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.
Why AI Security is Important
AI is reshaping everything from healthcare diagnostics to fraud detection, cybersecurity tools, and autonomous vehicles. 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.
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.
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.
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:
- Expand strong security foundations to the AI ecosystem
- Extend detection and response to bring AI into an organization’s threat universe
- Automate defenses to keep pace with existing and new threats
- Adapt controls to shift from reactive to proactive security
- Harmonize platform-level controls to ensure consistent security
- Contextualize AI system risks to align with organizational risk management
SAIF aims to address concerns such as model theft, data poisoning, prompt injection, and the extraction of confidential information from training data..
Framework for AI Cybersecurity Practices (FAICP)
The Framework for AI Cybersecurity Practices (FAICP), developed by ENISA, offers a multilayered approach to securing AI systems. It consists of three layers:
- Layer I – Cybersecurity Foundations: Emphasizes securing the ICT infrastructure hosting AI systems.
- Layer II – AI-Specific Cybersecurity: Focuses on the unique security requirements of AI components throughout their lifecycle.
- Layer III – Sector-Specific Cybersecurity for AI: Addresses additional cybersecurity practices tailored to specific economic sectors utilizing AI systems..
OWASP AI Security and Privacy Guide
The Open Worldwide Application Security Project (OWASP) AI Security and Privacy Guide offers over 200 pages of actionable insights. This guide assists organizations in designing, developing, testing, and procuring secure and privacy-preserving AI systems. It covers threat modeling, data protection, and compliance considerations, serving as a valuable resource for aligning with international standards..
OWASP Top 10 for LLMs
OWASP’s Top 10 for Large Language Model (LLM) Applications identifies the most critical security and privacy risks associated with LLMs. Key vulnerabilities include:
- Prompt Injection: Manipulating model behavior through crafted inputs
- Data Leakage: Unintended exposure of sensitive information
- Inadequate Sandboxing: Insufficient isolation leading to unauthorized code execution
- Overreliance on Autogenerated Content: Trusting AI-generated outputs without proper validation
The OWASP Top 10 aims to raise awareness and provide remediation strategies to improve the security posture of LLM applications. To help practitioners better understand and navigate these risks, Mend offers a quick guide to the 2025 OWASP Top 10 for LLM Applications, which breaks down each vulnerability and offers actionable guidance.
Best Practices for AI Cybersecurity
Bias and Fairness Assessments
Governance plays a key role in building fair AI systems. As discussed in Mend’s post on AI governance in AppSec, evolving governance models are essential for addressing bias and ensuring regulatory compliance. Evaluating AI systems for fairness ensures that outputs are not unintentionally discriminatory. Regular assessments during training and deployment phases help prevent bias amplification.
Input Validation
Improper input handling opens the door to threats like prompt injection and adversarial manipulation. Mend’s analysis of insecure AI-generated code and hallucinated packages highlights how input flaws can lead to critical system vulnerabilities. Sanitize and validate all inputs, especially when models are integrated with user-facing applications. This is critical for preventing prompt injection and adversarial attacks.
Leverage Explainable AI
Use interpretable models or tools like SHAP or LIME to understand model behavior. SHAP (SHapley Additive exPlanations) assigns each feature an importance value for a particular prediction, helping identify what influenced the outcome. LIME (Local Interpretable Model-agnostic Explanations) builds simple, local surrogate models to approximate and explain the behavior of complex models in specific prediction scenarios. Both tools help uncover hidden logic errors, biases, or unexpected decision patterns.
Maintain Clear Documentation of AI Model Development Processes
Documenting datasets, training parameters, validation results, and deployment practices enables traceability and accountability, both of which are essential for audits and incident response.
Regular Updates and Patch Management
Just like traditional software, AI systems need patching. Keep libraries, frameworks, and models up to date to defend against newly discovered vulnerabilities.
How Mend AI Enhances AI Security
Mend AI is purpose-built to secure the rapidly growing attack surface introduced by AI adoption. It addresses threats like model leakage, prompt injection, and shadow AI—an often-overlooked risk discussed in our guide to identifying Shadow AI. As organizations increasingly integrate AI components into their applications, ensuring the security of these elements becomes paramount. Mend AI offers a comprehensive solution to address the unique challenges posed by AI integration.
Comprehensive AI Component Visibility
Mend AI provides an exhaustive inventory of AI models and frameworks utilized within applications. This includes the detection of “Shadow AI”—unauthorized or undocumented AI components that might introduce unforeseen vulnerabilities. By maintaining a detailed AI Bill of Materials (AI-BoM), organizations can achieve full visibility into their AI assets, facilitating better risk management and compliance.
Identification of AI-Specific Risks
Beyond mere detection, Mend AI assesses known risks associated with AI components, such as licensing issues, public security vulnerabilities, and potential threats from malicious packages. This proactive approach ensures that organizations are aware of and can address vulnerabilities before they are exploited.
Behavioral Risk Analysis through Red-Teaming
Mend AI Premium introduces advanced capabilities to simulate and identify behavioral risks unique to AI-powered applications. Through customizable tests, it evaluates vulnerabilities like prompt injection, context leakage, and data exfiltration, ensuring that AI components behave securely under various scenarios.
Seamless Integration and Governance
Understanding the importance of integrating security into existing workflows, Mend AI seamlessly fits into current development environments. Its robust policy engine and automation workflows enable organizations to govern AI components throughout the software development lifecycle, ensuring consistent security practices without disrupting productivity.
Incorporating Mend AI into your security strategy ensures that AI components are not only integrated efficiently but also secured against evolving threats, maintaining the integrity and trustworthiness of your applications.. As AI adoption accelerates, staying ahead of threats and embedding security into the lifecycle of AI systems will be a non-negotiable aspect of trustworthy innovation.
*** This is a Security Bloggers Network syndicated blog from Mend authored by Mend.io Team. Read the original post at: https://www.mend.io/blog/ai-security-guide-protecting-models-data-and-systems-from-emerging-threats/