AI Red Teaming Services

AI red teaming attacks the model and the agent, not just the infrastructure around it. A normal penetration test looks for injection, broken auth, and misconfiguration in code and APIs. An AI red team engagement tests whether adversarial content can hijack the agent itself: prompt injection through documents and emails, jailbreaks, tool abuse, privilege escalation, and data exfiltration through model outputs. Because agent behaviour is non-deterministic, these paths cannot be found by static analysis.

Prompt injection is when attacker-controlled content is treated by the agent as instructions. Direct injection comes from the user. Indirect injection is hidden inside content the agent reads: an email, a PDF, a support ticket, a web page, or a document in your RAG index. Once the agent follows those instructions it can call tools, leak data, or take actions on the attacker’s behalf while every log looks normal.

An API has a fixed contract: the same input always produces the same path. An agent decides what to do at runtime, chooses which tools to call, and can be persuaded by the content it processes. So the controls are different too: instruction hierarchy, tool sandboxing, least-privilege agent IAM, output DLP, and behavioural monitoring, on top of the API security you already have.

No one can honestly promise that. Injection is reduced to an acceptable risk through layers: input screening and injection detection, least-privilege tool scoping so a hijacked agent cannot do much, output filtering before data leaves, and behavioural monitoring with circuit breaking when an agent behaves abnormally. The goal is to make a successful injection low impact rather than assume it will never happen.

The OWASP LLM Top 10 is the standard list of security risks for LLM applications, covering prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain risk, sensitive information disclosure, insecure plugin or tool design, excessive agency, overreliance, and model theft. Every engagement covers all ten categories, plus agentic-specific attacks mapped to MITRE ATLAS.

Yes. Multi-agent pipelines are where damage spreads fastest: one compromised sub-agent can pass malicious instructions upstream to an orchestrator and across every downstream agent. We test trust boundaries between agents, message integrity, and delegation logic, then harden them with cryptographic agent identity, message signing, and a defined trust hierarchy.

Six weeks end to end for a typical production agent: threat modelling in days 1-3, automated scanning in days 4-7, manual red teaming in days 8-14, architecture hardening in weeks 3-5, and validation with security sign-off in week 6. A focused assessment on a single pre-production agent can be delivered considerably faster.

MITRE ATLAS is the adversarial threat taxonomy for AI systems, the equivalent of ATT&CK for machine learning and LLM attacks. Mapping findings to ATLAS gives your security team a shared language for AI threats, makes red team results comparable across engagements, and helps evidence coverage during audit and board reporting.

A threat model and attack surface map, a red team report with every successful attack documented and reproduced as a proof of concept, risk ratings and prioritised remediation guidance, the hardened architecture and controls we implemented, validation results after re-testing, a formal security sign-off report, and a monitoring plan with an incident response playbook.