The Unsung Role Keeping AI Safe: Inside Adversarial AI

Most AI teams spend months making their models more accurate. Almost none of them ask a more important question what happens when someone deliberately tries to break it?

That question is not theoretical. It is happening right now. AI systems are being deployed in hospitals, banks, courtrooms, and autonomous vehicles. And in every one of those environments, a compromised model is not just a technical failure. It is a safety risk, a financial risk, and increasingly, a legal one.

Adversarial AI is the field that exists to answer that question. Australian Strategic Policy Institute (ASPI) Tech Tracker identifies it as one of the six most critical AI capabilities of our time. The professionals who work in it are among the rarest in the entire AI industry.

The Side of AI Nobody Is Building For

Here is something most people outside of AI research do not know: you can fool an AI model with a change so small the human eye cannot see it.

A tiny, carefully crafted modification to an image involving just a few altered pixels can cause a state-of-the-art vision model to misclassify it completely. A stop sign becomes a speed limit sign. A benign email gets past a spam filter. A medical scan is misread. These are called adversarial attacks, and they are not a niche academic curiosity. They are a documented, real-world threat to any AI system operating in a high-stakes environment.

Adversarial AI is the field that studies both sides of this: how AI systems can be attacked, and how to build defences that hold up under deliberate, intelligent pressure. It is one of the most technically demanding disciplines in all of AI.

From Academic Curiosity to Frontline Defence

Adversarial attacks were first formally studied in 2013, when researchers demonstrated that deep learning models could be fooled. At the time it was an academic curiosity; interesting, but seemingly distant from anything practical.

That changed fast. As AI moved from research labs into production systems, credit scoring, medical imaging, facial recognition, and content moderation the attack surface grew enormously. What was once a theoretical vulnerability became an exploitable weakness in real deployed systems.

The field evolved in response. Attack methods like backdoor poisoning and model extraction became more sophisticated. Defences had to keep pace through adversarial training certified robustness, input preprocessing, and formal verification methods all emerged as serious research directions.

Today adversarial AI sits at the intersection of security, mathematics, and machine learning. It is no longer just a research discipline. It is a frontline safety practice.

The Stakes Have Never Been Higher

AI is now making consequential decisions at scale. A hiring algorithm screens thousands of candidates. A medical AI flags abnormalities in patient scans. An autonomous vehicle interprets road signs at 100 km/h. A fraud detection model clears or blocks financial transactions in milliseconds.

In every one of these settings, an adversarial attack whether from a malicious user or a poisoned training dataset can cause real harm. And the organisations deploying these systems are almost never testing for it.

Regulatory pressure is also building. The European Union’s Artificial Intelligence Regulation (EU) 2024/1689, widely known as the EU AI Act, for example, explicitly requires robustness and security testing for high-risk AI applications. What was previously considered best practice is becoming a compliance requirement. Organisations that have not built adversarial testing into their AI development process are already behind.

This is why ASPI flags it as critical. It is not just a research problem. It is rapidly becoming a governance and infrastructure problem.

What the Job Really Involves

A Lead Adversarial AI Researcher is not a typical AI engineer. Their job is to think like an attacker and a defender simultaneously.

Attack generation — Generating universal adversarial perturbations (UAPs) using methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Carlini-Wagner (C&W) Attack to find the exact inputs that cause a model to fail. This reveals weaknesses before a real attacker does.

Robustness certification — Use techniques like randomised smoothing for L∞ robustness to provide mathematical guarantees about a model’s behaviour under attack. This is proof-driven work not just testing whether a model survives an attack but proving it within formally defined bounds.

Backdoor detection — Identify hidden vulnerabilities planted in a model during training, using spectral analysis and other forensic techniques. A backdoored model behaves normally until it encounters a specific trigger at which point it fails in a controlled, malicious way.

Together, these three define what genuine expertise in this field looks like. It is equal parts offensive security and rigorous mathematics.

How to Get Into This Field

• Foundations: Python, linear algebra, probability theory, and a solid understanding of deep learning fundamentals (CNNs, transformers, loss functions, backpropagation)
• Core ML Frameworks: PyTorch (primary), TensorFlow. You need to be able to implement and modify models at a low level, not just use high-level APIs
• Adversarial Attack Methods: FGSM (Fast Gradient Sign Method), PGD (Projected Gradient Descent), Carlini-Wagner (C&W), DeepFool, Prompt Attacks, and Backdoor Attacks (BadNets, Trojan)
• Adversarial Defence Methods: Adversarial Training, Randomised Smoothing, Input Preprocessing Defences, Feature Squeezing, and Certified Defences
• Key Libraries: Adversarial Robustness Toolbox (ART) by IBM, Foolbox, CleverHans, BackdoorBench
• Backdoor Detection: Spectral signatures, Neural Cleanse, STRIP, Activation Clustering — tools and papers to study and implement
• Mathematics to Master: Convex optimisation, L-norms (L0, L2, L∞), statistical hypothesis testing, measure theory basics
• Security Fundamentals: Threat modelling, attack surface analysis, and red teaming methodology to understand the security mindset

Why This Cannot Wait

AI is only as trustworthy as it is robust. And right now, most AI systems are being deployed without anyone seriously testing whether they hold up under deliberate attack.

Adversarial AI researchers are the people who change that. They are the ones who break AI systems so that the rest of us can trust them. It is one of the most technically demanding, least understood, and most critically needed roles in all of AI.

ASPI did not flag this as critical because it is interesting research. It flagged it because it is the difference between AI that is powerful and AI that is safe. And that distinction matters more every year.

Part of Kolofon’s series — The Critical AI Skills That Will Define the Next Decade. Read the series introduction: 6 Critical AI Technologies  And What It Takes to Be Ready for Them

Read the previous blog: Data Is Everywhere. Insight Is Rare — Advanced Data Analytics

Source: ASPI Technology Tracker — AI Technologies