What is an adversarial attack on an AI model

Exploiting How a Model Actually Thinks, Not a Software Bug

An adversarial attack on an AI model is a deliberate attempt to manipulate the model’s behavior or output by feeding it specially crafted input designed to exploit weaknesses in how the model processes information — a fundamentally different category of security concern from traditional software vulnerabilities like a code injection flaw or a misconfigured server.

Why This Differs From Traditional Cybersecurity Vulnerabilities

Traditional cybersecurity vulnerabilities generally involve flaws in how software is coded or configured, exploited through unauthorized access or malicious code execution. Adversarial attacks, by contrast, often work through a model’s completely normal, intended interface — simply providing carefully crafted input designed to trigger an unintended or incorrect response, without needing to breach any underlying system access controls at all.

A Common Example: Adversarial Images

One well-documented category involves subtly altering an image in ways imperceptible or nearly imperceptible to a human observer, but specifically calculated to cause an image recognition model to misclassify the image entirely — for example, causing a model to confidently identify a clearly recognizable object as something completely different, based on tiny, deliberately calculated pixel-level changes.

A Common Example: Adversarial Text Prompts

For language models specifically, adversarial attacks often take the form of carefully worded prompts designed to bypass the model’s intended restrictions or safety guidelines, sometimes called “jailbreaking,” exploiting the gap between what a model’s designers intended it to refuse and what specific phrasing or framing can actually get the model to produce.

Why These Attacks Exploit Fundamental Properties of How Models Work

Adversarial attacks generally succeed by exploiting the underlying statistical and mathematical properties of how a given model makes decisions — properties that are, in some sense, inherent to how current machine learning approaches work, rather than being an accidental flaw that could simply be patched out the way a traditional software bug might be fixed.

Why Defending Against These Attacks Requires Different Techniques

Because adversarial attacks exploit a model’s fundamental decision-making process rather than a traditional software flaw, defending against them requires different techniques than conventional cybersecurity measures — including adversarial training, where a model is deliberately exposed to adversarial examples during its own training process to become more robust against them.

Why This Remains an Active, Unresolved Area of AI Security Research

Despite meaningful progress in developing defenses, adversarial robustness remains an active, genuinely unresolved area of AI security research, since new attack techniques continue to be discovered even as defensive techniques improve, reflecting an ongoing, adversarial dynamic similar to other areas of cybersecurity.

Bottom Line

An adversarial attack on an AI model is a deliberate attempt to manipulate its behavior using specially crafted input that exploits how the model actually processes information, distinct from a traditional software vulnerability — a category of security concern that requires specialized defensive techniques and remains an active, unresolved area of AI security research.

Go deeper

• What Is a ‘Jailbreak’ in the Context of AI Models?