Adversarial machine learning: How attackers poison your models

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Executive Summary

Adversarial Machine Learning (AML) has emerged as a critical threat vector for organizations that rely on AI systems. Among the various adversarial techniques, model poisoning represents one of the most insidious attack strategies. This comprehensive report synthesizes recent research, real-world incidents, and emerging best practices to equip Chief Information Security Officers (CISOs) and security teams with actionable intelligence on this evolving threat landscape.

Our analysis reveals that poisoning attacks manipulate the integrity of AI systems by corrupting training data, introducing backdoors, or tampering with model updates during the learning process. Unlike traditional cyberattacks that target system vulnerabilities, poisoning attacks exploit the fundamental learning mechanisms of AI systems themselves. These attacks can be stealthy, persistent, and catastrophic, bypassing conventional security controls and creating hidden vulnerabilities that may remain dormant until exploited.

Research indicates attackers can achieve significant model manipulation by altering as little as 0.1% of training data, with effects ranging from degraded performance to complete system compromise. As AI adoption proliferates across industries—from financial services and healthcare to critical infrastructure—the attack surface for adversaries is expanding exponentially.

Key findings from this report include:

• Training data poisoning is the most prevalent attack vector, appearing in over 50% of documented cases, followed by backdoor attacks that create hidden triggers in seemingly well-functioning models.

• Organizations in financial services, healthcare, cybersecurity, and critical infrastructure face heightened risk due to their reliance on AI for sensitive operations.

• Most effective defense mechanisms combine data sanitization, robust training algorithms, real-time monitoring, and comprehensive governance.

• Large organizations are leading the implementation of AI security controls, with 78% of enterprises now using AI in at least one business function, yet gaps persist in poisoning-specific defenses.

• 43% of organizations report they have implemented specific controls to mitigate poisoning attacks, but adoption of comprehensive, defense-in-depth practices remains inconsistent and reactive.

For security leaders, the implications are clear: as AI adoption continues to accelerate—with GenAI usage growing from 33% to 71% of organizations between 2023 and 2025—robust defenses against model poisoning must become a core component of enterprise security frameworks. Traditional cybersecurity must evolve to address the unique vulnerabilities inherent in AI supply chains, development pipelines, and operational deployments.

This report provides a detailed analysis of attack methodologies, case studies, defense strategies, governance frameworks, and practical recommendations to help security professionals develop resilient countermeasures against this sophisticated and rapidly evolving threat.

Understanding Adversarial Machine Learning and Model Poisoning

The Fundamentals of Adversarial Machine Learning

Adversarial Machine Learning (AML) encompasses techniques that exploit vulnerabilities in machine learning (ML) models, either during training or inference. What makes these attacks particularly concerning is that they target the core components of AI systems—data and models—rather than conventional IT infrastructure vulnerabilities such as network devices or operating systems. AML attacks strike at the heart of what makes AI systems valuable: their ability to autonomously learn from data.

At its essence, AML represents a battle between ML systems designed to learn patterns from data and adversaries attempting to manipulate those patterns. The threat landscape has evolved dramatically since early academic research first explored adversarial examples between 2004 and 2006. Early studies focused largely on simple classifiers like Support Vector Machines (SVMs) and explored how minimal perturbations could cause misclassifications. By 2014, research had advanced to show how deep neural networks could be highly vulnerable to crafted adversarial inputs, sparking a wave of interest across academia and industry.

The period between 2015 and 2020 witnessed the commercialization of AI across industries. Correspondingly, adversarial research shifted from theoretical vulnerabilities to real-world exploitations. In 2017, the first demonstrations of poisoning attacks against security systems and autonomous vehicles were made public. By 2023–2025, with the explosion of generative AI and large-scale deployment of AI systems in critical sectors, adversarial ML moved from a niche research topic to a boardroom concern.

Today, adversarial attacks are broadly categorized into several primary types:

• Evasion attacks: Occur at inference time when an attacker crafts specific inputs that cause the model to produce incorrect outputs. For example, subtly modifying a road sign in an autonomous vehicle’s vision system to cause misinterpretation.

• Poisoning attacks: Occur during the training phase when an attacker corrupts training data or influences the learning process itself, leading to compromised model behavior.

• Model extraction attacks: Involve an attacker repeatedly querying a model to reconstruct its internal logic or replicate its functionality.

• Privacy attacks: Aim to extract sensitive information about the training data, such as personal identifiers, even when only model outputs are accessible.

Among these, poisoning attacks represent one of the most deeply concerning vectors because they compromise models at their foundation—during the critical phase of learning itself. Poisoned models may pass traditional testing and appear functional, all while harboring hidden vulnerabilities.

Model Poisoning: A Targeted Attack on AI Foundations

Model poisoning attacks specifically target the training phase of machine learning models, aiming to embed persistent vulnerabilities within the model itself. Unlike evasion attacks that exploit a model’s behavior during operation, poisoning attacks fundamentally alter the model's parameters, decision boundaries, or generalization properties during development.

Our analysis of 40 research studies on poisoning attacks reveals several key insights:

• Attack prevalence: Training data poisoning was the most common attack type, documented in 21 of 40 studies, followed by backdoor attacks (9 studies), clean-label attacks, and model replacement techniques.

• Manipulation techniques: Attackers employ diverse strategies such as:

  • Label flipping: Changing the labels of specific training samples.

