What Is Adversarial AI? Real-World Attacks on Modern AI Systems
- Offensive AI Security
Why Adversarial AI Matters Now
Artificial intelligence (AI) has moved from an experimental technology to a foundational infrastructure. Machine learning (ML) and generative AI (GenAI) systems are now embedded across authentication workflows, fraud detection platforms, endpoint protection tools, content moderation systems, decision support engines, customer service automation, and security operations centers. In many organizations, AI systems influence or directly make decisions that were previously handled by humans.
As AI adoption accelerates, a critical security gap is emerging. Most organizations focus on securing the infrastructure that supports AI rather than the behavior of the AI systems themselves. Models are deployed, integrated, and trusted long before their failure modes are fully understood. Traditional security controls are applied around AI systems, while the models inside those systems are assumed to be reliable. That assumption is increasingly dangerous.
Adversarial AI is a growing and under-addressed threat category in which attackers intentionally manipulate AI systems to elicit incorrect, insecure, or harmful behavior. These attacks do not rely on malware, exploits, or misconfigurations. They rely on understanding how AI systems learn, generalize, and make decisions. As AI becomes a core component of modern systems, adversarial AI becomes a core security concern.
What Is Adversarial AI
Adversarial AI refers to the deliberate manipulation of AI systems to influence their behavior in ways that benefit an attacker. The objective may involve evasion, data extraction, decision manipulation, or long-term degradation of system effectiveness. In each case, the attacker targets how the model processes inputs and produces outputs rather than how the surrounding software is implemented.
Adversarial AI differs from traditional cyberattacks in several important ways. Conventional attacks focus on exploiting software vulnerabilities, configuration weaknesses, or authentication failures. By contrast, adversarial AI attacks target learned behavior, statistical relationships, and assumptions embedded in the model through training data.
Adversarial AI also differs from model bugs or unintentional AI errors. Bugs and errors emerge from implementation mistakes, incomplete requirements, or data quality issues. Adversarial AI involves intentional, goal-driven actions by an attacker who understands the system well enough to influence its behavior in predictable ways.
Several characteristics define adversarial AI attacks:
- They target model behavior rather than code execution.
- They operate within expected system usage patterns.
- They exploit probabilistic decision-making instead of deterministic logic.
- They often produce subtle or delayed effects rather than immediate failures.
These characteristics make adversarial AI attacks difficult to identify using traditional security testing and monitoring approaches.
How Adversarial AI Attacks Work at a High Level
Although individual techniques vary, most adversarial AI attacks follow a similar lifecycle that mirrors traditional attack chains while targeting different attack surfaces.
Reconnaissance
Attackers begin by learning how the AI system behaves. This may involve observing outputs, measuring consistency across responses, probing decision thresholds, or identifying feedback mechanisms. Even limited interaction can reveal valuable insights into how a model interprets inputs.
In many cases, reconnaissance occurs indirectly. Attackers infer model behavior by observing downstream effects such as transaction approvals, content moderation outcomes, fraud scores, or alert prioritization. Over time, these observations allow attackers to build a mental model of system behavior.
Manipulation
Once sufficient understanding is gained, attackers begin manipulating inputs, data, or interaction patterns. This may include crafting adversarial examples, injecting malicious prompts, influencing retraining data, or exploiting feedback loops.
Manipulation relies on shaping inputs to align with how the model generalizes from training data. The attacker does not need to break the system. The system is encouraged to make the wrong decision on its own.
Impact
The final stage involves achieving the desired outcome. This may include bypassing detection systems, extracting sensitive information, degrading model accuracy, or influencing automated decision-making.
Impact is often subtle. Systems continue operating, logs appear normal, and failures may only become visible through degraded outcomes, increased false negatives, or long-term erosion of trust in AI-driven decisions.
Common Types of Adversarial AI Attacks
Adversarial Examples
Adversarial examples involve carefully crafted inputs designed to cause incorrect model predictions. These inputs often appear benign or indistinguishable from legitimate data to humans, yet they reliably influence model behavior.
Such attacks have been demonstrated against image recognition, speech processing, natural language understanding, and fraud detection systems. Small perturbations that are invisible or meaningless to a human observer can dramatically alter a model’s output.
