AI Security Guide: Protecting models, data, and systems from emerging threats

#1734009: AI Security Guide: Protecting models, data, and systems from emerging threats

Description:	What is AI security? AI security is where traditional cybersecurity meets the chaotic brilliance of machine learning. It’s the discipline focused on protecting AI systems—not just the code, but the training data, model logic, and output—from manipulation, theft, and misuse. Because these systems learn from data, not just logic, they open up fresh attack surfaces like data poisoning, model inversion, and prompt injection. Keeping AI safe means securing everything from the datasets that shape it to the decisions it makes in production. These risks become even more pronounced in retrieval-augmented generation (RAG) pipelines, where untrusted external data feeds into model prompts. Our guide on RAG security explains how to prevent poisoning and injection in RAG-based systems. Why AI security is important AI is reshaping everything from healthcare diagnostics to fraud detection, cybersecurity tools, and autonomous vehicles. The scale of adoption makes this even more urgent—see our roundup of key generative AI statistics to understand just how fast these technologies are spreading across industries. But as AI takes on more responsibility, its attack surface grows. A single vulnerability in an AI pipeline can ripple outward into very real-world consequences. Whether it’s leaking private data, misclassifying critical inputs, or getting hijacked for malicious ends, an insecure AI system is a liability waiting to happen. Locking down AI isn’t just smart, it’s survival. As these systems take on higher-stakes decision-making, vulnerabilities in AI can lead to significant harm—either through malicious exploitation or unintended consequences. Ensuring AI security is more than simply a technical concern; it’s a safety, trust, and compliance imperative. A formal AI risk management program is the structural backbone that ties these concerns together — covering identification, assessment, and mitigation across the AI lifecycle. These challenges are particularly acute in generative AI systems, which introduce unique risks such as prompt manipulation and data leakage. See our primer on generative AI security for a deeper dive. Key AI attacks and vulnerabilities Data poisoning Data poisoning involves injecting malicious or intentionally misleading data into a model’s training dataset to manipulate its outcomes. This manipulation can manifest in various ways: causing incorrect predictions, embedding backdoors that only activate under specific conditions, or skewing model behavior to reinforce undesirable biases. Poisoning attacks can be subtle—such as a small fraction of mislabeled examples—or overt, involving large volumes of manipulated data. For instance, in a facial recognition system, an attacker could introduce corrupted training samples that cause the model to consistently misidentify individuals from a specific demographic group, leading to real-world harm and reinforcing systemic biases. In a cybersecurity context, poisoning a threat detection model might enable malware to go undetected by subtly altering features in training logs. Model inversion attacks In model inversion attacks, an adversary interacts with a trained machine learning model—typically a black-box API—by feeding it a large number of crafted inputs and analyzing the corresponding outputs. Over time, the attacker can use statistical or machine learning techniques to infer details about the original training data, sometimes even reconstructing images, text, or records that closely resemble real examples from the dataset. This type of attack poses significant privacy concerns, especially in domains that rely on sensitive personal information. For instance, in healthcare, an attacker might recover parts of a patient’s medical record from a model trained to diagnose conditions. In finance, a model predicting credit risk could inadvertently leak attributes about individual loan applicants. Model inversion attacks are more likely when models are overly confident in their predictions or lack proper differential privacy mechanisms. Mitigation strategies include adding noise to outputs, limiting access to model internals, and employing privacy-preserving training techniques like federated learning, which enhances privacy by keeping sensitive information decentralized, or homomorphic encryption, which enables computation on encrypted data and allows models to make predictions or perform analytics without ever decrypting the underlying inputs, thus preserving confidentiality even during processing. Prompt injection Prompt injection targets large language models (LLMs) by embedding malicious instructions within user inputs or surrounding context. These attacks exploit the model’s inability to distinguish between trusted and untrusted input, potentially causing it to ignore system-level instructions, reveal sensitive