  • Feature manipulation: Altering key characteristics of input features to embed bias.

  • Gradient-based attacks: Tampering with the mathematical optimization process that updates model weights.

• Attack efficiency: Studies show attackers can significantly manipulate models by altering between 0.1% and 30% of the training data, with successful attacks often requiring changes to less than 5%.

• Model vulnerability: A broad range of model types have shown susceptibility, including neural networks, SVMs, decision trees, ensemble methods, and linear regression models.

The fundamental mechanisms behind poisoning attacks include:

• Data pollution: Introducing maliciously crafted samples into training datasets to bias learning.

• Label manipulation: Mislabeling existing legitimate data points to subtly distort decision boundaries.

• Gradient manipulation: Exploiting vulnerabilities in the training algorithm to cause specific parameter updates that favor attacker goals.

The objectives of poisoning attacks vary widely:

• Global degradation: Reducing overall model accuracy across many classes or outputs.

• Targeted attacks: Forcing specific misclassifications while preserving general model performance.

• Backdoor insertion: Embedding hidden triggers that cause controlled misbehavior when specific conditions are met.

For example, in financial fraud detection systems, an attacker might introduce poisoned transactions that resemble legitimate behavior but contain small anomalies, leading to systematic blind spots when similar fraudulent behavior occurs later. In medical AI, poisoned datasets could cause a diagnostic model to systematically miss early-stage cancers while preserving performance for other conditions.

These attacks exploit a critical truth about machine learning: models are only as trustworthy as the data and processes used to create them. Subtle manipulations at the foundation of learning can create devastating and long-lasting vulnerabilities.

Types of Poisoning Attacks

Research and real-world incidents point to several distinct categories of poisoning attacks, each with different goals, tactics, and levels of sophistication.

1. Availability (Indiscriminate) Attacks

Availability attacks aim to degrade the overall performance of a machine learning model, making it less accurate, less reliable, or outright dysfunctional across a broad range of inputs. These attacks are sometimes referred to as "indiscriminate poisoning" because they do not target specific classes or outputs—they simply seek to reduce the model’s effectiveness.

Examples:

• Spam Filters: A 2018 study demonstrated how attackers could poison spam detection models by inserting emails with a mix of legitimate and malicious content into training datasets. The result was a gradual erosion of the model’s ability to distinguish spam from non-spam, increasing the false positive and false negative rates.

• Medical Imaging Systems: Researchers showed that introducing subtly corrupted images into training sets for AI diagnostic tools could lower diagnostic accuracy across multiple conditions such as pneumonia, fractures, and cancer detection. Even minor reductions in model confidence led to significantly degraded clinical decision-making.

• Industrial Control Systems: In simulated experiments, poisoning anomaly detection systems used in energy and manufacturing sectors resulted in the systems missing subtle early indicators of mechanical failure, thereby increasing the risk of catastrophic breakdowns.

Risks: Availability attacks are particularly concerning for safety-critical systems, where even a slight degradation in detection or classification capabilities can lead to physical harm, operational disruptions, or financial loss.

2. Integrity (Targeted) Attacks

Integrity attacks are more sophisticated. Instead of degrading overall model performance, these attacks seek to cause specific misclassifications while preserving the model’s general accuracy. The goal is to embed subtle vulnerabilities that can be exploited later without raising suspicion during validation or operational monitoring.

Examples:

• Healthcare Diagnostics: Jagielski et al. (2018) conducted experiments where small perturbations in a fraction of training data led models to misdiagnose certain diseases (e.g., misclassifying malignant tumors as benign) without affecting diagnostic accuracy for other conditions.

• Financial Risk Models: Targeted poisoning has been shown to cause loan approval models to consistently misclassify applicants from specific demographic groups or risk categories, opening up potential for systemic fraud or bias.

• Insurance Fraud Detection: In 2024, researchers demonstrated that poisoning historical claims datasets could cause AI fraud detectors to underweight fraudulent patterns involving new types of medical treatments.

Risks: Targeted attacks are extremely difficult to detect because models behave normally in most circumstances, masking the compromised behavior until very specific conditions are met.

3. Backdoor (Trojan) Attacks

Backdoor attacks introduce hidden "triggers" into a model such that the model behaves normally unless the trigger is present. When activated, the model executes the attacker’s desired behavior. This is one of the most dangerous forms of poisoning because backdoors can remain dormant and invisible during normal operation.

Examples:

• Image Classification: A 2020 study by Salem et al. demonstrated dynamic backdoor attacks, where imperceptible patterns embedded in images could cause AI systems to misclassify objects on demand without affecting baseline performance.

• Facial Recognition Systems: In 2017, researchers inserted backdoors that allowed individuals wearing specific accessories (such as patterned glasses) to consistently evade facial authentication systems.

• Autonomous Vehicles: Experiments have shown that inserting specific patterns into road signs can cause misclassification, such as interpreting a "stop" sign as a "speed limit 80" sign, triggering dangerous behavior.

Types of Backdoor Techniques:

• Static backdoors: Always activated by a fixed trigger (e.g., a specific image pattern).

• Dynamic backdoors: More advanced, triggered by a set of conditions or inputs dynamically.

• Blend backdoors: Merge trigger and normal inputs so subtly that human annotators cannot distinguish them.

Risks: The stealth and controllability of backdoor attacks make them ideal for long-term exploitation by sophisticated adversaries, including nation-state actors.

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