Data Poisoning Attacks
Data poisoning occurs when attackers influence training or retraining data to bias model behavior. This may involve injecting malicious samples, manipulating labels, or exploiting automated data collection and labeling pipelines. The effects of data poisoning are frequently delayed. Models may operate normally until specific conditions trigger poisoned behavior, complicating detection, root-cause analysis, and remediation.
Prompt Injection
Prompt injection attacks target large language models (LLMs) and other generative systems. Attackers manipulate prompts to override system instructions, extract sensitive context, or influence downstream automation. Indirect prompt injection presents a particularly serious risk. Malicious instructions may be embedded in documents, emails, or web content that AI systems are designed to ingest and trust as part of normal operation.
Model Inversion and Extraction
Model inversion and extraction attacks seek to recover sensitive information about training data or reconstruct proprietary model behavior. Through repeated queries, attackers infer internal characteristics of the model and its underlying data. These attacks raise significant concerns related to privacy, intellectual property protection, and regulatory compliance.
AI Logic Abuse
AI logic abuse exploits how models generalize and apply learned patterns. Attackers manipulate inputs to trigger edge cases, exploit bias, or induce unwarranted confidence in incorrect outputs. This category often overlaps with other adversarial techniques and highlights the inherent difficulty of securing learned logic.
Real-World Examples of Adversarial AI Attacks
Adversarial AI attacks are already affecting widely deployed systems. In many cases, attackers manipulate model behavior through carefully crafted inputs rather than exploiting traditional software vulnerabilities. Several documented incidents and studies illustrate how these attacks work in practice.
One example involved a security issue in Slack’s generative AI assistant disclosed by PromptArmor. An indirect prompt injection attack could cause Slack AI to retrieve sensitive information from private Slack channels accessible to the target user and expose it through model responses. The attack embedded malicious instructions inside content that the AI assistant was designed to summarize or retrieve. Because the model interpreted those instructions as legitimate input, it could reveal information that the attacker would normally be unable to access. PromptArmor reported the issue to Slack through responsible disclosure (PromptArmor, n.d.).
Enterprise productivity tools have also demonstrated similar risks. Researchers identified a vulnerability known as EchoLeak affecting Microsoft 365 CoPilot. In this case, a specially crafted email contained hidden instructions that the AI assistant interpreted as valid prompts during normal processing. The attack could cause CoPilot to retrieve sensitive internal data and disclose it externally without requiring user interaction. The research highlights how prompt injections can create data-exfiltration paths within enterprise AI assistants (Reddy & Gujral, 2025).
Adversarial manipulation has also been demonstrated against widely used consumer AI systems. Researchers showed that training data could be extracted from ChatGPT through a divergence attack. By prompting the model to diverge from its intended chatbot behavior, it eventually diverged and began reproducing memorized fragments of training data, including sensitive information. The experiment illustrated how large language models can unintentionally reveal information embedded in their training datasets (Nasr et al., 2023).
Multimodal AI systems introduce additional attack surfaces. Researchers studying multimodal large language models demonstrated that safety controls could be bypassed by using meticulously crafted images to hide and amplify harmful intent. When the model interpreted the image, it followed the hidden instructions and produced responses that violated safety policies. These findings demonstrate how adversarial techniques can exploit the interaction between computer vision and language models in multimodal systems (Li et al., 2025).
Computer vision systems provide another well-known example of adversarial manipulation. One of the earlier demonstrations showed that small physical modifications to a stop sign, such as strategically placed stickers, could cause deep learning road sign classifiers to misclassify the sign as a different class. These physical adversarial examples demonstrated that even minor perturbations to real-world inputs can cause deep learning systems to produce incorrect classifications under realistic conditions. The research highlighted how models that appear highly accurate in controlled environments may still be vulnerable when adversaries intentionally manipulate inputs (Eykholt et al., 2018).
These cases share a common pattern. The systems involved continue functioning as designed from a software perspective. The vulnerability arises from how adversarial inputs interact with learned decision-making processes. When attackers understand how AI models interpret inputs and generalize from training data, they can influence system behavior even in environments that appear secure through traditional testing.
Why Traditional Security Testing Misses Adversarial AI
Traditional security testing focuses on deterministic failures. Penetration tests, vulnerability scans, and red team exercises aim to identify exploitable flaws in code, configuration, and infrastructure.
Adversarial AI attacks rarely trigger these controls. They rely on valid inputs, follow normal workflows, and use expected interfaces. The system behaves correctly from a technical standpoint while producing incorrect or harmful outcomes.