information, or behave erratically. Prompt injection can be either direct—by placing malicious content within a user’s prompt—or indirect, such as injecting harmful text into documents, web pages, or logs that are later ingested by an LLM-powered system. This is one of the growing security threats amplified by LLMs that organizations must prepare for. Prompt injection targets large language models (LLMs) by embedding malicious instructions within user inputs or surrounding context. These attacks exploit the model’s inability to distinguish between trusted and untrusted input, potentially causing it to ignore system-level instructions, reveal sensitive information, or behave erratically. Prompt injection can be either direct—by placing malicious content within a user’s prompt—or indirect, such as injecting harmful text into documents, web pages, or logs that are later ingested by an LLM-powered system. This vulnerability becomes particularly dangerous when LLMs are used in autonomous agents, customer support bots, or workflow automation tools where they have access to sensitive data or the ability to take actions. For example, a prompt injection could trick a virtual assistant into executing unauthorized commands or disclosing confidential internal notes. Mitigation strategies include implementing strong input validation and contextual sanitization, maintaining strict separation between user content and system prompts, and using prompt hardening techniques such as system message reinforcement or output filtering to reduce exploitability. Adversarial attacks Adversarial attacks are closely linked to weaknesses in the way models interpret and encode data. Subtle manipulations, such as those discussed in Mend’s analysis of vector and embedding vulnerabilities, can drastically alter model behavior while remaining undetected by human reviewers. These attacks involve crafting inputs that are subtly manipulated—often imperceptible to humans—to deceive a model into making incorrect or even dangerous decisions. These manipulations, known as adversarial examples, exploit the model’s sensitivity to certain input features and its lack of robust generalization. In image recognition, for example, a stop sign with strategically placed stickers or noise can be misclassified by a self-driving car’s vision system as a speed limit sign or yield sign, potentially leading to hazardous behavior on the road. Adversarial attacks are not limited to vision systems. In natural language processing, slightly altering a sentence—such as reordering words or replacing synonyms—can significantly change a model’s classification results. In cybersecurity applications, adversarial samples can be used to bypass malware detectors or intrusion detection systems. These attacks highlight the fragility of many machine learning models and emphasize the need for robust training, input preprocessing, adversarial training techniques, and continuous testing against evolving attack patterns. Model theft Model theft—also known as model extraction—occurs when an adversary replicates a proprietary model by systematically querying it and analyzing its outputs. This tactic, particularly threatening in AI-as-a-service environments, can lead to intellectual property theft and erode competitive advantage. To help organizations counter this risk, Mend outlines practical strategies for defending against model extraction, offering guidance on how to secure valuable AI assets from unauthorized replication. Protecting models against theft requires a dedicated strategy. Our guide on AI model security outlines practical steps to safeguard intellectual property while preserving performance. Frameworks and guidelines for AI security When exploring security frameworks, it’s useful to distinguish between securing AI itself and using AI as a security tool—a topic examined in Securing AI vs AI Security. Frameworks provide strategy, but organizations also need practical tools to continuously monitor and improve their defenses. This is where AI security posture management comes in, giving teams visibility and control across the AI lifecycle NIST AI risk management framework Developed by the National Institiute of Standards and Technology, the AI RMF provides a voluntary, structured approach to managing risks associated with AI systems across their lifecycle. It is built around four core functions: Govern, Map, Measure, and Manage, each encompassing specific categories and subcategories to guide organizations in identifying and mitigating AI-related risks. It emphasizes governance, map, measure, and manage functions. Google’s secure AI framework (SAIF) Google’s Secure AI Framework (SAIF) outlines six core elements designed to enhance the security of AI systems:
More info:	https://www.mend.io/blog/ai-security-guide-protecting-models-data-and-systems-from-emerging-threats/?utm_source=marketo&utm_medium=email&utm_campaign=2026q1-product-mend-ai&utm_content=em1-guide-link&utm_product=ai&utm_origin=email&utm_from=marketo

