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Repello AI

Also known as: Repello, Repello AI, ARTEMIS, repello.ai

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Security / SOC agentindependentVerified 2026-07-22

Enterprise platform for autonomous AI red teaming, runtime guardrails, and continuous AI security across LLMs, agents, and MCP, run on a three-phase loop: discover and build an AI BOM, red-team, then deploy adaptive guardrails calibrated from results. ARTEMIS profiles and attacks like a human red-teamer, chaining multi-stage exploits across integrations and visualizing attack paths, drawing on 15M+ attack patterns with multi-modal and multi-lingual coverage mapped to OWASP, NIST, and MITRE. Black-box, CI/CD-integrated, 270+ vulnerability types; publishes open-source tooling and original AI-threat research.

Repello AI is an enterprise platform for autonomous AI red teaming, runtime guardrails, and continuous AI security across LLMs, agents, and MCP. It runs on a proprietary three-phase framework where each phase feeds the next: discovery catalogs all AI systems, models, and integrations to build an AI Bill of Materials and application graph and understand the threat model; automated red teaming (ARTEMIS) attacks that surface; and adaptive guardrails deploy runtime controls calibrated from the red-teaming results, with runtime insights in turn improving future testing. ARTEMIS is the autonomous red teamer: it profiles system capabilities to generate tailored attack strategies, builds threat models, runs parallel attack threads, chains exploits across integrations, and adapts tactics based on agent responses, discovering and validating multi-stage exploit chains in AI agents the way a human red-teamer would and visualizing the attack paths that show exactly where breaches happen. It draws on a proprietary threat-intelligence repository the company describes as 15M+ evolving attack patterns for roughly 15x more coverage than manual testing, offers multi-lingual and multi-modal testing across text, image, and audio, and safeguards against 270+ vulnerability types, with findings mapped to OWASP, NIST, and MITRE and delivered as risk scores, evidence, and compliance-mapped mitigations. Testing is black-box, needing no access to underlying algorithms or code, and integrates into CI/CD to catch AI-specific vulnerabilities before release. Repello also publishes open-source tooling (the Artemis GitHub Action, Whistleblower for system-prompt-leakage and capability discovery, and an awesome-llm-redteaming list) and original research on agentic-browser threats and the collapse of mean-time-to-exploit for AI vulnerabilities. Within the AI-agent-security cluster, Repello is a red-teaming-plus-guardrails entrant closing the discovery-to-runtime loop, comparable to the offensive-testing-for-AI positioning of Mindgard and the runtime posture of the agent-native platforms.

Vendor details

Canonical URL

https://repello.ai

Category

Security / SOC agent

Funding status

Independent AI-security company; product recognition includes customer red-teaming engagements with enterprises such as Lyzr; maintains public open-source tooling on GitHub (Artemis GitHub Action, Whistleblower system-prompt-leakage tester, awesome-llm-redteaming); specific funding amounts not disclosed in retrieved materials

Company status

independent

Use cases & customers

Primary use cases

autonomous AI red teaming of LLMs, agents, and MCPmulti-stage exploit chain discovery and attack-path visualizationAI asset discovery and AI Bill of Materials (AI BOM)adaptive runtime guardrails calibrated from red-teaming resultsCI/CD-integrated continuous AI security with compliance-mapped reporting

Target customers

enterprises deploying LLMs, AI agents, and MCP in productionAI product and engineering teams needing pre-release security testingsecurity teams demonstrating compliance coverage for AI systemsorganizations securing multi-modal AI applications

Deployment options

black-box testing with no access to underlying algorithms or codeCI/CD pipeline integration (Artemis GitHub Action)runtime guardrail deployment in production

Integrations

Discovers and catalogs all AI systems, models, and integrations across the infrastructure to build an AI Bill of Materials (AI BOM) and application graph. ARTEMIS integrates into CI/CD pipelines (with a GitHub Action) for continuous testing across LLMs, agents, and MCP, with multi-modal coverage across text, image, and audio. Findings map to OWASP, NIST, and MITRE standards; adaptive guardrails deploy as runtime controls calibrated from red-teaming results. Operates black-box with no access to underlying algorithms or code.

