/g6e nodes for self-hosted |
| Governs local stdio MCP servers that never touch the network | ✓Claude Desktop filesystem, git, database | ✗no traffic for a proxy or trace pipeline to see |
| Open source (MIT) — the content-free claim is auditable | ✓ | ◐Kong plugin Apache-2.0; detection engine and CLIs not open |
| Full public documentation — no access code, no demo call | ✓ | ✗product docs gated |
| Free to start — install and enforce without talking to sales | ✓agent MIT and uncapped; console free self-hosted, no user limit | ✗sales-led |
| Per-tenant ed25519-signed policy bundle, signature verified on the device | ✓ | — |
| Slopsquatting firewall — hallucinated package installs blocked at the tool call | ✓ | — |
| On-device exposure ledger — what this machine has already sent, held locally | ✓ | ✗equivalent history lives in their cloud |
| Entitlement envelope per agent, with drift detection | ✓ | — |
Where Straiker covers ground MoorAI does not
MoorAI loses more ground to Straiker than to any other vendor on this site. Straiker is building a security platform for all enterprise AI: the agents you buy, the agents you build, and the chatbot your customers talk to. MoorAI governs one layer: what AI agents do at the point of action, deepest on the coding agent on a developer’s machine. If the requirement is broader than that surface, the rows below are the answer and we are not the vendor.
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| MoorAI | Straiker | |
|---|---|---|
| Automated adversarial red-teaming against your deployed agents | ◐moorai-redteam tests your policy against a local corpus | ✓Ascend AI attacks the live app with offensive models |
| Continuous, scheduled red-team campaigns with reporting | ✗ | ✓ |
| Protects enterprise-built AI apps and customer-facing chatbots | ✗developer endpoint only | ✓ |
| Builder platforms — Bedrock AgentCore, Azure AI Foundry, Copilot Studio | ✗ | ✓ |
| Claude Chat, Cowork and Enterprise via the Compliance API | ✗ | ✓their own Anthropic integration page |
| LLM-based semantic detection of multi-turn, obfuscated attacks | ◐deterministic matrix; opt-in, default-off on-device model escalation, advisory | ✓fine-tuned detection models |
| Multimodal image understanding, not just text lifted out of an image | ◐on-device OCR, then text rules | ✓multimodal detection, per their published material |
| MCP vulnerability database at scale | ✗policy on the call, not a catalogue of known-bad servers | ✓ |
| Published exploit research, with vendor patches shipped as a result | ✗ | ✓ |
| Enforcement at the API gateway tier (Kong, Azure APIM, LiteLLM, AgentGateway) | ✗endpoint only, by design | ✓four public integration repos |
| Agentic-browser guardrails | ◐companion extension covers 8 chat apps (ChatGPT, Claude, Copilot, Gemini, Perplexity, Mistral, DeepSeek, Grok) | ✓ |
| SOC 2 and ISO 27001 certified | ✗ | ✓ |
Same capability, different mechanism
Both products do all nine of these, so a check mark in each column would tell you nothing. They do them from different positions with different failure modes, and that difference should drive the choice. The cells describe how each works.
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| MoorAI does it by… | Straiker does it by… | |
|---|---|---|
| Claude Code coverage | A PreToolUse hook in the agent, evaluating locally and returning a decision before the tool runs. Nothing leaves to reach that decision. | “Execution-path hooks for Claude Code,” in their words, plus OpenTelemetry from Cowork and the Compliance API for Claude Platform and Enterprise. The hook mechanism is not publicly documented, so we describe it no further than they do. |
| Cursor and GitHub Copilot | Narrower. Hook enforcement is validated end to end on Claude Code; Codex and Copilot CLI block through their pre-tool hooks but are not yet validated against the live agents. Agent on macOS, Windows and Linux. | Named as first-class: “runtime security for coding agents including Cursor, GitHub Copilot, and Claude Code.” Broader assistant coverage than ours. |
| Blocking destructive actions at runtime | At the tool call, in-process. The hook sees the command and its arguments and refuses. No network path is involved, so nothing about the block depends on reachability. | At their enforcement point, which varies by deployment mode — hook, sensor, proxy or gateway. Their published gateway plugin can fail closed but does not by default (fail_closed: false); the hook path’s failure behaviour is not documented. |
| Prompt injection, direct and indirect | Deterministic matching in the live flow (injected instructions arriving via tool output, a poisoned rules file, or a cross-server toxic flow), plus the backstop that the injection still has to become a tool call, which is separately governed. | Fine-tuned detection models reading traces and payloads, which is a stronger primitive for multi-turn and obfuscated attacks. Requires the trace to reach the detector. |
| MCP inventory and risk scoring | What this machine actually runs, including stdio servers with no network presence, scored locally and reported content-free as part of the AIBOM. | A vulnerability database at scale, built from research across the ecosystem — far more known-bad servers than any single endpoint could learn about on its own. |
