AI Red
Teaming

Your AI models are attack surfaces that traditional security tools cannot assess. We perform adversarial red teaming against your AI and ML systems to find prompt injection paths, model manipulation techniques, data poisoning vectors, and safety bypass methods before malicious actors exploit them.

Request AI Red Team All Services

Prompt Injection Model Manipulation Safety Bypass Data Poisoning

AI Attack Surface Dashboard

Prompt FilteringActive

Output ValidationBypassable

Training Data IntegrityUnverified

Access ControlsEnforced

Safety GuardrailsEvasive

Hardened

Partial

At Risk

Prompt

Model

Safety

Data

Assessment Areas

What We Assess in Your AI Systems

Comprehensive adversarial assessment spanning prompt injection, model manipulation, safety bypass, and AI deployment infrastructure.

Direct Prompt Injection Testing

Testing of user-facing inputs for direct prompt injection that overrides system instructions, manipulates model behaviour, or extracts sensitive configuration.

Indirect Prompt Injection via Data

Assessment of data sources, tool outputs, and external content feeds that could carry injected prompts into your model's context without user awareness.

Jailbreak and Safety Bypass

Testing of known and novel jailbreak techniques that circumvent safety guardrails, content filters, and alignment training to produce harmful outputs.

Multi-Turn Manipulation

Simulation of multi-turn conversation attacks that gradually build context to manipulate the model over several interactions, bypassing single-turn safety controls.

System Prompt Extraction

Attempts to extract system prompts, hidden instructions, and internal configuration through crafted queries that reveal your model's operational directives.

Instruction Override Testing

Testing of encoding tricks, formatting manipulation, and special character injection that override embedded instructions or bypass input validation.

Adversarial Input Generation

Creation of adversarial inputs designed to trigger incorrect classifications, unexpected outputs, or behaviour deviations in your ML models and pipelines.

Model Evasion Testing

Testing of evasion techniques that allow malicious inputs to bypass model detection, classification, or filtering without triggering safety responses.

Data Poisoning Simulation

Simulation of data poisoning attacks that corrupt training data, skew model behaviour, or introduce backdoors through manipulated data pipelines.

Model Extraction Attempts

Attempts to extract or reconstruct model architecture, weights, and proprietary logic through systematic querying and inference analysis.

Membership Inference Testing

Testing whether an attacker can determine if specific data records were used in training, exposing sensitive information about your training datasets.

Output Manipulation Attacks

Testing of techniques that manipulate model outputs, induce hallucinations, or cause the model to generate fabricated information with high confidence.

API Security Assessment

Assessment of AI model API endpoints for authentication gaps, rate limiting issues, input validation flaws, and insecure data handling in transit.

Authentication and Authorisation

Review of access controls for model inference, management interfaces, and administrative functions to prevent unauthorised model access or manipulation.

Rate Limiting and Abuse Testing

Testing of rate limiting and quota enforcement to prevent model abuse, resource exhaustion, and extraction attacks through high-volume querying.

Model Access Controls

Assessment of model-level access controls including role-based access, fine-grained permissions, and tenant isolation for multi-user AI platforms.

Logging and Monitoring Gaps

Review of AI inference logging, anomaly detection, and monitoring configurations to identify gaps that allow adversarial activity to go undetected.

Supply Chain and Dependency Risks

Assessment of third-party model dependencies, pre-trained model provenance, library vulnerabilities, and supply chain integrity across your AI stack.

Methodology

How We Run an AI Red Team Engagement

A structured six-phase programme from AI system discovery through to adversarial validation and remediation.

Phase 01

AI System Discovery

Map all AI and ML models, pipelines, APIs, and data flows across your organisation including third-party model integrations and training infrastructure.

Phase 02

Threat Modelling

Develop threat models specific to your AI systems covering prompt injection, model manipulation, data poisoning, and safety bypass attack vectors.

Phase 03

Prompt and Input Testing

Execute adversarial prompt injection, jailbreak, and safety bypass attempts against your AI models using both known and novel attack techniques.

Phase 04

Model and Pipeline Testing

Test model robustness against adversarial inputs, evaluate data pipeline integrity, and assess model extraction and inference attack feasibility.

Phase 05

Infrastructure Assessment

Review AI deployment infrastructure, API security, access controls, monitoring, and supply chain dependencies for vulnerabilities.

Phase 06

Remediation and Validation

Deliver a comprehensive report with adversarial findings, safety recommendations, and conduct a re-test to validate that attack paths are mitigated.

Who It's For

Who Needs AI Red Teaming

AI Platform Operators

Organisations deploying LLMs, chatbots, recommendation engines, and AI-powered products that need to ensure their models cannot be manipulated or exploited.

ML Engineering Teams

Teams building and deploying machine learning pipelines who need to validate model robustness, data integrity, and safety guardrails against adversarial attacks.

Regulated AI Deployers

Financial services, healthcare, and government organisations with AI governance requirements under EU AI Act, NIST AI RMF, or ISO 42001 frameworks.

FAQ

Questions We Get Asked Often

AI red teaming is an adversarial assessment that simulates attacks against your AI and ML systems to identify vulnerabilities including prompt injection, safety bypass, model manipulation, and data poisoning. Unlike traditional security testing, it targets the unique attack surfaces created by AI models, training data, and inference pipelines.

AI and LLM Security is a broader assessment covering your overall AI security posture including governance, architecture, and compliance. AI red teaming specifically focuses on adversarial attack simulation, testing whether attackers can actually manipulate, bypass, or exploit your AI systems. Red teaming is the offensive validation layer within your AI security programme.

We test direct prompt injection through user inputs, indirect injection through data sources and tool outputs, multi-turn manipulation that builds context over conversation turns, system prompt extraction, instruction override through formatting tricks, and encoding-based bypasses. We use both published techniques and novel approaches.

Yes. We test closed-source models including GPT-4, Claude, and Gemini through their APIs, focusing on prompt-based attacks, output manipulation, and deployment security rather than model internals. We also assess the security of your API integration, access controls, and monitoring.

We reference NIST AI RMF, ISO 42001, OWASP Top 10 for LLMs (2025), MITRE ATLAS, and the EU AI Act risk classification. Our findings map to these frameworks so you can demonstrate compliance while addressing the specific adversarial risks identified.

AI RedTeaming