AI Red Team Services
Adversarial security testing for AI and machine learning systems. We find the vulnerabilities before attackers do.
The Problem
AI systems introduce attack surfaces that traditional security testing misses entirely. Models can be manipulated through adversarial inputs, training data can be poisoned, prompts can be injected to bypass safety controls, and model weights can be extracted by determined attackers.
Standard penetration testing does not cover these risks. You need specialists who understand both cybersecurity and machine learning at a deep level.
What We Test
Adversarial Machine Learning
- Evasion attacks against classifiers and detectors
- Adversarial example generation and transferability
- Model robustness evaluation under perturbation
- Input validation and preprocessing bypass
LLM Security
- Prompt injection (direct and indirect)
- Jailbreak and safety bypass techniques
- Data exfiltration via conversation manipulation
- Agent and tool-use exploit chains
Training Data Integrity
- Data poisoning and backdoor injection
- Label manipulation attacks
- Data provenance and supply chain review
- Privacy extraction (membership inference, model inversion)
AI Supply Chain
- Model repository security (Hugging Face, model zoos)
- Dependency and library vulnerability scanning
- Serialisation and deserialisation risks (Pickle, ONNX)
- MLOps pipeline security review
Our Methodology
- Scoping & Reconnaissance — Understand your AI architecture, threat model, and critical assets.
- Threat Modelling — Map attack vectors specific to your models, data, and deployment.
- Attack Execution — Conduct controlled adversarial testing across identified vectors.
- Analysis & Reporting — Document findings with severity ratings, evidence, and reproduction steps.
- Remediation Support — Work with your teams to implement fixes and validate mitigations.
Engagement Models
Targeted Assessment
Focused testing of a specific model, API, or pipeline. Ideal for pre-launch security review or assessing a single high-risk component.
Duration: 1–2 weeks
Full-Scope Red Team
Comprehensive adversarial engagement covering your entire AI estate — models, data, infrastructure, and MLOps pipeline.
Duration: 4–8 weeks
Continuous Red Team
Ongoing adversarial testing on a retainer basis. Regular assessments as you deploy new models and update existing ones.
Duration: Quarterly / Ongoing
Why LittleData
- Deep expertise at the intersection of cybersecurity and machine learning
- Real adversarial testing, not just automated scanning
- Clear, actionable reports with remediation guidance
- Experience across regulated industries — financial services, healthcare, government
Ready to test your AI defences?
Talk to our red team about an adversarial assessment.