Capability coverage

9.0 / 14 capabilities · 64%

Integrations & Tool CallingDiscovers and catalogs all AI systems, models, and integrations, tests across LLMs, agents, and MCP, and integrates into CI/CD pipelines via a GitHub Action, Repello site and GitHub 2026-07-22 Full
Workflow OrchestrationARTEMIS autonomously profiles, plans, and attacks like a human red-teamer, running parallel attack threads, chaining exploits across integrations, and adapting tactics based on agent responses, Repello product page 2026-07-22 Full
Knowledge Grounding & RAGProprietary threat-intelligence repository of 15M+ evolving attack patterns (15x more coverage than manual testing) grounds attack generation, plus business-contextualized testing tailored to each organization, Repello site and ARTEMIS blog 2026-07-22 Full
Human Oversight & GuardrailsAdaptive guardrails deploy dynamic controls and findings come with compliance-mapped mitigations for human review; explicit approval-gating in the red-teaming loop is not documented, Repello product page 2026-07-22 Partial
Security, Identity & GovernanceFindings map to OWASP, NIST, and MITRE with compliance-mapped mitigations and benchmarking against AI security standards; formal platform attestations not retrieved, Repello site 2026-07-22 Partial
Observability & AuditabilityDelivers risk scores, evidence, and compliance-mapped mitigations and visualizes attack paths showing exactly where breaches happen, with findings organized by OWASP or other frameworks, Repello product page 2026-07-22 Full
Memory & State PersistenceThe AI BOM and application graph persist an inventory of AI assets, and the three-phase framework carries runtime insights back into future testing, Repello site 2026-07-22 Partial
Deployment & Data ResidencyBlack-box testing needs no access to underlying algorithms or code, and runtime guardrails deploy in production; explicit on-premise or options not documented, Repello site 2026-07-22 Partial
Prebuilt Agents, Templates & PacksA large prebuilt attack-vector library within the threat-intelligence repository plus productized platform modules (discovery, ARTEMIS, guardrails), Repello site and ARTEMIS blog 2026-07-22 Partial
Triggers & Channel CoverageContinuous red teaming integrated into CI/CD plus runtime blocking of threats in production, with multi-modal coverage across text, image, and audio, Repello site and ARTEMIS blog 2026-07-22 Full
Model Flexibility & RoutingTests any target model but offers no customer-facing model choice or routing for its own operation, Repello materials 2026-07-22 Unable to verify
APIs, SDKs & MCP ExtensibilityPublic open-source tooling including the Artemis GitHub Action for CI/CD scans, the Whistleblower offensive-security tool, and MCP-aware testing, Repello GitHub and site 2026-07-22 Full
Testing, Debugging & OptimizationBenchmarks the customer's AI application against the highest AI security and safety standards with regression-oriented continuous testing, Repello site and red-teaming blog 2026-07-22 Partial
Browser & Computer UseNo first-class browser or computer-use capability documented; testing operates against AI application interfaces and APIs, Repello materials 2026-07-22 Unable to verify

Pricing

Contact sales

scope of AI systems under continuous red teaming and runtime protection

Contact onlyMedium variable cost

What is public

The platform capabilities (three-phase framework, ARTEMIS, 15M+ attack patterns, 270+ vulnerability types, framework mappings) and free open-source tools are public; no commercial plans, tiers, or dollar amounts are disclosed.

Variable cost rationale

Cost scales with the number of AI systems under continuous testing and runtime protection, growing as the organization's AI footprint expands.

Sales call required

Yes — required for paid access

Free / trial

Open-source tooling available (Artemis GitHub Action, Whistleblower)

Verified 2026-07-22

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