AI Attack Surface Mapped. Vulnerabilities Ranked. Remediation Prioritised.
A systematic assessment of your AI applications against OWASP LLM Top 10 — with agent-specific attack surface mapping and a prioritized remediation roadmap.
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Standard penetration testing was designed for web applications, APIs, and network infrastructure. It was not designed for LLM applications, AI agents, or autonomous systems.
An AI application has a fundamentally different attack surface. It processes natural language instructions, executes tool calls based on model output, and makes decisions that can have real-world consequences. The OWASP LLM Top 10 defines the ten most critical vulnerability classes that emerge from this architecture.
What We Test
Our AI Security Assessment systematically evaluates your applications against all ten OWASP LLM vulnerability categories:
- Prompt injection — can an adversary manipulate your agent’s instructions via user input or data it reads?
- Insecure output handling — does your application safely handle LLM output before rendering or executing it?
- Training data poisoning — for custom-trained models, was training data integrity maintained?
- Excessive agency — do your AI agents have more tool access and permissions than they actually need?
- Sensitive information disclosure — can the model be induced to reveal training data, system prompts, or sensitive business data?
- Insecure plugin design — do your LLM plugins and tool integrations follow least-privilege principles?
The QA Gap in AI Security
Engineering and QA teams have built robust practices for testing AI application functionality — does the model return the right answer? Does the output parse correctly? Does the UI render the response as expected? These are necessary tests. They are not sufficient.
OWASP LLM Top 10 vulnerabilities are not functional failures. An application that passes every functional test can still be vulnerable to prompt injection, excessive agency exploitation, or sensitive information disclosure. A user sending crafted adversarial input is not a functional user — they are an attacker. Your QA suite does not simulate attackers.
The AI Security Assessment bridges this gap by introducing adversarial test cases that your QA suite never runs: system prompt extraction attempts, tool call injection sequences, memory manipulation probes, and model behavior consistency tests under adversarial conditions.
Global Compliance Alignment
ISO 27001, SOC 2 Type II, and the NIST AI Risk Management Framework all require documented evidence of security testing for AI-powered systems. Our assessment report is structured to map findings directly against these framework requirements — giving your compliance team the documented evidence they need for auditor review without requiring a separate compliance mapping exercise.
For engineering teams shipping features under GDPR or the EU AI Act, the AI Security Assessment provides the technical validation documentation required for high-risk AI system conformity assessments.
Engagement Phases
Discovery & Recon
AI stack inventory, threat model, external attack surface enumeration, tool connection mapping, privilege scope assessment.
Exploitation & Testing
OWASP LLM Top 10 systematic testing, prompt injection sweeps, tool poisoning simulation, agent hijacking attempts, API security assessment.
Reporting
Findings report with CVSS scores, OWASP LLM Top 10 compliance scorecard, attack surface map, prioritized remediation roadmap.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| AI Security Coverage | No AI-specific testing — OWASP Top 10 only | OWASP LLM Top 10 coverage in 2-3 weeks |
| Compliance Evidence | No documented AI security testing | Compliance scorecard + findings report for auditors |
| Time to First Findings | Weeks (traditional assessment) | Critical findings within 48 hours of engagement start |
Tools We Use
Frequently Asked Questions
What does OWASP LLM Top 10 cover?
The OWASP LLM Top 10 covers the ten most critical vulnerability classes for Large Language Model applications: prompt injection (LLM01), insecure output handling (LLM02), training data poisoning (LLM03), model denial of service (LLM04), supply chain vulnerabilities (LLM05), sensitive information disclosure (LLM06), insecure plugin design (LLM07), excessive agency (LLM08), overreliance (LLM09), and model theft (LLM10).
Who needs an AI Security Assessment?
Any enterprise that has deployed LLM-powered applications, AI agents, or AI-assisted workflows that interact with business data or make business decisions. This includes customer-facing chatbots, internal AI assistants, automated workflows using LLMs, and any AI feature added to existing applications.
What do we receive at the end?
You receive an OWASP LLM Top 10 compliance scorecard (pass/fail per category with evidence), a full technical findings report with CVSS scores and reproduction steps, an executive summary for leadership, an attack surface map showing all AI components and their connections, and a prioritized remediation roadmap.
How is this different from the Agentic Red Team Exercise?
The AI Security Assessment is a systematic audit against OWASP LLM Top 10 — broader coverage of vulnerability categories, shorter timeframe, focused on compliance evidence. The Agentic Red Team Exercise is a full adversarial simulation using the APEX methodology — deeper, longer, and focused on finding creative attack chains that a systematic audit might miss. Most enterprises start with an assessment and move to a full red team exercise.
Do I need written authorization?
Yes. Written authorization from a person with legal authority over all systems in scope is mandatory before testing begins. We provide a standard Authorization to Test (ATT) document. No testing begins without signed written authorization.
Ship Secure. Test Everything.
Book a free 30-minute security discovery call with our AI Security experts. We map your AI attack surface and identify your highest-risk vectors — actionable findings within days, CI/CD integration recommendations included.
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