01.2
Full-Stack AI Security Testing
AI Penetration Testing
Test your AI systems from the model to the cloud
We attack your models, applications, agents, and cloud infrastructure the way real adversaries would, then show what’s exploitable and how to fix it.
The Problem
Past the Jailbreak
The real question isn’t just whether someone can trick a model into going off-script. It’s whether an attacker can turn your AI against you: pulling protected data, breaking tenant isolation, firing off an action it was never meant to take, or pivoting into the cloud it runs on.
Prompt-only evaluations and scanners stop at the model’s edge. We attack the full stack and show which weaknesses, alone or chained across layers, turn into business impact.
This is for you if your team is saying:
- Our assistant can read customer records, and we’ve never tested who else could make it do that.
- The agent calls real tools, and we don’t know how far a poisoned document could push it.
- Our scanner came back clean, but nobody has tried to chain anything.
Full-Stack Coverage
Four Layers, One Engagement
Attacks rarely stay in one place. A prompt becomes a tool call, the tool call becomes a cloud credential, and the credential opens your data. We test every layer of the system in scope, then follow the paths that cross between them.
Model
Prompt injection, jailbreaks, guardrail and filter evasion, and harmful or off-policy output. Where you host or fine-tune the model, add data poisoning, extraction, and inversion.
Application
The web app and APIs wrapped around the model: authentication, access control, tenant isolation, SSRF, IDOR, cross-site scripting, and sensitive data leaking through responses.
Agents, Tools, and Retrieval
The tools, plugins, and MCP servers your AI can call, plus its retrieval sources and memory, tested for tool abuse, excessive agency, goal hijacking, poisoned content, and privilege escalation.
Infrastructure
The cloud accounts, identities, secrets, data pipelines, and CI/CD behind the AI, tested for privilege escalation, misconfiguration, exposed model artifacts, and runaway cost.
What We Test
The Threats We Hunt
Coverage is risk-based. Some categories won’t apply to your system, and the report states which ones did.
- Direct and indirect prompt injection
- Jailbreaks, guardrail and filter evasion, and bypass of system instructions
- Harmful, false, or off-policy responses a customer-facing assistant can be pushed into
- Data poisoning, model extraction, inversion, and training-data leakage, where you host or fine-tune the model
- Sensitive-data exposure and cross-user or cross-tenant leaks
- Broken authentication, access control, sessions, and tenant isolation
- Web and API flaws around the AI, such as SSRF, IDOR, and cross-site scripting
- Unsafe output handling that injects into downstream systems
- Excessive agency, tool abuse, goal hijacking, and rogue actions
- Model, API, plugin, and Model Context Protocol (MCP) integration boundaries
- Poisoned RAG sources and vector stores, manipulated retrieval, and broken provenance
- Memory tampering and unsafe persisted state
- Cloud misconfiguration, identity and privilege escalation, and exposed secrets
- Tampering with data pipelines, CI/CD, and model artifacts
- Denial-of-wallet, rate, cost, and resource abuse
- Logging and detection gaps that block reconstruction of a material event
How We Test
Scope. Recon. Exploit. Validate.
Scanners replay known patterns. The paths that hurt are specific to your system, and those hard-to-find chains take a human to uncover. Every phase runs inside defined safety controls, you get regular status updates, and anything critical reaches you as soon as we confirm it, not weeks later in the report.
Scope and Plan
We settle which models, applications, agents, and infrastructure are in scope, plus business impact, prohibited actions, and stop conditions. Nothing is touched before the rules of engagement are in place.
Recon and Threat Model
We chart each reachable surface and build a threat model before a single attack runs: the model, the application and its APIs, agents and tools, retrieval and data, identities, and the cloud beneath.
Exploit and Chain
Testers work each layer by hand while rate-limited automation replays known injection and jailbreak patterns at scale. Then we chain weaknesses across layers. For example, a poisoned document steers an agent into an over-permissioned tool whose cloud role exposes a secret.
Validate and Debrief
We reproduce each chain, confirm impact, cut false positives, and name the safeguard or design assumption that gave way. Then we walk your engineers through the fixes in a remediation workshop.
Informed By
- OWASP Top 10 for LLM Applications
- OWASP Top 10 for Agentic Applications
- OWASP Machine Learning Security Top 10
- MITRE ATLAS
- NIST AI Risk Management Framework
- NIST adversarial ML taxonomy
Frameworks guide the method. Your architecture and business consequences drive the findings.
