Can your machine learning system be manipulated to make the wrong decision?
ML AST - Machine Learning
Secure the models that make your most critical decisionsMachine learning systems now underpin critical business decisions—from credit approvals and fraud detection to pricing, recommendations, and risk scoring—but unlike traditional applications, they learn from data and influence outcomes at scale. Despite this, most organizations still depend on conventional web application penetration testing, leaving a dangerous security gap. EntersOft’s ML AST (Machine Learning Application Security Testing) closes this gap by protecting machine learning driven applications against sophisticated attacks that traditional security testing cannot detect.What Is ML AST?
ML AST is a specialized discipline under Entersoft’s AI Application Security Testing (AIAST) framework.
Where SAST examines source code and DAST tests web application behavior, ML AST evaluates the intelligence layer itself the data pipelines, features, models, and inference mechanisms that drive automated decisions.
It answers a simple but critical question:
Why ML Security Needs Its Own Testing
ML models introduce a new attack surface where behavior can be manipulated without breaking the app.
Bypass fraud and anomaly detection
Skew predictions and scoring
Extract sensitive information from training data
Steal proprietary models through inference abuse
Poison training data to influence model decisions
Trigger unauthorized actions through adversarial inputs
ML AST exists to find these issues before attackers do.
What We Secure in ML-Driven Applications
ML AST focuses on systems where decisions are automated, probabilistic, and data-driven. This includes
Credit Scoring & Risk Engines
Assess creditworthiness and automate financial risk decisions at scale.
Fraud Detection & Monitoring
Identify suspicious transactions and prevent real-time financial fraud.
Recommendations & Personalization
Deliver personalized content, products, and user experiences using ML models.
Pricing & Demand Forecasting
Optimize pricing strategies and predict demand using data-driven models.
Anomaly Detection Systems
Detect unusual behavior across SOC, IoT, and industrial environments.
Built for Governance and Compliance
ML AST integrates with enterprise governance and compliance programs to secure machine-learning systems while aligning with trusted global frameworks for consistent risk management and accountability.
- OWASP ML Top 10: Addresses the most critical machine learning vulnerabilities, including data poisoning, model evasion, inference attacks, and training data leakage.
- NIST AI Risk Management Framework (AI RMF): Supports structured identification, assessment, and mitigation of AI risks across the full ML lifecycle—from design and training to deployment and monitoring.
- ISO/IEC 23894 & ISO/IEC 42001: Enables alignment with international AI risk and management system standards, supporting responsible AI adoption and enterprise-wide governance.
- Model Risk Management (MRM): Provides visibility into model behavior, misuse, and failure modes for internal risk and audit teams.
- Lifecycle Security Coverage Background: Secures ML systems across training, validation, deployment, and inference—not just the application layer.
- Explainability & Accountability Support: Helps organizations demonstrate control, oversight, and responsible use of automated decision systems.
How ML AST Works
Every ML AST engagement begins with understanding how your model thinks.
We map the full data and decision flow from ingestion and feature engineering to model inference and output consumption. From there, we simulate real-world attacks designed to manipulate model behavior rather than exploit code.
These tests evaluate how resilient your model is when:
- Inputs are crafted to evade detection
- Training or inference data is subtly poisoned
- APIs are probed to extract model logic
- Features are manipulated to trigger biased outcomes
The goal is not theoretical risk it’s demonstrable impact.
Security Beyond the Model
ML AST evaluates not just the model, but the attack surface around it—where real-world adversaries operate. Most ML breaches occur through exposed interfaces, weak controls, and operational blind spots, not the algorithm alone.
- Inference APIs ∓ Access Abuse
Identify risks where attackers exploit exposed inference endpoints through unauthorized access, prompt abuse, or automated querying. - Rate Limiting & Cost Attacks
Detect weaknesses that allow attackers to drain compute resources, trigger denial-of-service conditions, or inflate operational costs through excessive inference calls. - Logging, Traceability & Blind SpotsAssess whether malicious inputs, abnormal outputs, and model misuse can go undetected due to insufficient logging or lack of explainability.
- Audit & Regulatory Exposure
Uncover gaps that leave ML systems vulnerable during regulatory reviews, investigations, or post-incident audits due to missing evidence or weak governance controls.
Why Enterprises Choose Entersoft
Entersoft applies offensive security expertise to test ML systems like real attackers.
AI Native Testing
AI-native security testing beyond web and API scopes
Impact Driven
Practical, business-impact-driven findings
Actionable Fixes
Clear remediation guidance tailored to ML systems
Audit Ready
Reports suitable for engineering teams and auditors
When Do You Need ML AST?
You need ML AST if your organization uses machine learning to:
Automate decisions:
Make real-time or large-scale decisions without manual intervention.
Reduce human oversight:
Rely on models to operate with limited review or supervision.
Influence critical outcomes:
Impact financial performance, operational efficiency, or regulatory compliance.
Process sensitive or regulated data:
Handle personal, financial, or confidential data within ML workflows.
