What Is AI Security Risks?

AI security risks are the ways artificial intelligence systems can be attacked, manipulated, exposed, or misused so their data, models, tools, or outputs stop behaving as intended. The term covers two directions of threat: attacks against AI systems and the use of AI to strengthen an attacker’s own operations.

That distinction matters. A poisoned training set, a stolen model, and a prompt injection attack are risks to the AI system itself. A deepfake fraud call or AI-generated phishing campaign is different: the AI is being used as a tool for abuse. Both belong in AI security, but they call for different controls.

What AI Security Risks Include

AI security risks focus on adversarial activity, misuse, and the protection of AI assets such as training data, model weights, prompts, inference APIs, embeddings, tools, logs, and generated outputs. They are narrower than general AI risk, which also includes issues such as fairness, job displacement, environmental impact, and long-term societal concerns.

A simple way to frame the field is this: traditional cybersecurity protects software, networks, identities, and data. AI security extends that work to statistical, data-driven systems whose behavior can be influenced through training data, prompts, model interfaces, connected tools, and downstream automation.

AI Security Risks vs. Related Terms

The vocabulary around AI risk is still settling, so adjacent terms often get blurred. The table below separates the most common concepts.

In short: adversarial machine learning is a technical subset of AI security; AI security risk is a narrower subset of general AI risk; and AI safety overlaps with security but also covers non-adversarial harms.

Why AI Introduces New Security Risks

AI changes the security picture because models do not behave like ordinary deterministic software. Their outputs depend on data, context, probability, and learned patterns. That creates failure modes that standard application and infrastructure controls do not fully cover.

• Probabilistic behavior. The same system may respond differently depending on prompt wording, context, retrieval data, or small input changes.
• New assets. Training data, model weights, embeddings, prompts, system instructions, and inference APIs all become things attackers may try to steal, poison, or manipulate.
• AI-specific failure modes. Data poisoning, adversarial examples, model extraction, prompt injection, and memorization-based leakage do not map neatly to ordinary software vulnerabilities.
• Tool-connected impact. When AI systems can call APIs, read files, send emails, write code, or act through agents, a manipulated output can become a real downstream action.

The practical lesson is not that AI requires a separate security universe. It means AI systems need traditional controls plus AI-specific controls for data, models, prompts, tools, evaluation, and monitoring.

Common Types of AI Security Risks

The categories below reflect common language used across adversarial machine learning research, NIST guidance, and LLM application security work. Where useful, they are mapped to NIST’s adversarial ML groupings: evasion, poisoning, privacy, and misuse or abuse.

Data Poisoning

Attack on AI – poisoning. Data poisoning happens when malicious or manipulated data enters a training, fine-tuning, or retrieval pipeline. The attacker may try to reduce accuracy, create biased behavior, or plant a hidden trigger that activates under specific conditions.

Adversarial Attacks (Evasion)

Attack on AI – evasion. Evasion attacks use crafted inputs at inference time to make a model produce the wrong result. The change may be obvious in text, or nearly invisible in images, audio, or sensor data.

Prompt Injection and Jailbreaking

Attack on AI – misuse/evasion. Prompt injection uses malicious instructions to override or redirect a model’s intended behavior. In direct prompt injection, the attacker puts the instruction in the user prompt. In indirect prompt injection, the instruction is hidden in content the model reads, such as a webpage, document, email, ticket, or tool response.

Sensitive Data Leakage and Privacy Compromise

Attack on AI – privacy. AI systems may expose sensitive data through training-data memorization, logs, poorly governed prompts, retrieval systems, or generated outputs. The risk is especially high when employees paste confidential content into unsanctioned tools or when AI applications retrieve data without proper access checks.

Model Theft and Extraction

Attack on AI – confidentiality/privacy. Model theft includes stealing model weights, copying a hosted model’s behavior through repeated querying, or extracting enough information to build a close substitute. This threatens intellectual property and can help attackers study the model for future abuse.

API Exploitation and Unbounded Consumption

Attack on AI – availability and abuse. AI systems are often exposed through APIs. Weak authentication, poor rate limits, insecure integrations, or excessive permissions can let attackers exfiltrate data, abuse compute, trigger expensive inference, or degrade service. OWASP’s 2025 LLM risk list treats unbounded consumption as a distinct concern.

