AI Red Teaming Services

Expose vulnerabilities in your AI systems before adversaries do. OWASP LLM Top 10 aligned adversarial testing for LLMs, AI agents, and intelligent applications.

Request an Assessment

Full OWASP LLM Top 10 Coverage

From prompt injection to model theft — complete coverage of every OWASP LLM risk category, delivered at up to 70% lower cost than traditional providers.

View OWASP Coverage

AI-Augmented Attack Simulation

Our red team uses cutting-edge AI tools to accelerate threat modelling, craft adversarial inputs, and simulate sophisticated AI-powered attack scenarios.

Explore Services

AI Red Teaming at Enterprise Quality, SMB Pricing

Adversarial security testing for AI systems that saves time and money

LLM & GenAI Red Teaming

Simulate real-world attacks on your large language models, chatbots, and generative AI products. Fully aligned to OWASP LLM Top 10 vulnerability categories.

  • Prompt injection & jailbreaking
  • Sensitive data exposure testing
  • Model denial-of-service simulation

AI Pipeline Security Testing

Assess ML training pipelines, data ingestion, model serving infrastructure, and MLOps tooling against adversarial threats from supply chain to deployment.

  • Training data poisoning scenarios
  • Supply chain attack simulation
  • Model inversion & extraction attacks

Continuous AI Red Teaming

Beyond point-in-time assessments — subscribe to ongoing adversarial testing that evolves as your models do, with monthly reports and 24/7 expert access.

  • Monthly adversarial exercises
  • Regression testing for new model versions
  • Continuous OWASP alignment monitoring

OWASP LLM Top 10 — Full Coverage

Every engagement maps to the OWASP Top 10 for LLM Applications — the industry standard for AI security risk

ID Vulnerability What We Test Risk Level
LLM01 Prompt Injection Direct & indirect injection attempts to hijack model behavior, override system prompts, and exfiltrate data through crafted user inputs and embedded instructions. Critical
LLM02 Insecure Output Handling Downstream exploitation of unvalidated LLM outputs — XSS, SSRF, remote code execution, and CSRF triggered via LLM-generated content reaching other systems. High
LLM03 Training Data Poisoning Manipulation of training datasets and fine-tuning pipelines to introduce backdoors, biases, or malicious behaviors that persist in model outputs after deployment. Critical
LLM04 Model Denial of Service Resource-exhaustion attacks via computationally expensive prompts, recursive context flooding, variable-length input abuse, and token manipulation attacks. High
LLM05 Supply Chain Vulnerabilities Risks in third-party models, datasets, plugins, pre-trained weights, and model hub packages used in your AI stack — including dependency confusion scenarios. High
LLM06 Sensitive Information Disclosure Extraction of PII, trade secrets, API keys, system prompts, and confidential training data embedded in model outputs through targeted adversarial prompting. Critical
LLM07 Insecure Plugin Design Exploitation of LLM plugins and tool-use integrations — parameter injection, privilege escalation, and unauthorized API calls executed via malicious agent actions. High
LLM08 Excessive Agency Testing AI agents granted overly broad permissions — identifying scenarios where the model takes unintended high-impact real-world actions autonomously. High
LLM09 Overreliance Assessing business processes that depend on LLM outputs for critical decisions without sufficient human oversight, validation controls, or automated fallback mechanisms. Medium
LLM10 Model Theft Extraction attacks to replicate proprietary model behavior, weights, or training data through systematic API querying, output analysis, and membership inference techniques. High

Empowering AI Security Excellence

Specialized red team capabilities across every dimension of AI and ML security

LLM Application Testing

  • Prompt Injection (Direct & Indirect)
  • System Prompt Extraction
  • Jailbreak & Alignment Bypass
  • Hallucination Exploitation
  • Context Window Manipulation

Training Pipeline Attacks

  • Data Poisoning Simulation
  • Backdoor Trigger Injection
  • Label Flipping Attacks
  • Dataset Integrity Assessment
  • Fine-Tuning Hijacking

