How adversaries poison data, steal models, and weaponize generative AI.

The Rise of AI as a Prime Target

AI is no longer just a clever feature; it’s critical infrastructure for finance, healthcare, government, and consumer tech. That makes it a magnet for attackers. Modern risk frameworks like NIST’s AI Risk Management Framework now explicitly treat AI integrity, confidentiality, and availability as first-class security concerns, alongside privacy and safety. NIST Publication

Organizations are discovering that traditional cybersecurity controls—firewalls, MFA, antivirus—barely touch the unique vulnerabilities of machine learning pipelines. Security teams are now expected to understand training data, model architectures, and MLOps tooling as deeply as they know network diagrams and IAM policies.

Adversarial Machine Learning: Hacking the Model’s Mind

Adversarial machine learning (AML) explores how attackers can manipulate or evade AI systems. NIST’s 2024 AML taxonomy groups attacks by lifecycle stage (training, deployment, inference) and adversary capabilities, underscoring how broad the attack surface has become. NIST Computer Security Resource Center

Common attack patterns include:

• Data poisoning: Subtly corrupting training data so the model “learns” the wrong behavior—like misclassifying particular objects or favoring specific outcomes.

• Evasion attacks: Crafting adversarial inputs—images, prompts, or API calls—that look normal to humans but cause wildly incorrect predictions.

• Backdoor attacks: Injecting a hidden trigger during training (a specific phrase, pattern, or token) that flips the model’s behavior on demand.

For defenders, the lesson is clear: you must secure every stage of the AI lifecycle—data sourcing, labeling, training, deployment, and monitoring—not just the final model artifact. ENISA

AI-Powered Cyberattacks: Offense Gets an Upgrade

Attackers aren’t just targeting AI; they’re using AI to scale their own operations. Generative models and code assistants are already changing the economics of cybercrime.

Two patterns stand out:

• Hyper-personalized phishing and social engineering: Large language models can produce grammatical, context-aware phishing emails that blend seamlessly with corporate communication styles, making traditional “bad English” detection useless. NIST

• Automated vulnerability discovery: AI tools can sift through code, configurations, and logs to suggest exploit paths or chain misconfigurations faster than many junior security analysts.

Defenders have to respond in kind: deploying AI for anomaly detection, threat intel correlation, and rapid incident response—but with strong safeguards so those same defensive models can’t be turned against them.

Model and Data Theft: When Your IP Walks Out the Door

For many companies, the model is the product. That raises three intertwined challenges:

• Model extraction: Attackers query a deployed model until they can approximate it locally, recreating valuable IP without ever breaching the network.

• Training data leakage: Poor isolation, weak access control, or inadequate anonymization can expose sensitive source data, especially when models are trained on regulated datasets. Palo Alto Networks+1

• Model exfiltration from MLOps pipelines: Insecure artifact registries, build systems, or CI/CD pipelines can leak models before they’re even deployed.

Mitigation is a mix of rate limiting, watermarking, model fingerprinting, encryption in storage and transit, and strict RBAC across data lakes and model registries.

Deepfakes and Synthetic Misinformation

The same generative tools that enable creative marketing and entertainment have also democratized deepfakes. Lawmakers are starting to respond—for example, Spain has proposed heavy fines for companies that fail to label AI-generated content, aiming specifically at deepfake misuse. Reuters

Defenses are still maturing, but common steps include:

• Digital watermarks and provenance standards (C2PA-style metadata) embedded at generation time.

• Detection models trained to spot statistical artifacts in audio, images, and video.

The challenge is that as detection improves, generation improves too—creating an ongoing arms race that will make media literacy and transparent labelling just as important as technical controls.

Closing Thoughts and Looking Forward

AI security is moving from “nice-to-have research topic” to “board-level risk.” Over the next few years, expect:

• Standardized taxonomies and benchmarks for evaluating adversarial robustness and model theft resistance, building on the work of NIST and ENISA. NIST Computer Security Resource Center

• Dedicated AI red teams blending security engineers, ML researchers, and social engineers to probe both technical and human vulnerabilities.

• Tighter integration of AI and cybersecurity roadmaps, where SOCs monitor not just endpoints and networks but AI pipelines, data flows, and model behavior drifts.

The organizations that win will treat AI like any other critical system—architected with security, safety, and privacy in mind from day one, not bolted on after a breach or regulatory inquiry.

Reference Sites

1. Artificial Intelligence Risk Management Framework (AI RMF 1.0) – NIST
   https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf NIST Publications

2. Cybersecurity, Privacy, and AI – NIST
   https://www.nist.gov/itl/applied-cybersecurity/cybersecurity-privacy-and-ai NIST

3. Securing Machine Learning Algorithms – ENISA
   https://op.europa.eu/en/publication-detail/-/publication/c7c844fd-7f1e-11ec-8c40-01aa75ed71a1/language-en Publications Office of the EU

4. NIST AI 100-2 E2023: Adversarial Machine Learning – A Taxonomy and Terminology of Attacks and Mitigations – NIST
   https://csrc.nist.gov/pubs/ai/100/2/e2023/final NIST Computer Security Resource Center

5. Defense Strategies for Adversarial Machine Learning – Computer Science Review
   https://www.sciencedirect.com/science/article/abs/pii/S1574013723000400 ScienceDirect

Author: Serge Boudreaux – AI Hardware Technologies, Montreal, Quebec
Co-Editor: Peter Jonathan Wilcheck – Miami, Florida