Shadow AI

Exposure risk – misuse. Shadow AI is the use of unapproved AI tools, accounts, agents, or integrations outside organizational visibility. It creates risk because security teams cannot enforce retention, access control, monitoring, or data-loss policies for tools they do not know are being used.

Excessive Agency and Agentic AI Risks

Attack on AI – abuse. Excessive agency occurs when an AI system or agent has more functionality, permissions, or autonomy than it needs. The risk rises when agents can call tools, change records, send messages, move files, write code, or act across business systems.

• Goal hijacking: an attacker manipulates the agent’s objective.
• Over-permissioned tools: the agent can read, write, delete, or transfer data beyond what the task requires.
• Memory poisoning: stored context is corrupted so future decisions are influenced.
• Unsafe autonomous action: high-impact actions occur without human approval or independent authorization.

Misuse for Malicious Purposes

AI as an attack tool – abuse. Attackers can use AI to scale phishing, generate malware variants, write convincing impersonation messages, create deepfakes, automate reconnaissance, or produce disinformation. The AI system may not be compromised; the risk comes from how its capabilities are used.

The AI Attack Lifecycle

AI security is not only an inference-time problem. Risks appear from data collection through deployment, operation, and retirement. Mapping risks to lifecycle stages helps teams put controls where they actually matter.

The lifecycle view prevents a common mistake: treating AI security as a prompt-filtering problem. Filters help, but they do not protect training data, model weights, supply chains, tool permissions, or monitoring pipelines by themselves.

Frameworks for Managing AI Security Risk

Several authoritative frameworks give teams a shared language for AI security risk. They are useful for structure, but they are not a substitute for threat modeling a specific system.

• NIST AI Risk Management Framework (AI RMF 1.0) was released on January 26, 2023. It is voluntary guidance organized around Govern, Map, Measure, and Manage.
• NIST AI 600-1, the Generative AI Profile, was published on July 26, 2024 and updated April 8, 2026. It is a companion profile to AI RMF 1.0 for generative AI risk management.
• NIST AI 100-2e2025 updates NIST’s adversarial machine learning taxonomy and discusses evasion, poisoning, privacy, and misuse/abuse attacks across predictive and generative AI systems.
• OWASP’s 2025 Top 10 for LLMs and GenAI Apps covers application-level risks such as prompt injection, sensitive information disclosure, supply-chain risk, data and model poisoning, excessive agency, unbounded consumption, and model theft.
• The NIST Cybersecurity Framework 2.0, published February 26, 2024, can help integrate AI security into broader cybersecurity governance through Govern, Identify, Protect, Detect, Respond, and Recover.

A mature AI security program usually draws from more than one framework: AI RMF for risk governance, adversarial ML taxonomies for attack language, OWASP for application-level LLM risks, and CSF for integration with existing security operations.

Frequently Asked Questions

What is the difference between AI risk and AI security risk?

AI risk is the umbrella category. It includes security, safety, legal, operational, ethical, reputational, and societal risks. AI security risk is narrower: it focuses on adversarial attacks, misuse, and protection of AI systems, data, models, tools, and outputs.

What are the most common AI security risks?

Common categories include data poisoning, evasion attacks, prompt injection, sensitive information disclosure, model theft, API exploitation, shadow AI, excessive agency, and misuse of AI for phishing, deepfakes, malware, or reconnaissance.

How is AI security different from traditional cybersecurity?

Traditional cybersecurity protects infrastructure, applications, identities, and data. AI security adds protection for AI-specific assets and behaviors: training data, model weights, embeddings, prompts, inference APIs, retrieval systems, and tool-connected agents.

Conclusion

AI security risks are adversarial attacks on AI systems and the misuse of AI as an attack tool. They sit inside the broader landscape of AI risk, but they deserve their own vocabulary because models, training data, prompts, inference APIs, and agents create attack surfaces that conventional security programs were not designed to handle alone.

The most useful security posture is layered: protect the AI lifecycle, limit what models and agents can access, validate behavior continuously, and connect AI-specific risk management to established cybersecurity governance. That gives teams a practical way to secure AI without pretending the problem is either brand new or already solved by existing tools.