| Skill and rules-file analysis | Drift detection on a fingerprint, and hiding techniques named on the device. The file is hashed and watched, and instructions hidden by padding, HTML comments, concealment wording or text aimed at a reviewer get their own labels; the content of your CLAUDE.md is never transmitted. |
Identifying “malicious MCP server and Skills connections in development environments,” in their words — classification of what the file is trying to do. |
| Secrets and PII detection | Before it leaves. The prompt, paste or tool argument is checked on the device and blocked at the boundary; only category and a keyed hash are recorded (plus the login and hostname, which the console pseudonymises on arrival). | By analysing the payload — “prevents data exfiltration of proprietary code and secrets.” Higher-fidelity classification, reached by the payload being analysed somewhere. |
| OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF mapping | Attached to each signed decision, spanning the OWASP LLM Top 10 and the OWASP Agentic Top 10 (ASI01–ASI10) and carried with in-toto/SLSA attestation and an AIBOM of the agent, so the compliance artefact is the enforcement record itself, and it contains no prompt content. | Attached to findings and red-team results, across a much wider estate than one developer laptop, with SOC 2 and ISO 27001 behind the platform. |
| Agent discovery | Endpoint-out. Which agents, CLIs, browser tools and MCP servers exist on this machine — including the ones nobody registered and the ones that never open a socket. | Estate-in. Which AI applications and agents exist across the organisation, including ones running in cloud infrastructure where no laptop is involved at all. |
Where MoorAI is stronger. Content stays on the device, and it still works offline. Every MoorAI verdict is computed by code you can read, on the machine where the action is about to happen, from a matrix that needs no GPU and no round-trip. That is what makes offline enforcement real, keeps policy enforced when the network is gone, makes stdio MCP servers governable at all, and makes an audit trail an auditor can read without reading anybody’s prompts. If your constraint is a regulator, an air gap, or a legal team that will not approve conversation content leaving the endpoint, that constraint is the product.
Where Straiker is stronger. Everything except that. Seven fine-tuned detection models will catch semantic attacks a 85-entry matrix will not. Offensive red-teaming against your live agents is a capability we do not have and are not building. They cover the AI applications your company builds and the chatbot your customers use, neither of which runs on a developer laptop, and they meet enterprise procurement where it actually is — SOC 2, ISO 27001, analyst coverage, and a gateway story for infrastructure teams who will never install an endpoint agent. If your AI risk is broader than “coding agents on developer machines,” theirs is the larger product.
Splitting the estate between them. These two overlap more than any other pair on this site, and they still fail in different places. Run Straiker across the AI estate (the apps you build, the chatbots you expose, the gateways your traffic already crosses) and for adversarial testing of any of it. Run MoorAI on the developer machines, where the tool call has to be refused in-process rather than judged remotely, where stdio MCP servers never generate a trace to route anywhere, and where the evidence an auditor reads must contain no one’s prompt content at all. Their fail-open default and our fail-closed one are both reasonable; decide which one you want on each surface.
Straiker capabilities are mapped from Straiker’s own published material, checked in 2026: straiker.ai/products/defend-ai, straiker.ai/anthropic-claude, their integration-models blog, and the public repositories at github.com/straiker-ai — kong, apim, litellm, AgentGateway and onprem-installer. Every quoted phrase is theirs. The mechanism behind “execution-path hooks” is not publicly documented and is not characterised here. Straiker’s published accuracy, false-positive and latency figures are vendor claims with no methodology or independent validation available, and no row on this page depends on them. Funding, patents, founding year, headcount and headquarters are omitted because public sources disagree. ◐ = partial — present but narrower than the other column. — = unconfirmed, not necessarily absent. MoorAI marks reflect shipped capability: hook enforcement is validated end to end on Claude Code, with Codex and Copilot CLI blocking through their own pre-tool hooks but not yet validated against the live agents, the agent runs on macOS, Windows and Linux (the desktop app on macOS and Windows), and MoorAI does not ship TLS inspection — it is permitted by the capability spec as an opt-in layer and no row here credits it. The community agent is MIT and uncapped; the console is free self-hosted for an entire organisation, with no user limit. Straiker and Defend AI are trademarks of their respective owner; this page is independent and is not affiliated with or endorsed by Straiker. Both products evolve — verify specifics against current documentation.