Description:

What is AI security?
AI security is where traditional cybersecurity meets the chaotic brilliance of machine learning. It’s the discipline focused on protecting AI systems—not just the code, but the training data, model logic, and output—from manipulation, theft, and misuse. Because these systems learn from data, not just logic, they open up fresh attack surfaces like data poisoning, model inversion, and prompt injection. Keeping AI safe means securing everything from the datasets that shape it to the decisions it makes in production. These risks become even more pronounced in retrieval-augmented generation (RAG) pipelines, where untrusted external data feeds into model prompts. Our guide on RAG security explains how to prevent poisoning and injection in RAG-based systems.

Why AI security is important
AI is reshaping everything from healthcare diagnostics to fraud detection, cybersecurity tools, and autonomous vehicles. The scale of adoption makes this even more urgent—see our roundup of key generative AI statistics to understand just how fast these technologies are spreading across industries. But as AI takes on more responsibility, its attack surface grows.

A single vulnerability in an AI pipeline can ripple outward into very real-world consequences. Whether it’s leaking private data, misclassifying critical inputs, or getting hijacked for malicious ends, an insecure AI system is a liability waiting to happen. Locking down AI isn’t just smart, it’s survival. As these systems take on higher-stakes decision-making, vulnerabilities in AI can lead to significant harm—either through malicious exploitation or unintended consequences. Ensuring AI security is more than simply a technical concern; it’s a safety, trust, and compliance imperative. A formal AI risk management program is the structural backbone that ties these concerns together — covering identification, assessment, and mitigation across the AI lifecycle.
These challenges are particularly acute in generative AI systems, which introduce unique risks such as prompt manipulation and data leakage. See our primer on generative AI security for a deeper dive.

Key AI attacks and vulnerabilities
Data poisoning
Data poisoning involves injecting malicious or intentionally misleading data into a model’s training dataset to manipulate its outcomes. This manipulation can manifest in various ways: causing incorrect predictions, embedding backdoors that only activate under specific conditions, or skewing model behavior to reinforce undesirable biases. Poisoning attacks can be subtle—such as a small fraction of mislabeled examples—or overt, involving large volumes of manipulated data.

For instance, in a facial recognition system, an attacker could introduce corrupted training samples that cause the model to consistently misidentify individuals from a specific demographic group, leading to real-world harm and reinforcing systemic biases. In a cybersecurity context, poisoning a threat detection model might enable malware to go undetected by subtly altering features in training logs.

Model inversion attacks
In model inversion attacks, an adversary interacts with a trained machine learning model—typically a black-box API—by feeding it a large number of crafted inputs and analyzing the corresponding outputs. Over time, the attacker can use statistical or machine learning techniques to infer details about the original training data, sometimes even reconstructing images, text, or records that closely resemble real examples from the dataset.

This type of attack poses significant privacy concerns, especially in domains that rely on sensitive personal information. For instance, in healthcare, an attacker might recover parts of a patient’s medical record from a model trained to diagnose conditions. In finance, a model predicting credit risk could inadvertently leak attributes about individual loan applicants.

Model inversion attacks are more likely when models are overly confident in their predictions or lack proper differential privacy mechanisms.

Mitigation strategies include adding noise to outputs, limiting access to model internals, and employing privacy-preserving training techniques like federated learning, which enhances privacy by keeping sensitive information decentralized, or homomorphic encryption, which enables computation on encrypted data and allows models to make predictions or perform analytics without ever decrypting the underlying inputs, thus preserving confidentiality even during processing.

Prompt injection
Prompt injection targets large language models (LLMs) by embedding malicious instructions within user inputs or surrounding context. These attacks exploit the model’s inability to distinguish between trusted and untrusted input, potentially causing it to ignore system-level instructions, reveal sensitive information, or behave erratically. Prompt injection can be either direct—by placing malicious content within a user’s prompt—or indirect, such as injecting harmful text into documents, web pages, or logs that are later ingested by an LLM-powered system.

