AI Adversarial Red Teaming

Think like an attacker and validate the real-world business impact of AI risk.

Exfinity conducts human-led adversarial testing against live AI systems to determine whether high-risk outcomes can actually be achieved, rather than producing only a checklist of theoretical weaknesses.

The Challenges

Organisations are deploying AI assistants, agentic workflows, RAG-based knowledge systems, local models, and AI customer-service platforms at speed. Even after application VAPT, API testing, and AI security reviews, leadership may still not know whether confidential information can be extracted or guardrails can be bypassed.

AI systems carry behavioural risks that traditional testing does not fully examine. An attacker may try to manipulate instructions, poison retrieved context, misuse connected tools, cross user or tenant boundaries, or push an agent into performing an unauthorised action.

The Solution

Exfinity defines the highest-risk business outcomes with your team, maps the complete AI attack surface, and then works like a determined attacker within agreed rules of engagement.

Objectives and Attack Surface Mapping: Define high-risk outcomes and map models, prompts, RAG pipelines, agents, tools, APIs, data sources, identities, and trust boundaries

Human-Led Adversarial Simulation: Use adaptive attack chains to test prompt manipulation, data extraction, guardrail bypass, RAG abuse, and agent or tool misuse.

Business-Impact Validation: Demonstrate what an attacker can achieve and distinguish successful attack paths from theoretical possibilities.

Evidence and Remediation Priorities: Provide an attack narrative, proof of impact, control observations, and prioritised recommendations.

Benefits

Because we craft success for every problem

01

Know Which Attacks Succeed

See exactly which agreed adversarial scenarios work against the AI system and which do not.

02

Validate Business Impact

Move from theoretical weaknesses to evidence of what an attacker can actually achieve.

03

Test AI Guardrails

Assess whether restrictions, safety controls, and behavioural boundaries hold under adaptive attacks.

04

Protect Sensitive Information

Identify realistic paths to confidential, restricted, or cross-user data exposure.

05

Validate Agents and Tools

Understand whether connected tools and systems can be manipulated into unintended actions.

06

Prioritise Remediation

Focus engineering and governance effort on demonstrated attack paths with meaningful impact.

Frequently Asked Questions

Got any questions? we’re here to help

Unsure of what solutions best fit your needs? Don’t hesitate to reach out!

What is AI Adversarial Red Teaming?

It is human-led testing that simulates determined attackers against an AI system to validate whether agreed high-risk business outcomes can be achieved.

How is it different from AI Application Security Testing?

AI application testing looks broadly for weaknesses across the application and AI stack. Adversarial red teaming starts with high-risk objectives and adaptively chains attacks to prove real-world impact.

What can the exercise cover?

The agreed scope can include models, system prompts, RAG pipelines, agents, tools, APIs, identities, enterprise data, and connected business systems.

Is the engagement controlled?

Yes. Objectives, boundaries, excluded actions, data handling, and rules of engagement are agreed before testing begins.

What do we get at the end?

You receive the attack narrative, successful and unsuccessful attack paths, evidence of impact, control observations, and prioritised remediation guidance.