Security for AI: understanding threats and building resilient defenses.

AI Security Overview

AI systems are intrinsically insecure and vulnerable to a wide range of attacks, including data manipulation, model extraction, and malware insertion. Like other critical software, AI requires robust security measures such as model debugging, audits, and other measures that will be explained further down in this article, such as red teaming and bug bounties. The key threats include insider threats, external manipulation, and compromised third-party components.

Security assessments should go beyond traditional machine learning testing to include threat modeling, adversarial testing (explained further down) and proactive defenses. Checking confidentiality, integrity, and availability, offers a foundational framework for understanding AI vulnerabilities. Attacks may involve data extraction (confidentiality), data poisoning (integrity), or denial of access (availability), and can also lead to discrimination or misuse.

Basic security practices for AI include access control, incident response plans, routine backups, least privilege enforcement, secure authentication, avoiding unsecured physical media, ensuring product-level security, vetting third-party tools, and maintaining strict version control. Adopting an adversarial mindset is crucial for identifying and mitigating emerging threats in AI systems.

Common Types of ML Attacks

As machine learning systems become more integrated into critical applications, they present new attack vectors for cybercriminals and insiders. These attacks can compromise model integrity, decision-making, privacy, and fairness, undermining the very purpose of the AI system.

Common types of machine learning attacks include adversarial example attacks, where attackers manipulate input data to influence predictions in their favor, such as securing loans or lower insurance rates. Data poisoning involves altering training data to change model behavior. Impersonation attacks occur when attackers mimic individuals with favorable outcomes to obtain similar benefits. Evasion attacks are designed to bypass detection systems or avoid negative outcomes, like fraud detection or risk assessments. Confidentiality attacks involve extracting sensitive information from a model’s outputs. These attacks matter because they can lead to intellectual property theft, privacy breaches, and significant financial or reputational damage. Vulnerabilities can be exploited through public APIs, insider access, or manipulated training data.

Next Step: countermeasures. Understanding these threats is the first step. Proactive defenses, including access controls, anomaly detection, robust training methods, and secure APIs, are essential to protect ML systems from these evolving threats.

General AI Security Concerns

AI systems are fundamentally software systems, and thus inherit many of the same security vulnerabilities. As their adoption grows, so does their potential for misuse and attack. Below are some key security risks.

Deepfakes: used for non-consensual content, impersonation scams, or fraud.

Facial recognition abuse: can lead to racial profiling or surveillance misuse.

Trojans & Malware: ML pipelines often rely on open source packages, which may contain hidden malware. Always scan and vet third-party software and models for malicious payloads.

Black-Box Models low transparency and high complexity: attackers might exploit models more deeply than their creators understand. When possible, interpretable models can reduce risk and enhance control (see my article: “accuracy vs. transparency: Black-Box vs. Interpretable machine learning models.”)

Distributed Computing Vulnerabilities: Big data and distributed AI systems have broader attack surfaces. Attacks like data poisoning or backdoors can target just a few nodes or models in an ensemble. Debugging is harder due to complexity and scale.

These concerns reinforce the need to treat AI systems with the same (or more) rigor as traditional software, applying best practices for security, testing, and governance across the entire AI lifecycle.

Counter-measures for Securing AI

To secure AI systems, a strong defense strategy is essential. Effective measures include internal practices, robust development processes, as well as newer academic fields. Below are some examples.

Red-teams: testing teams acting with a hacker mindset.

Bug bounties: organizations offer money to individuals who find and report security vulnerabilities or bugs in their systems before attackers can exploit them.

Adversarial Machine Learning: is a field of study focused on how attackers can manipulate machine learning systems, and how to defend against those attacks. It explores how small changes to inputs can cause ML models to make incorrect predictions. These changes are called adversarial examples. For example, an attacker might slightly alter a stop sign image so that an AI-powered self-driving car sees it as a speed limit sign.

Robust Machine Learning: develop models that resist adversarial attacks by enforcing consistency across similar inputs and maintaining stable predictions.

Authentication: require user authentication for sensitive systems.

Model Documentation: maintain thorough documentation of models and vulnerabilities.

Model Management: track all deployed models and monitor their performance and incidents.

These defenses, when implemented together, form a strong security posture for AI systems. Next steps typically involve simulating real-world attacks on AI systems involving a red-team again. The following step would involve learning from real-world security incidents.