AI Agent & Plugin Security

  • Autonomous Agent Red Teaming
  • Tool-Use & Plugin Exploitation
  • Excessive Agency Testing
  • Multi-Agent Trust Boundary Tests
  • Memory & Context Injection

Model Extraction & Inference

  • Black-Box Model Extraction
  • Membership Inference Attacks
  • Model Inversion Testing
  • Intellectual Property Theft Sim
  • API Rate-Limit Bypass

AI Supply Chain Security

  • Third-Party Model Risk Assessment
  • Model Registry & Hub Audit
  • Dependency Confusion in ML Libs
  • Pre-trained Weight Tampering
  • SBOM for AI Components

Cloud-Native AI Infrastructure

  • MLOps Pipeline Security
  • Model Registry Access Controls
  • GPU/TPU Workload Isolation
  • AI SaaS Integration Risk
  • Serverless AI Function Attacks

AI-Powered Social Engineering

  • AI-Generated Spear Phishing
  • Deepfake Voice/Video Simulation
  • CEO Fraud via AI Cloning
  • AI-Powered Vishing Campaigns
  • Synthetic Identity Attacks

Multimodal AI Testing

  • Image & Vision Model Attacks
  • Adversarial Image Perturbations
  • Audio Model Manipulation
  • Video Generation Abuse Testing
  • Cross-Modal Injection Attacks

AI Governance & Compliance

  • EU AI Act Risk Assessment
  • NIST AI RMF Alignment
  • Bias & Fairness Testing
  • AI Transparency Audit
  • Red Team Report for Regulators

RAG & Knowledge Base Security

  • Retrieval Poisoning Attacks
  • Vector Database Injection
  • Knowledge Base Exfiltration
  • Embedding Inversion Testing
  • Grounding Bypass Scenarios

Our AI Red Teaming Methodology

A structured engagement lifecycle aligned to OWASP, MITRE ATLAS, and NIST AI RMF

01

Threat Modelling

Define AI assets, attack surfaces, threat actors, and OWASP LLM categories in scope.

02

Reconnaissance

AI-assisted OSINT on model versions, endpoints, training sources, and third-party integrations.

03

Adversarial Testing

Execute multi-vector attacks — prompt injection, extraction, poisoning, and agent exploitation.

04

Escalation & Pivoting

Chain AI vulnerabilities to achieve real-world impact: data exfiltration, infrastructure compromise.

05

Objective Achievement

Simulate attacker end goals: model theft, sensitive data extraction, or AI service disruption.

06

Reporting

Executive summary and full technical report with OWASP mapping and remediation roadmap.

Enterprise AI Security, SMB Pricing

Advanced AI red teaming capabilities that don't break the bank

AI-Augmented Red Team

Our operators use AI tools to accelerate attack generation, OSINT, and adversarial prompt crafting — delivering deeper OWASP coverage in less time.

Full OWASP LLM Top 10 Alignment

Every engagement is structured around the OWASP Top 10 for LLM Applications, ensuring comprehensive and industry-recognized risk coverage for your AI systems.

Rapid Engagement Start

Kick off your AI red team engagement in days, not weeks. Streamlined scoping and onboarding built for fast-moving AI development teams.

Compliance-Ready Reports

Reports structured to satisfy SOC 2, ISO 27001, EU AI Act, and NIST AI RMF audit requirements, saving you time with regulators and auditors.

Continuous Red Teaming

AI models evolve constantly. Subscribe to ongoing adversarial testing to stay ahead of new threats with every model release and fine-tuning cycle.

24/7 Expert Support

Direct access to your assigned AI red team lead throughout the engagement. No tickets, no delays — expert answers when you need them most.

Ready to Red Team Your AI Systems?

Get a complimentary AI attack surface analysis and custom red team proposal — aligned to OWASP LLM Top 10, no commitment required.

Start Your AI Red Team Assessment