Background

AI & MLSecurity Testing

Most penetration testing firms still don't offer AI security assessment. I combine offensive security expertise with AI/ML knowledge to test LLM applications against the OWASP Top 10 for LLM Applications 2025, covering prompt injection, jailbreaking, model extraction, and training data poisoning before attackers or auditors find them first.

30%
AI projects abandoned due to governance by 2027
Gartner 2025
Aug '26
EU AI Act high-risk system enforcement
EU AI Act Timeline
10
Critical LLM risk categories tested
OWASP LLM Top 10 2025

OWASP Top 10 for LLM Applications 2025

The 2025 edition defines the most critical security risks to LLM applications, from prompt injection and jailbreaking to model extraction and training data poisoning. I test every category with offensive techniques, not checklist reviews.

01

Prompt Injection

Manipulating LLM via crafted inputs

02

Insecure Output

Insufficient validation of model outputs

03

Training Data Poisoning

Compromising training data integrity

04

Model Denial of Service

Resource exhaustion attacks

05

Supply Chain

Third-party model and data risks

06

Sensitive Info Disclosure

Leaking confidential data

07

Insecure Plugin Design

Vulnerable LLM extensions

08

Excessive Agency

Overprivileged LLM capabilities

09

Overreliance

Unchecked dependence on outputs

10

Model Theft

Unauthorized model extraction

Comprehensive AI Security Testing

A three-pronged offensive assessment aligned to NIST AI RMF (AI 100-1), NIST Generative AI Profile (AI 600-1), and MITRE ATLAS for adversarial ML, covering application layer, model integrity, and data pipeline security.

Application Layer

Prompt injection attacks
Output validation bypass
Context window manipulation
System prompt extraction
Jailbreak techniques
Plugin security assessment
API abuse and rate limiting
Authentication bypass

Model Layer

Model extraction attacks
Adversarial input generation
Model inversion techniques
Membership inference attacks
Model poisoning detection
Backdoor identification
Bias and fairness testing
Model card validation

Data Pipeline

Training data poisoning
Data leakage identification
PII exposure in training data
Fine-tuning security
Vector database security
Embedding manipulation
RAG pipeline vulnerabilities
Supply chain dependencies

Why Offensive AI Testing Matters

Automated scanners can't find prompt injection or model extraction. EU AI Act enforcement begins August 2025 for prohibited practices, with high-risk system requirements in August 2026, organizations need evidence-based security assessments, not governance paperwork alone.

Prompt Injection & Jailbreaking

Bypassing guardrails to extract data, execute tools, or override system instructions

Model Extraction

Reconstructing proprietary models through API queries, stealing your AI investment

Training Data Poisoning

Corrupting model behavior via compromised fine-tuning or RAG data sources

Regulatory Exposure

EU AI Act, NIST AI RMF, and emerging frameworks requiring demonstrable security controls

AI Systems I Test

Where traditional pentesters stop at web apps, I apply MITRE ATLAS adversarial ML techniques and real-world prompt injection chains to the systems your business actually depends on.

LLM Applications

Chatbots
Code assistants
Content generators
Customer service AI

ML Models

Classification systems
Recommender systems
Fraud detection
Computer vision

AI Infrastructure

Vector databases
Model APIs
Fine-tuning platforms
RAG systems

Ready to Secure Your AI Systems?

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