How Does an Al Pentest Work?
See our step-by-step methodology for testing LLM apps, RAG pipelines, Al agents and the APIs and plugins around them.
AI Penetration Testing
Trust AI systems that behave as intended, secure, resilient, and aligned. We test how AI actually fails - so your team knows before an attacker finds out.
HOW WE TEST AI AND LLM SYSTEMS
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, mapped to MITRE ATLAS.
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 attacker behavior, guided by the OWASP Top 10 for LLM Applications, 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
Our AI Penetration Testing Methodology
From LLM penetration testing to AI security testing across your whole AI system, every engagement follows the same eight phases.
Step 01
Recon
01
How the LLM is used (RAG pipeline, etc.)
The user interface
The LLM provider
Step 02
Threat Modeling
02
STRIDE
OWASP Top 10 for LLMs
Step 03
Prompt Injection
03
Direct injection
Indirect / context injection
Multi-turn manipulation
Step 04
Input Validation & Context Filtering
04
Jailbreaking and filter bypass
Harmful or unsafe output
SQL / XSS injection in generated code
Step 01
Output Injection & Reflection
01
HTML / JS injection in the frontend
Command injection
Prompt leaks
Step 06
API & Plugin Security
06
Authorization
API key exposure
Command chaining or misuse
Step 07
Data Leakage & Privacy
07
User data
Internal documents
System prompts or embeddings
Step 08
Adversarial Input Testing
08
Unicode tricks and typosquatting
Multi-language manipulation
Poisoned docs in RAG systems
Which Service Do You Need?
AI testing overlaps with three other services. Here is how they differ, so you can choose the right one.
AI Penetration Testing
Choose this when you need to know how your models, prompts and AI agents can be abused.
Your Are Here
Application Penetration Testing
Choose this when the risk is in the app or APIs around your AI features.
Continuous Penetration Testing
Choose this when your AI features change often and need ongoing testing.
Cloud Penetration Testing
Choose this when the risk is in the cloud infrastructure your AI runs on.
AI Red Teaming vs. Traditional Penetration Testing
Al systems still run on code, APIs, and infrastructure - traditional testing still matters. Al-specific testing adds coverage for attack surfaces conventional pentesting doesn't reach.
Al Red Teaming & Penetration Testing
Attack surface
Prompts, training data, model behavior, RAG pipelines, Al agents
Frameworks referenced
NIST AI RMF, OWASP Top 10 for LLM Applications, MITRE ATLAS
catches that the other misses
Model abuse, prompt injection, data leakage from training data or context
vs
Traditional Penetration Testing
Attack surface
Code, APIs, identity, infrastructure, access controls
Frameworks referenced
OWASP Top 10, PTES, OSSTMM
catches that the other misses
Traditional injection flaws, broken auth, access-control gaps
Choose your model
Engagement Options
Choose the AI penetration testing model that fits how often your AI changes.
Continuous
Continuous AI Penetration Testing
Ongoing testing as your models, prompts and integrations change. Learn about continuous testing →
Best for
Teams shipping AI features often.
Point-in-time
Point-in-time AI Penetration Testing
A defined-scope assessment before launch or at a key milestone.
Best for
Pre-launch reviews, audits, or new model releases.
Testing Standards & Compliance
Our model security testing follows proven AI frameworks, from the first test scenario to the final report.
How we test
The methodologies that shape every enagement
NIST AI RMF
U.S. standard framework for building and using AI that’s trustworthy, secure and accountable.
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OWASP Top 10 for LLM Applications
The most common LLM risks, used to shape test scenarios.
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MITRE ATLAS
Real-world AI attack techniques, used to map threats and findings.
The Process
How Engagements Work
Every AI penetration testing engagement follows one connected process, from planning to retest.
Scope & Rules of Engagement
We agree on the models, prompts, integrations and environments in scope, test windows, and what's off-limits before testing starts.
Test the Environment
Experts map your AI architecture and threats, then test models, prompts, RAG pipelines and agents by hand, supported by the Darwin platform.
