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Find and fix AI attack paths before launch.

Threat modeling, adversarial testing, and verified remediation—so your security team can sign off with confidence.

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Overview

We identify vulnerabilities in your AI systems before attackers do. Our red team exercises simulate real-world attacks and deliver actionable remediation plans with verified fixes.

Who this is for

Ideal buyer / team

Security teams, CISOs, or engineering leads preparing for production deployment

Best-fit workflow

Pre-launch or periodic security assessment of AI systems that process sensitive data or accept user input

Typical environment

Organizations deploying chatbots, copilots, or agents where prompt injection, data exfiltration, or privilege escalation are risks

Strong fit when…

When your security team needs to sign off on AI deployment but lacks AI-specific adversarial testing expertise

When to use this

  • You're deploying AI that processes sensitive data (PII, PHI, financial)
  • Your AI system accepts user input (chatbots, copilots, agents)
  • Security team needs to sign off on AI deployment but lacks AI-specific expertise
  • You want to proactively identify risks before a public incident

What you get

Threat model workshop + documentation
Red-team tests (prompt injection, jailbreaks, data exfiltration)
Tool-call abuse and privilege escalation tests
Remediation plan with prioritized fixes
Patched controls (guardrails, filters, policies)
Runbooks + alert rules for AI-specific threats

What you'll hand stakeholders

Threat model document — attack surfaces, threat actors, and risk ratings per component
Test report with reproducible cases — step-by-step repro for every finding, severity-rated
Fixes / PRs — merged code changes for guardrails, filters, and policy enforcement
Post-fix validation — retest confirmation that each finding is resolved

Reference architecture

AI security assessment scope: model, pipeline, data, and output layers

  • Input layer — prompt injection, jailbreak, and encoding attack surface
  • Tool/action layer — privilege escalation, tool-call abuse, unauthorized actions
  • Data layer — exfiltration, membership inference, training data leakage
  • Output layer — PII exposure, harmful content, guardrail bypass
  • Infrastructure layer — API key exposure, model endpoint abuse, rate-limit evasion

Typical timeline

Week 1

Scoping, threat model workshop, and attack planning.

Week 2–3

Red team execution: prompt injection, jailbreaking, data exfiltration, tool-call abuse.

Week 4

Report delivery, remediation PRs, retesting, and sign-off.

Success metrics

  • Critical and high findings remediated and retested
  • Significant reduction in successful prompt injection attacks post-hardening
  • Security sign-off achieved for production deployment
  • Documented threat model accepted by security team

Security & governance included

  • Secure testing environment
  • NDA-protected engagement
  • Evidence handling and destruction policy
  • Responsible disclosure process
  • Post-engagement cleanup

Integration-first

We connect to your stack

Azure OpenAI
AWS Bedrock
Custom LLM deployments
API gateways
SIEM systems

Frequently asked questions

Want to talk through your use case?

Book a 30-minute discovery call. We'll scope the workflow, map integrations, and outline security controls—no commitment required.

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