AI Red Teaming Platform

The only platform purpose-built to automatically find, classify, and score vulnerabilities in your LLM and GenAI applications — before attackers do.

Automated Attack Simulation Real-time Threat Detection Risk Scoring Dashboard Agentic AI Coverage OWASP · MITRE · NIST Mapped
Request Early Access

The AI Red Teaming Platform

A product that automatically attacks, evaluates, and scores your AI — so you know exactly what's broken and why

Standard security scanners don't understand language models. The AI Red Teaming Platform does. It speaks the same language as your LLM — generating thousands of adversarial inputs, evaluating every output, and surfacing what breaks your model's guardrails.

Point it at any LLM endpoint or agent workflow. The platform runs autonomously — no manual prompt writing, no scripting, no external team involved. Every vulnerability is classified, severity-scored, and mapped to industry frameworks in a live dashboard.

Automatically generates context-aware adversarial prompts tailored to your application's purpose and guardrails
Uses LLM-as-evaluator to assess every model output for policy violations, data leakage, and harmful content
Delivers a structured risk report — every finding scored, categorized, and mapped to OWASP LLM Top 10 and MITRE ATLAS
Covers LLM chatbots, RAG pipelines, autonomous agents, fine-tuned models, and multi-model workflows
AI Red Teaming Platform — Scan Results
Attack Variants Run
3,847
This scan
Vulnerabilities Found
24
Across 6 categories
Risk Score
HIGH
7.4 / 10
Critical System prompt fully extracted via indirect injection through RAG context OWASP LLM01
Critical Jailbreak succeeded — model provided restricted content via role-play persona OWASP LLM02
High PII leakage detected — training data regurgitation via targeted probing OWASP LLM06
High Agent tool misuse — unauthorized API call triggered via goal hijacking OWASP LLM08
Medium Guardrail bypass via multilingual token obfuscation (base64 encoding) OWASP LLM02
Attack Simulation
Generates thousands of context-aware adversarial inputs automatically
LLM-as-Evaluator
AI-powered output assessment for policy, safety, and data violations
Risk Scoring
Every finding severity-scored and framework-mapped in a live dashboard
Agent Coverage
Full testing for agentic workflows, tool-use, and multi-step AI pipelines
Model Scanning
Detect backdoors, poisoning, and supply chain risks in model weights

What the Platform Detects

50+ vulnerability classes mapped to OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF

Prompt Injection

  • Direct user prompt injection
  • Indirect injection via RAG
  • System prompt extraction
  • Instruction override attacks

Jailbreak & Bypass

  • Role-play & persona exploits
  • Token obfuscation techniques
  • Guardrail circumvention
  • Multilingual evasion

Data & PII Leakage

  • Training data regurgitation
  • System prompt disclosure
  • PII extraction via probing
  • Vector store data exposure

Unsafe Agent Behavior

  • Unauthorized tool invocation
  • Privilege escalation in chains
  • Goal hijacking attacks
  • Memory & context poisoning

Model Poisoning

  • Backdoor trigger detection
  • Fine-tune integrity check
  • Training data corruption
  • Hidden behavior activation

Adversarial Attacks

  • Inference-time adversarial input
  • Model extraction probing
  • Membership inference tests
  • Evasion attack simulation

Supply Chain Risk

  • Third-party model inspection
  • Plugin & tool audit
  • AIBOM generation
  • Dependency CVE mapping

Harmful Content

  • Toxic & hate speech output
  • CSAM & CBRN content tests
  • Misinformation generation
  • Brand & legal risk output

How It Works

Point. Scan. Review. The platform does the rest automatically.

1
Connect Your LLM
Point the platform at your LLM API endpoint, agent, or RAG pipeline. No code changes, no agents to install.
2
Platform Profiles It
The platform auto-detects your app's purpose, system prompt, guardrails, and available tools to tailor its attack strategy.
3
Attacks Run Automatically
Thousands of adversarial prompts are fired across 50+ vulnerability categories. No manual scripting required.
4
Outputs Are Evaluated
An LLM-based evaluator assesses every model response for violations, leakage, and unsafe behavior with high accuracy.
5
Dashboard & Report
All findings appear in a live dashboard — severity-scored, framework-mapped, and ready for review or export.

Platform Features

Everything built into the product — no add-ons, no extra tooling needed

Context-Aware Attack Engine

Generates attacks adapted to your specific application — business purpose, system prompt content, and active guardrails — not generic templates.

Auto-profiling 50+ Attack Types Custom Prompts

LLM-as-Evaluator Engine

Uses fine-tuned LLM detectors — not keyword rules — to assess model outputs for jailbreaks, PII, harmful content, and policy violations with low false-positive rates.

AI-powered Detection Low False Positives Multi-category

Agentic & Tool-Use Scanner

Simulates multi-step attacks against LLM agents — testing tool misuse, privilege escalation across chains, goal hijacking, and context poisoning in autonomous workflows.

Agent Workflows Tool Misuse Multi-step Attacks

Model Security Scanner

Scans model weights and serialized files for malware, embedded backdoors, and hidden triggers before they reach production. Generates AIBOM for full model supply chain visibility.

Weight Scanning Backdoor Detection AIBOM

Live Risk Dashboard

All vulnerabilities surface in a real-time dashboard — filtered by severity, category, and framework. Track your AI risk posture over time as models and prompts evolve.

Real-time View Severity Filtering Trend Tracking

Scheduled Retesting

Automatically re-run scans on a schedule or on demand when your model, system prompt, or tool configuration changes. Detect regressions and new vulnerabilities early.

Scheduled Scans Drift Detection Regression Checks

Runtime Monitoring

Monitors live model inputs and outputs in production. Detects and blocks malicious prompts, PII in responses, and policy violations in real time — without touching model weights.

Input Inspection Output Guard PII Blocking

Scan Reports & Export

Every scan produces a structured findings report with evidence artifacts — exportable as PDF, JSON, or CSV. Findings pre-mapped to OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, and EU AI Act.

PDF / JSON / CSV Evidence Artifacts Framework Mapping

Supported LLM Providers

Works with any model accessible via API — cloud, open-source, or self-hosted

OpenAI GPT-4 / o-series Anthropic Claude Azure OpenAI Google Gemini Meta Llama Mistral AI Cohere Custom / Self-hosted

Compliance Framework Coverage

Every finding automatically mapped — no manual cross-referencing required

OWASP LLM Top 10All 10 categories covered
MITRE ATLASAdversarial ML threat matrix
NIST AI RMFAI risk management framework
EU AI ActHigh-risk AI compliance
ISO/IEC 42001AI management systems
SOC 2AI control mapping

See the AI Red Teaming Platform in Action

Request early access or a live demo — find out exactly what vulnerabilities are hiding in your LLM applications

Request Early Access