Validate Attack Paths
Every finding is confirmed as exploitable, showing how prompt injection or model abuse could lead to data exposure or unwanted actions.
Report & Prioritize
A clear report with proof for each finding, mapped to the OWASP LLM Top 10 and MITRE ATLAS, ranked by real-world impact.
Retest
After your team fixes an issue, we test again to confirm it's closed.
WHAT YOU GET
Results You Can Act On
Every engagement ends with more than a list of vulnerabilities. Here's exactly what lands in your hands when testing wraps.
Risk-ranked findings
Ordered by real-world exploitability and business impact, not just CVSS score
Exploit evidence, not a CVE list
Proof-of-concept steps showing how each finding was actually exploited
Prioritized remediation guidance
Clear next steps for your AI and engineering team, ordered by what to fix first
A retest to confirm the fix
We verify each issue is actually closed, not just marked resolved
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.
FAQ’s
We use OpenAI's or Anthropic's API, not our own model. Are we still at risk?
Yes. Most AI risk sits in how your product uses the model: prompts, plugins, RAG pipelines and APIs. Our LLM penetration testing covers prompt injection testing, data leakage and RAG misconfigurations at this integration layer, not just the model.
How is AI penetration testing different from a normal web app pentest?
A standard web application pentest doesn't cover prompt injection, model abuse or training data exposure. AI penetration testing adds these AI-specific attack paths and tests them together with the APIs and infrastructure around your AI system.
Can you test our RAG pipeline, not just the model itself?
Yes. GenAI testing covers the ingestion, retrieval and orchestration layers of your RAG pipeline, not just the model endpoint.
Do you test AI agents that take autonomous actions?
Yes. We test how agents use tools, plugins and permissions, including prompt injection that pushes an agent into actions it should not take.
What access do you need from us?
Scoped access to your model endpoints, APIs or staging environment, agreed together during scoping. Production credentials are not needed unless production testing is agreed.
Do you test in production or staging?
Either, depending on what you approve during scoping. Production testing is scheduled separately and has clear limits.
What do we receive when testing is complete, and is retesting included?
A risk-ranked findings report with exploit evidence, prioritized remediation guidance, and a retest to confirm each fix closes the gap.
Is AI penetration testing a one-time engagement or ongoing?
Both are available: a single point-in-time engagement, or continuous testing that follows your release schedule.
Game Changing Resources
Case studies, threat intel, and insights on what continuous, human-led testing looks like in practice.
Evolve Security Joins Anthropic’s Cyber Verification Program
Evolve Security joins Anthropic's Cyber Verification Program, giving OSOC operators full, unthrottled Claude access for authorized red team and AI pen testing.
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AI AppSec Champions: How to Build Internal AI Security Expertise Before It’s Too Late
AI security risks like prompt injection and LLM data flows require a new kind of champion. Discover how the AI AppSec Champion model helps engineering teams catch vulnerabilities before they become breaches.
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Matt Sharp, CISO, Xactly Corp
In this customer spotlight, Matt Sharp, CISO at Xactly, discusses how AI is reshaping the threat landscape, why Continuous Penetration Testing has become essential for modern security programs, the importance of human-in-the-loop validation, and how Evolve Security helps prioritize the exposures that matter most to reduce business risk.
Webinar: Large Language Models 4 NØØBs
EvolveSec | July 2022 | J. R. Hernandez
Executive Dinner: Offensive Security in the Age of Frontier AI
Carnevor | Milwaukee’s Premier Steakhouse, Milwaukee, United States
Black Hat & Def Con 2026
Las Vegas
Is Anybody Safe? Adaptability in the Age of AI Disruption - with Mark Carney
Is any security company safe from AI disruption? Evolve Security CEO Mark Carney's answer: no and that's the wrong question anyway.
Pen Testing in the Age of AI: Man + Machine w/ Paul Petefish
AI is changing security fast. But is it replacing pentesters, or just giving them a powerful new co-pilot?
