# AI/ML Model Security Testing

> Adversarial evasion, model extraction, inversion & data poisoning - the model layer, not just the app.

Prompt injection is only one layer. The models underneath - classifiers, fraud engines, recommenders, computer-vision and fine-tuned LLMs - are attackable in their own right. We run adversarial ML testing: evasion attacks against detection models, model extraction and inversion, membership inference, and training-data/pipeline poisoning, mapped to MITRE ATLAS.

## Outcomes

- Adversarial evasion testing against classifiers and detection models
- Model extraction and model inversion attack simulation
- Membership-inference and training-data privacy-leakage testing
- Training-pipeline and data-poisoning risk assessment

## Process

01. **Profile** - Map model type, training pipeline, inputs/outputs and access surface.
02. **Attack** - Adversarial examples, extraction, inversion and poisoning attempts.
03. **Assess** - Quantify robustness, privacy leakage and pipeline exposure.
04. **Report** - Findings mapped to MITRE ATLAS with hardening guidance.

## Deliverables

- Model security report
- MITRE ATLAS mapping
- Robustness & privacy-leakage scoring
- Hardening recommendations

## Standards

MITRE ATLAS, NIST AI RMF, OWASP ML Security Top 10


## Overview

Prompt injection targets the application wrapped around a model. Adversarial ML targets the model itself - its decision boundaries, training data and weights - and it applies just as much to classic classifiers, fraud engines and computer-vision models as it does to LLMs.

We run evasion, extraction, inversion and poisoning attacks against your production models, quantify how easily an attacker can degrade, steal or manipulate them, and map every finding to MITRE ATLAS.


## What we cover

### Adversarial Evasion
Crafting inputs that flip a classifier's or detector's decision without human-visible changes.

### Model Extraction & Inversion
Rebuilding or reverse-engineering a proprietary model, or its training data, via query access alone.

### Membership Inference
Testing whether an attacker can determine if specific records were used to train the model.

### Training-Data Poisoning
Assessing whether the training/fine-tuning pipeline can be tampered with to implant backdoors.


## FAQ

**Do you need the model weights?**
Grey-box access (API plus some pipeline visibility) gives the deepest coverage, but we also run black-box adversarial testing against a hosted inference endpoint alone.

**How is this different from AI/LLM Security Testing?**
AI/LLM Security Testing covers the application layer around a model - prompts, agents, RAG. This service attacks the model itself: its decision boundaries, training data and weights.

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