AI RED TEAMING

Find how AI systems fail before it matters.

Adversarial testing of GenAI applications, agents, workflows, tools and authorization boundaries—connected to real business impact.

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Test assumptionsChallenge controls under adversarial pressure.
Find attack pathsConnect model behavior to surrounding systems.
Prove impactShow what a weakness could enable.
Improve resilienceDeliver retestable remediation guidance.

THE ASSURANCE GAP

A model can behave safely in a demo and still fail inside an enterprise workflow.

Real risk often lives between the model and the systems around it: prompts, retrieval sources, identities, tools, APIs, memory, delegated authority and human approval. RedSkope tests the full path.

01

Prompt injection

Test direct and indirect instructions that attempt to override trusted behavior or application controls.

02

Jailbreak resistance

Evaluate whether guardrails can be bypassed to produce disallowed actions or information.

03

Sensitive-data disclosure

Assess leakage through prompts, retrieval, outputs, memory, logs and connected systems.

04

Excessive agency

Determine whether an AI system can act beyond intended authority or without required approval.

05

Tool and MCP abuse

Probe unsafe tool invocation, poisoned context, trust boundaries and protocol implementation risk.

06

Authorization weaknesses

Test whether identity, tenant, object or action boundaries hold when the workflow is manipulated.

SCOPE OPTIONS

Test the system at the layer where failure would matter.

Engagements can focus on one high-value AI application or evaluate a broader agentic workflow and its supply chain.

  • GenAI applicationsPrompts, retrieval, data flows, guardrails and output handling.
  • Agentic workflowsPlanning, memory, delegation, approvals and action boundaries.
  • AI-enabled APIsAuthorization, object access, input handling and downstream effects.
  • MCP implementationsServers, clients, tools, trust relationships and contextual integrity.
  • AI vendor integrationsThird-party services, data movement and shared responsibility.
  • AI supply chainModels, libraries, retrieval sources, plugins and external dependencies.

ENGAGEMENT METHOD

Controlled, authorized and tied to business impact.

Testing rules, safety boundaries and evidence handling are agreed before execution.

01 / MODEL

Define threat scenarios

Map critical assets, trust boundaries, abuse cases, safety constraints and success criteria.

02 / ATTACK

Execute adversarial tests

Exercise realistic manipulation and abuse paths within agreed rules of engagement.

03 / PROVE

Validate exploitability

Connect technical behavior to reproducible impact without overstating hypothetical risk.

04 / STRENGTHEN

Guide and retest

Prioritize fixes, improve controls and confirm remediation where included in scope.

DELIVERABLES

Evidence your builders can fix and leaders can understand.

Findings distinguish model limitations, application flaws, authorization failures and governance gaps.

  • 01
    Executive attack narrativeCritical scenarios, demonstrated impact and leadership decisions.
  • 02
    Technical findingsReproduction evidence, affected components, severity and exploitation conditions.
  • 03
    Attack-path mapHow prompts, identities, tools, data and systems combine into material risk.
  • 04
    Remediation and retest planPrioritized control changes with clear ownership and validation criteria.

BEFORE ADVERSARIAL TESTING

Still mapping the broader AI estate?

An AI Security Assessment can establish the exposure baseline and select the highest-value red-team targets.

Explore the Assessment

TEST BEFORE TRUST

Challenge your AI system under controlled adversarial pressure.

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