Adversarial Attacks

Adversarial attacks are attempts to manipulate or deceive AI systems so they produce wrong or misleading outputs. In cybersecurity, this can mean poisoning inputs, hiding malicious activity, or steering models away from true threats. Defending against them requires monitoring, validation, and human oversight around AI decisions.

Expanded Definition

Adversarial attacks are deliberate attempts to influence an AI system so it behaves incorrectly, inconsistently, or in a way that benefits an attacker. In security operations, they can target training data, inputs, prompts, model outputs, or the surrounding workflow that interprets those outputs. The term is broader than simple model evasion because it also includes poisoning, prompt manipulation, extraction, and deception of downstream automation. For AI governance, the important distinction is that the attack may succeed even when the underlying infrastructure remains intact, because the weakness is in how the system reasons, learns, or is trusted to act. NIST’s AI Risk Management Framework treats these risks as part of trustworthy AI management, while MITRE’s MITRE ATLAS adversarial AI threat matrix catalogues attacker techniques against AI systems. Usage in the industry is still evolving, and definitions vary across vendors when they extend the label to any AI error or hallucination.

The most common misapplication is calling ordinary model inaccuracy an adversarial attack when no attacker-controlled manipulation or adversarial intent is present.

Examples and Use Cases

Implementing adversarial attack defenses rigorously often introduces review overhead and latency, requiring organisations to weigh faster automation against stronger assurance.

• A phishing-detection model is trained on poisoned samples so it learns to ignore indicators that would normally trigger escalation.
• An attacker crafts inputs that are subtle to humans but cause a classifier to mislabel malicious activity as benign.
• A security analyst uses MITRE ATT&CK Enterprise Matrix alongside AI telemetry to separate traditional intrusion steps from model-targeted manipulation.
• A generative assistant is prompted to reveal restricted policy details, internal instructions, or sensitive context that should not be exposed.
• Threat hunters consult CISA cyber threat advisories when adversarial techniques overlap with active campaigns against public-facing services.

In practice, these attacks often appear inside larger intrusion chains rather than as standalone events, which is why defenders need to inspect both the AI system and the workflow around it. A model can be technically available and still be operationally unsafe if its inputs, retrieval sources, or output consumers are easy to manipulate. Where AI is embedded into identity, fraud, or access decisions, the attack surface expands because a misleading output can change who gets trusted, challenged, or blocked.

Why It Matters for Security Teams

Adversarial attacks matter because they undermine confidence in AI-assisted decisions at the exact point where organisations want automation to increase speed and consistency. A successful attack can distort alert triage, corrupt detection logic, or cause an AI assistant to recommend unsafe actions. That creates second-order risk: security teams may trust a system that looks functional while it is being steered by an attacker. NIST control guidance such as NIST SP 800-53 Rev 5 Security and Privacy Controls is relevant where organisations need monitoring, integrity checks, auditability, and response processes around AI-supported workflows. The identity connection is important too: when adversarial manipulation affects authentication, verification, or trust decisions, a false output can become an access-control failure rather than just a model-quality issue. Teams should treat adversarial resilience as a governance concern, not only a data science problem. Organisations typically encounter the true cost only after an AI-assisted control has been bypassed or a malicious campaign has already benefited from a distorted model output, at which point adversarial attacks become operationally unavoidable to address.

Related resources from NHI Mgmt Group

• Why do AI systems with broad access increase the impact of adversarial attacks?
• Why are identity-based attacks growing faster than traditional network attacks?
• What are cascading hallucination attacks?
• Why is NHI governance critical in the age of AI attacks?