Offensive Security & VAPT

AI/ML Model Security Testing

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.

Request this service All services

MITRE ATLAS NIST AI RMF OWASP ML Security Top 10

Typical timeline

8–12 business days

Engagement model

Grey / black / white box

How it runs

Kickoff → test → report → re-test

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.

At a glance

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

Get a scope & quote

Coverage

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.

Outcomes

What you get

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

Methodology

How the engagement runs

Profile

Map model type, training pipeline, inputs/outputs and access surface.

Attack

Adversarial examples, extraction, inversion and poisoning attempts.

Assess

Quantify robustness, privacy leakage and pipeline exposure.

Report

Findings mapped to MITRE ATLAS with hardening guidance.

MITRE ATLAS

Attacks Mapped

Grey/Black-box

Test Coverage

Extraction &

Inversion Tested

Pipeline

Poisoning Assessed

Deliverables

What lands in your inbox

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

Why A5

Why teams pick us

Manual-first, not scan-first

Senior testers hand-craft test cases for your business logic - scanners only set the baseline.

Proof, not guesses

Every finding ships with a working proof-of-concept and exact reproduction steps.

Fix-focused reporting

Remediation with code and config examples, not just a CVSS number and a shrug.

Re-test included

We verify your fixes and issue a clean report - closure, not just discovery.

FAQ

Frequently asked

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.