AI Red Teaming

Prompt injection is the most prevalent vulnerability in LLM-powered applications. Our team systematically tests your AI inputs for direct and indirect injection vectors — crafting adversarial prompts that attempt to override system instructions, exfiltrate data, bypass content filters, and manipulate model behaviour. We test both application-level and model-level defences, identifying the specific conditions under which your safety controls fail.

Beyond text-based attacks, we simulate adversarial inputs crafted to fool your AI models — images engineered to be misclassified, audio designed to trigger false transcriptions, and structured data manipulated to corrupt model predictions. We apply established adversarial ML techniques (FGSM, PGD, CW) as well as novel black-box approaches that don't require model internals, reflecting the capabilities of real-world attackers.

We assess your deployed models for a comprehensive range of vulnerabilities: model inversion attacks that reconstruct training data, membership inference attacks that leak information about your dataset, model extraction attacks that steal your intellectual property through repeated querying, and evasion attacks that bypass your model's detection capabilities. Each finding is documented with proof-of-concept evidence and severity classification.

If your models are trained on data from external sources, third-party feeds, or user-generated content, they are potentially vulnerable to data poisoning — where adversaries corrupt your training data to introduce backdoors, biases, or degraded performance. We audit your data pipelines and training datasets for poisoning indicators, backdoor triggers, and anomalous patterns that suggest tampering.

Every red team engagement concludes with a comprehensive report: an executive summary for leadership, detailed technical findings for your engineering team, a risk-ranked vulnerability register, and a prioritised remediation roadmap. We don't hand over a list of problems and leave — we work alongside your team through the remediation process and conduct re-testing to verify that fixes are effective and don't introduce new vulnerabilities.