Key Outcomes
Know What Breaks and Why
Expose Exploitable Paths
See each validated chain from first input to business impact, step by step, across every layer it crosses.
Validate Real Impact
Every finding is reproduced and linked to the data, decision, or action in the line of fire, so severity reflects consequence.
Prioritize What to Fix
Get short- and longer-term remediation ranked by the risk each fix removes, not by how alarming it sounds.
Target Root Causes
Learn which safeguard or design assumption broke, so the fix lands on the cause rather than a symptom.
Deliverables
Built for Engineers and Executives
Executive Risk Insight
A plain account of what we tested, what broke, and why it matters, because executives need the bottom line.
Reproducible Technical Findings
Affected assets and the reasoning behind each severity rating, plus screenshots, requests, prompts, traces, and proof-of-concept artifacts, with sensitive data redacted, each tagged with its OWASP and MITRE ATLAS reference, because engineers fix what they can repeat.
Cross-Layer Attack Chains
Each validated chain from initial input to impact, showing how weaknesses in the model, application, agents, and cloud compound, because real attacks rarely rely on a single flaw.
Root-Cause and Control-Gap Analysis
The safeguard or design assumption at fault, because patching symptoms invites repeat findings.
Remediation Plan and Workshop
Ranked short- and longer-term priorities plus a technical workshop with the team doing the work, because findings only matter once they’re fixed.
Method, Scope, and Limitations
What was tested, how, and what wasn’t, because confidence depends on knowing where the testing stops.
Go further
Running models on your own hardware? Pair this with a Self-Hosted AI Security Assessment. Want to find out if a realistic attacker could reach one specific goal, and whether your team would notice? Explore AI Red Team & Readiness. Keeping pace with frequent changes? Add Continuous AI Penetration Testing.
Common Questions
AI Pen Testing Explained
What is AI penetration testing, exactly?
It’s hands-on attack work against a working AI system at every layer: the model, the application and APIs around it, the agents, tools, and data it reaches, and the cloud infrastructure it runs on. It aims to cover one defined system thoroughly and confirm what can really be exploited, and at what cost.
What kinds of AI systems do you test?
Customer-facing chatbots, internal assistants and copilots, action-taking agents, document-processing systems, RAG workflows, and custom or fine-tuned models, in production or pre-production, along with their tools, plugins, MCP integrations, and cloud. Each needs a clear technical owner and enough stability to test in staging or during a controlled production window.
How is this different from an architecture review?
A review studies the design without attacking it. Penetration testing runs real attacks against the live system to confirm which weaknesses can actually be exploited. If you don’t yet know what to test, start with the AI Threat & Exposure Assessment; it’s built to scope this engagement.
Should I start with a pentest or a red team?
Match it to the question. For thorough coverage of one defined AI system, this is the answer. For a mature program asking whether a realistic attacker could reach one specific goal, and whether you’d spot it, look at AI Red Team & Readiness. When in doubt, start here.
Can this stand in for our annual pentest?
No. It covers the AI system and the application and cloud infrastructure it runs on, not your wider network or every system you own. Your conventional pentest covers different ground, and neither one substitutes for the other. If you need both, we’ll help you coordinate scope.
Our model comes from a third-party provider. Can you still test?
Yes. We focus on what you control, such as your prompts, retrieval, application, tools, permissions, integrations, and cloud, and on how they call the provider’s model, all within the provider’s own published terms.
Will testing disrupt production?
Testing follows defined rules of engagement, and staging or test environments come first. If production is in scope, we work within set rate, cost, and data limits and clear stop conditions, and both sides name emergency contacts.
How long does an engagement take?
Effort tracks the system’s reach: whether it’s a single chatbot or a full stack of agents, retrieval pipelines, custom models, and cloud accounts, and how much configuration or source code we can examine. Your statement of work confirms the timeline.
Does this help with compliance?
It supports it, but it doesn’t make you compliant, and certifying compliance isn’t something we do. Each finding is tagged with its OWASP and MITRE ATLAS reference, and the report records what we tested, what we found, and, if retesting is in scope, whether your fixes held. Bring it to auditors, customers, insurers, and regulators.
Will you retest once we’ve fixed things?
Yes, if retesting is part of your engagement. We check that each documented attack path is closed and give you a retest letter to share with customers, auditors, and procurement teams. The letter states what was retested and the result; it isn’t a certification.
Your Move
Tell Us Where to Aim
Point us at the AI system you need tested. We’ll scope it layer by layer around the business impact that matters most to you.