Machine Learning Model Security Assessment

First we agree the attacker’s position. Black-box, where they only see predictions? Grey-box, with some knowledge of architecture or confidence scores? White-box, with access to weights? Each opens up different attacks, and the confidence and probability values your API returns often decide whether extraction and inference are even feasible. We map what your endpoint leaks before we push on it.

We craft adversarial inputs to force misclassification, probe how small a perturbation your model tolerates before it breaks, and test whether the decision boundary can be gamed by an attacker who understands the domain. On the integrity side we examine how training data is sourced and validated, whether an outside party can influence it, and whether a poisoned sample or a hidden backdoor trigger could survive into production.

Here we treat the model as intellectual property and the training data as regulated information. We attempt model extraction by systematic querying, test membership inference against known and synthetic records, and evaluate model-inversion exposure where sensitive attributes could be reconstructed. For any model trained on personal data, this section speaks directly to your GDPR obligations.

You get an executive summary that frames risk in business terms and a technical report with each finding rated for impact, scored where CVSS applies, and paired with reproduction steps and a concrete fix. After remediation we retest for free to confirm the mitigations actually reduced the exposure rather than shifting it.

An attacker changes their input just enough to flip the model’s decision while a human would notice nothing wrong. A fraudulent transaction scored as legitimate, a malicious file classified as clean, a face that defeats a recognition check. We test how stable your decision boundary is and how much domain knowledge an attacker needs to cross it.

Models that retrain on user-supplied or third-party data can be steered by whoever supplies that data. A poisoning attack degrades accuracy quietly; a backdoor plants a hidden trigger that makes the model behave normally until a specific input appears. We assess your data provenance and validation and look for the gaps that let either one through.

With enough queries, an attacker can rebuild a close copy of your model, stealing the investment that went into it and gaining a local oracle to craft further attacks against. We measure how many queries your API would require, whether rate limiting and monitoring would catch the pattern, and what the returned confidence scores give away.

These attacks target the training data rather than the model. Membership inference confirms whether a specific person’s record was used in training, which for a health or finance model is itself a privacy breach. Model inversion goes further and reconstructs attributes of that data. We test both and tie the result to your data-protection exposure.

The adversarial-ML knowledge base that maps real tactics and techniques against machine learning systems. We tag findings to ATLAS so your team can place each one in a familiar threat framework.

A practical categorisation of ML-specific risks, from input manipulation to model theft. We use it as a coverage baseline and note which categories your architecture is exposed to and which it is not.

For organisations aligning to the NIST AI Risk Management Framework, the testing evidence supports your documentation. Where your model touches personal data, we map inference and inversion findings to GDPR, because a model that leaks who was in its training set is a data-protection problem, not just a technical one.

Fraud and credit models are directly adversarial: someone profits every time the model is fooled. Evasion is the headline risk, and extraction is a close second, because a stolen copy of a fraud model becomes a free testing ground for the attacker. We focus on how much an attacker can learn from declined and approved responses and whether your monitoring would notice the probing.

Here the privacy attacks dominate. A diagnostic or imaging model trained on patient data is exposed to membership inference and inversion, both of which are data-protection incidents in their own right. We weigh those findings against your GDPR and clinical obligations, not just their technical difficulty.

Models that flag malware, spam or abusive content are under constant evasion pressure from motivated attackers. We test how easily an adversary shifts an input across the decision boundary and whether a poisoning path exists through the feedback and retraining loop that many of these systems rely on.