AI Penetration Testing & Red Teaming
LLMs, AI agents, copilots, ML models. Prompt injection, data poisoning, model extraction, adversarial inputs. Most security firms don't test AI systems. We do — it's a core practice area.
What Is AI Penetration Testing & Red Teaming?
AI penetration testing evaluates the security of systems built on large language models (LLMs), AI agents, copilots, and machine learning pipelines. As organisations race to integrate AI into their products, new attack surfaces emerge that traditional security testing doesn't cover.
Prompt injection, data poisoning, model extraction, adversarial inputs — these are not theoretical risks. They are live vulnerabilities in deployed systems. We test against the OWASP Top 10 for LLM Applications and emerging frameworks from NIST AI RMF and MITRE ATLAS.
Services of an AI Pentest
The attack surfaces unique to AI systems.
AI Penetration Testing
Prompt injection, system prompt extraction, jailbreak attempts, and adversarial input testing against deployed LLM applications. We test whether your model can be coerced into ignoring its guardrails, leaking training data, or producing dangerous outputs.
AI Red Teaming
End-to-end attack simulation against AI systems. Chaining prompt injection with traditional vulnerabilities, agent manipulation, tool abuse, indirect injection through tool outputs, and demonstrating real-world breach scenarios that combine AI and non-AI attack paths.
AI Pipeline Review
End-to-end review of AI infrastructure: data ingestion, preprocessing, model serving, API endpoints, secret management, and integration points between AI components and traditional application infrastructure. Includes evaluation of training data integrity and fine-tuning pipeline vulnerabilities.
AI Security Architecture Review
Architecture-level assessment of AI deployment: access controls around model endpoints, rate limiting, logging and monitoring of model interactions, data classification for AI inputs, tenant isolation in multi-tenant AI platforms, and alignment with NIST AI RMF and ISO 42001.
Methodology
An emerging discipline. We helped define it.
System Mapping
AI architecture review, model identification, input/output channel mapping, tool integration analysis, and understanding the data flows between AI components and traditional infrastructure.
Attack Surface Discovery
Identification of prompt interfaces, API endpoints, agent tool sets, training data sources, and model access paths. We map every way an attacker could interact with the AI system.
Exploitation
Prompt injection campaigns, adversarial input generation, extraction attempts, agent manipulation testing, and chain attacks that combine AI vulnerabilities with traditional security flaws.
Reporting
Findings with reproduction steps, business impact in the context of AI deployment, and practical remediation guidance for development teams building AI systems. We speak the language of ML engineers.
Our Technical Expertise
Most firms haven't built this capability yet. We test against emerging frameworks and real-world attack patterns.
OWASP LLM Top 10
The definitive reference for LLM application security. Prompt injection, insecure output handling, training data poisoning, model DoS, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, model theft, and overreliance.
OWASP Agentic Applications
New framework for AI agent security. Multi-agent collusion, tool abuse, prompt injection through intermediaries, trust boundary violations, emergent behaviour patterns, and unintended action chains.
OWASP Agentic Skills
Security assessment of AI capabilities. Adversarial prompt design, model poisoning through inputs, capability boundary testing, emergent vulnerability discovery, and manipulation of agent reasoning processes.
OWASP MCP Security
Model Context Protocol security assessment. Authentication and authorisation in MCP implementations, secure data exfiltration through context windows, prompt injection via MCP calls, and tool abuse vulnerabilities.
MITRE ATLAS
Adversarial Threat Landscape for Artificial-Intelligence Systems. We map real-world AI attacks to the MITRE framework, including data poisoning, model extraction, evasion attacks, and supply chain compromise techniques used by actual threat actors.
Adversarial ML
Evasion attacks, poisoning attacks, model inversion, membership inference, and model extraction. Applied from NIST AI RMF, MITRE ATLAS, and academic research to deployed systems.
Request an AI Security Assessment
Tell us about your AI system. We'll scope an assessment that covers the attack surfaces traditional pentesting misses.