Manual AI Red Team

Manual AI Red Team engagements are expert-led penetration tests specifically tailored to the unique attack surface of AI-powered systems. Unlike automated scanning, a manual red team emulates sophisticated, persistent adversaries who combine domain knowledge of ML, prompt engineering, software exploitation, and social tactics to uncover subtle, high-impact vulnerabilities. The goal is to reveal realistic attack paths that could lead to model compromise, data exfiltration, integrity loss, or misuse of automated decisioning — and then provide prioritized, actionable mitigations.

A focused penetration test for AI systems examines the whole stack that enables model-driven functionality — from data stores, model training environments and CI/CD pipelines to inference endpoints, APIs, and connected user interfaces. Typical activities include:

• Asset & threat modeling: Identify critical ML assets (datasets, model artifacts, feature stores, inference endpoints) and map likely attacker goals and capabilities.
• Endpoint & API testing: Assess authentication, authorization, rate-limiting, input validation, and logic flaws on model-serving endpoints and model-management APIs.
• Infrastructure & orchestration review: Evaluate container registries, orchestration (Kubernetes), storage permissions, secrets management, and CI/CD pipelines for misconfigurations that could enable lateral movement or model replacement.
• Privilege escalation & lateral movement: Simulate techniques an attacker might use after initial access to reach sensitive data or model artifacts.
• Supply-chain & dependency analysis: Look for insecure third-party libraries, model marketplaces, and pre-trained weights that may introduce vulnerabilities.

Outcome: A prioritized list of exploitable issues, mapped to r

Model Poisoning Simulations

Model poisoning (training-time attacks) can corrupt model behavior or implant backdoors. In controlled, ethical simulations we:

• Threat scenario definition: Work with your team to define high-impact poisoning goals (targeted misclassification, backdoor triggers, bias introduction).
• Controlled data manipulation: In a safely isolated test environment, inject crafted malicious samples or subtle label flips to measure model susceptibility and the attack budget required.
• Evaluation of defenses: Test the effectiveness of data validation pipelines, robust training techniques (e.g., differential privacy, robust loss functions), and provenance controls in detecting and mitigating poisoning.
• Impact analysis: Quantify how poisoning affects model metrics, decision boundaries, and downstream business processes.

Outcome: Concrete guidance on hardening training pipelines, improving data governance, and deploying monitoring that detects poisoning attempts early.