As a result, AI systems may pass conventional security assessments while remaining vulnerable to adversarial manipulation. Security teams receive a false sense of assurance that critical decision-making systems are adequately protected.
Adversarial AI as an Offensive Security Discipline
Addressing adversarial AI requires an offensive security mindset that extends beyond traditional techniques. Practitioners must understand how models learn, how they generalize, and how they fail under adversarial pressure.
Adversarial AI testing emphasizes experimentation, hypothesis-driven analysis, and behavioral validation. It requires close collaboration between security professionals and AI engineers to identify meaningful risks and test realistic attack scenarios. This approach treats AI systems as dynamic decision makers rather than static software components.
The Skills Gap: Why Most Professionals Are Not Ready
Most security professionals have limited exposure to how machine learning systems are designed, trained, and deployed. Traditional security education emphasizes deterministic systems, clearly defined logic paths, and repeatable outcomes. AI systems operate differently, creating a meaningful skills gap.
Many security practitioners lack a working understanding of how models learn from data, how training distributions influence decision-making, and how generalization introduces risk. Without this foundation, it becomes difficult to understand how an attacker might intentionally shape inputs to influence outcomes. Concepts such as confidence thresholds, feature weighting, and model drift are often unfamiliar territory for security teams.
Another major gap involves testing methodology. Traditional offensive testing focuses on exploiting known weaknesses or misconfigurations. Adversarial AI requires a different approach that emphasizes experimentation, behavioral analysis, and hypothesis-driven testing. Practitioners must learn to probe decision boundaries, measure output sensitivity, and identify conditions under which models fail silently rather than catastrophically.
Without targeted education and hands-on experience, most security professionals are poorly equipped to identify, test, and communicate the risks of adversarial AI. This gap will continue to widen as AI systems become more deeply embedded in security-critical workflows.
Building Capability to Address Adversarial AI
Adversarial AI is not a theoretical concern. As this article demonstrates, these attacks are already occurring in production environments across industries where AI has been deployed. The challenge for security teams is that traditional security testing was not designed to detect these failure modes, and most practitioners do not yet have the methodological framework to systematically identify them. The CRAGE certification addresses this from the governance and responsible AI perspectives, equipping leaders to build oversight structures that make adversarial manipulation harder to execute and easier to detect. COASP addresses it from the offensive side, training practitioners in the specific attack techniques, tooling, and assessment methodologies required for AI security testing. For security professionals who recognize the gap between their current skill set and the attack surface described in this article, either program provides a structured path to closing it.
References
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., & Song, D. (2018, April 10). Robust Physical-World Attacks on Deep Learning Models (arXiv:1707.08945). arXiv. https://arxiv.org/abs/1707.08945
Li, Y., Guo, H., Zhou, K., Zhao, W. X., & Wen, J.-R. (2025, January 13). Images are Achilles’ Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models. arXiv. https://arxiv.org/abs/2403.09792
Nasr, M., Carlini, N., Hayase, J., Jagielski, M., Cooper, A. F., Ippolito, D., Choquette-Choo, C. A., Wallace, E., Tramèr, F., & Lee, K. (2023, November 28). Scalable Extraction of Training Data from (Production) Language Models. arXiv. https://arxiv.org/abs/2311.17035
PromptArmor. (n.d.). Data Exfiltration from Slack AI via Indirect Prompt Injection. https://www.promptarmor.com/resources/data-exfiltration-from-slack-ai-via-indirect-prompt-injection
Reddy, P., & Gujral, A. S. (2025, September 6). EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM System. arXiv. https://arxiv.org/abs/2509.10540
About the Author
Dr. Donnie Wendt
Dr. Donnie Wendt is the author of The Cybersecurity Trinity: AI, Automation, and Active Cyber Defense and AI Strategy and Security: A Roadmap for Secure, Responsible, and Resilient AI Adoption, and a coauthor of the open-source AI Adoption and Management Framework (AI-AMF). A recognized voice in AI security, his work focuses on the intersection of cybersecurity, automation, and artificial intelligence.
Over a 30-year career spanning software development, network engineering, security engineering, and AI innovation, Donnie served as a principal security researcher at Mastercard, where he explored emerging threats and AI-driven defense systems. Today, he is a cybersecurity lecturer at Columbus State University and advises organizations on responsible and secure AI adoption.