This is one of the growing security threats amplified by LLMs that organizations must prepare for. Prompt injection targets large language models (LLMs) by embedding malicious instructions within user inputs or surrounding context. These attacks exploit the model’s inability to distinguish between trusted and untrusted input, potentially causing it to ignore system-level instructions, reveal sensitive information, or behave erratically. Prompt injection can be either direct—by placing malicious content within a user’s prompt—or indirect, such as injecting harmful text into documents, web pages, or logs that are later ingested by an LLM-powered system.

This vulnerability becomes particularly dangerous when LLMs are used in autonomous agents, customer support bots, or workflow automation tools where they have access to sensitive data or the ability to take actions. For example, a prompt injection could trick a virtual assistant into executing unauthorized commands or disclosing confidential internal notes.

Mitigation strategies include implementing strong input validation and contextual sanitization, maintaining strict separation between user content and system prompts, and using prompt hardening techniques such as system message reinforcement or output filtering to reduce exploitability.

Adversarial attacks
Adversarial attacks are closely linked to weaknesses in the way models interpret and encode data. Subtle manipulations, such as those discussed in Mend’s analysis of vector and embedding vulnerabilities, can drastically alter model behavior while remaining undetected by human reviewers. These attacks involve crafting inputs that are subtly manipulated—often imperceptible to humans—to deceive a model into making incorrect or even dangerous decisions. These manipulations, known as adversarial examples, exploit the model’s sensitivity to certain input features and its lack of robust generalization. In image recognition, for example, a stop sign with strategically placed stickers or noise can be misclassified by a self-driving car’s vision system as a speed limit sign or yield sign, potentially leading to hazardous behavior on the road.

Adversarial attacks are not limited to vision systems. In natural language processing, slightly altering a sentence—such as reordering words or replacing synonyms—can significantly change a model’s classification results. In cybersecurity applications, adversarial samples can be used to bypass malware detectors or intrusion detection systems.

These attacks highlight the fragility of many machine learning models and emphasize the need for robust training, input preprocessing, adversarial training techniques, and continuous testing against evolving attack patterns.

Model theft
Model theft—also known as model extraction—occurs when an adversary replicates a proprietary model by systematically querying it and analyzing its outputs. This tactic, particularly threatening in AI-as-a-service environments, can lead to intellectual property theft and erode competitive advantage. To help organizations counter this risk, Mend outlines practical strategies for defending against model extraction, offering guidance on how to secure valuable AI assets from unauthorized replication. Protecting models against theft requires a dedicated strategy. Our guide on AI model security outlines practical steps to safeguard intellectual property while preserving performance.

Frameworks and guidelines for AI security
When exploring security frameworks, it’s useful to distinguish between securing AI itself and using AI as a security tool—a topic examined in Securing AI vs AI Security. Frameworks provide strategy, but organizations also need practical tools to continuously monitor and improve their defenses. This is where AI security posture management comes in, giving teams visibility and control across the AI lifecycle

NIST AI risk management framework
Developed by the National Institiute of Standards and Technology, the AI RMF provides a voluntary, structured approach to managing risks associated with AI systems across their lifecycle. It is built around four core functions: Govern, Map, Measure, and Manage, each encompassing specific categories and subcategories to guide organizations in identifying and mitigating AI-related risks. It emphasizes governance, map, measure, and manage functions.

Google’s secure AI framework (SAIF)
Google’s Secure AI Framework (SAIF) outlines six core elements designed to enhance the security of AI systems:

More info:

https://www.mend.io/blog/ai-security-guide-protecting-models-data-and-systems-from-emerging-threats/?utm_source=marketo&utm_medium=email&utm_campaign=2026q1-product-mend-ai&utm_content=em1-guide-link&utm_product=ai&utm_origin=email&utm_from=marketo

Date added	April 21, 2026, 4:17 p.m.
Source	mend.io
Subjects	AI/ML - Artificial Intelligence / Machine Learning / GenAI / Artificial General Intelligence - AGI - Various PodCasts / Webcast / Webinar / eSummit / Virtual Event etc. Security Guidelines / Checklists etc
Venue	June 19, 2026, midnight - June 19, 2026, midnight