AI Penetration Testing

Trust AI systems that behave as intended, secure, resilient, and aligned. Secure AI and LLM implementations by validating models, pipelines, and integrations against real-world abuse. Our team tests how AI actually fails, so it can be trusted to operate safely at scale.

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AI/LLM Testing Overview

A hybrid approach blending AI governance, threat modeling and adversarial testing of models, including repeatable tests and remediation validation.

NIST AI RMF

Align AI systems to NIST AI RMF with structured risk identification, governance controls, and continuous validation.

Methodology:

Map AI use cases to NIST AI RMF core functions and risk categories
Assess governance, accountability, and model lifecycle controls
Evaluate data quality, bias, transparency, and explainability risks
Test model robustness, security, and operational resilience
Deliver prioritized remediation aligned to compliance and business impact

Threat Modeling

Identify and prioritize AI-specific threats across models, data, infrastructure, and deployment environments.

Methodology:

Decompose AI architecture, data flows, and trust boundaries
Identify AI-specific threats (model abuse, poisoning, prompt injection)
Assess attacker impact, likelihood, and business risk
Map threats to controls and mitigation strategies
Validate findings through attack simulation and expert review

AI Penetration Testing

Simulate real-world attacks to uncover exploitable weaknesses in AI models, APIs, and pipelines.

Methodology:

Test AI models, APIs, plugins, and orchestration layers
Execute prompt injection, data leakage, and model abuse scenarios
Assess authentication, authorization, and input validation controls
Evaluate training data exposure and inference risks
Deliver actionable findings with prioritized remediation guidance

NIST AI RMF

Align AI systems to NIST AI RMF with structured risk identification, governance controls, and continuous validation.

Methodology:

Map AI use cases to NIST AI RMF core functions and risk categories
Assess governance, accountability, and model lifecycle controls
Evaluate data quality, bias, transparency, and explainability risks
Test model robustness, security, and operational resilience
Deliver prioritized remediation aligned to compliance and business impact

Threat Modeling

Identify and prioritize AI-specific threats across models, data, infrastructure, and deployment environments.

Methodology:

Decompose AI architecture, data flows, and trust boundaries
Identify AI-specific threats (model abuse, poisoning, prompt injection)
Assess attacker impact, likelihood, and business risk
Map threats to controls and mitigation strategies
Validate findings through attack simulation and expert review

AI Penetration Testing

Simulate real-world attacks to uncover exploitable weaknesses in AI models, APIs, and pipelines.

Methodology:

Test AI models, APIs, plugins, and orchestration layers
Execute prompt injection, data leakage, and model abuse scenarios
Assess authentication, authorization, and input validation controls
Evaluate training data exposure and inference risks
Deliver actionable findings with prioritized remediation guidance

NIST AI RMF

Align AI systems to NIST AI RMF with structured risk identification, governance controls, and continuous validation.

Methodology:

Map AI use cases to NIST AI RMF core functions and risk categories
Assess governance, accountability, and model lifecycle controls
Evaluate data quality, bias, transparency, and explainability risks
Test model robustness, security, and operational resilience
Deliver prioritized remediation aligned to compliance and business impact

Threat Modeling

Identify and prioritize AI-specific threats across models, data, infrastructure, and deployment environments.

Methodology:

Decompose AI architecture, data flows, and trust boundaries
Identify AI-specific threats (model abuse, poisoning, prompt injection)
Assess attacker impact, likelihood, and business risk
Map threats to controls and mitigation strategies
Validate findings through attack simulation and expert review

AI Penetration Testing

Simulate real-world attacks to uncover exploitable weaknesses in AI models, APIs, and pipelines.

Methodology:

Test AI models, APIs, plugins, and orchestration layers
Execute prompt injection, data leakage, and model abuse scenarios
Assess authentication, authorization, and input validation controls
Evaluate training data exposure and inference risks
Deliver actionable findings with prioritized remediation guidance

Powered By Our Darwin Attack Platform

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WHAT TO EXPECT?

Onboarding Platform

Align Objectives & Outcomes

Ongoing Testing / PIT Testing

Quarterly Service Review

Ongoing Testing Dashboard

Why Evolve Security?

01 CTEM Maturity Model

Evaluate CTEM maturity and strengthen resilience by assessing readiness against evolving adversary techniques and attack vectors.

02 CPT Market Leader

Offensive SOC and engineering experts drive measurable outcomes, guiding every phase from exposure discovery to remediation.

03 Award Winning Platform

Darwin Attack platform validates security controls and precisely pinpoints prioritized vulnerabilities across dynamic environments.

04 OffSec Operations Center (OSOC)

Agile bullpen of offensive testers rapidly adapts tactics, mirroring adversaries as threats and business priorities shift.

05 Trusted Methodologies

Industry-trusted methodologies including OWASP, OSSTMM, PTES, and NIST ensure disciplined, comprehensive penetration testing rigor.

06 Customized Simulations

Tailored simulations reflect an industry’s distinct threats, adversary behaviors, and mission-critical attack scenarios.

Game Changing Resources

Dive into our game changing resource library that delivers novel thought leadership and real-time perspectives that reimagine how organizations design, manage and elevate offensive security programs

ROI on Continuous Penetration Testing (CPT)

ROI on Continuous Penetration Testing (CPT): Annual Penetration Testing Is Failing Modern Security Programs

The CTEM Chronicles: A Fictional Case Study of Real-World Adoption

Explore a fictional case study of Lunera Capital, a mid-sized financial firm that adopted Continuous Threat Exposure Management (CTEM). See how theory meets practice and how this company goes from chaos to clarity in cybersecurity.

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