AI Security Risks: How to Identify and Reduce the Most Common Exposures

AI systems can fail in ways that traditional security approaches aren’t designed to catch — because the vulnerabilities often sit inside the way the systems learn, interpret input, retrieve context and produce output. Risk can enter through training data, prompts, model interfaces, external dependencies, retrieval pipelines or lightweight internal tools that never went through formal review. The attack surface has expanded, and so has the potential impact of getting it wrong.

The stakes have risen even further as agentic AI systems begin taking autonomous action — retrieving internal data, passing context between components, calling tools and influencing downstream decisions. A vulnerability that once produced a bad answer can now trigger a dangerous outcome. This article maps the main risk categories organizations need to understand across the AI lifecycle, alongside the frameworks and mitigation strategies available to address them.

AI security risks are the threats that target AI and machine learning (ML) systems across the lifecycle — training data, model behavior, inference, retrieval, orchestration and deployment. They sit within the broader threat landscape, but they differ from traditional cybersecurity risks in an important way: many attacks are designed to manipulate model behavior, not just exploit code, identities or infrastructure.

This difference is significant because AI systems are probabilistic — they can be pushed off course by corrupted data, crafted inputs, malicious instructions or unsafe connections to downstream tools, even when the surrounding application is technically working. Stanford HAI's 2025 AI Index reported 233 AI-related incidents in 2024, up 56.4% from 2023, which reflects how quickly this risk environment is expanding as AI systems become more embedded in production workflows.

See our comprehensive guide to AI security to learn more about the risks, frameworks and best practices for securing AI systems end to end.

The current AI attack surface spans classical ML vulnerabilities, LLM application risks and exposures that appear when AI agents access systems, use external tools or operate with autonomy. The categories below are not exhaustive, but they cover the risks most teams need to identify first when evaluating AI risk posture.

Data poisoning corrupts training data or fine-tuning inputs so a model learns the wrong patterns without obvious signs of failure. A poisoned model produces incorrect, biased or attacker-shaped outputs because training data integrity has been compromised.

Read Understanding AI Data Security to learn how to protect the data that trains, grounds and is exposed through AI systems.

Adversarial attacks use carefully crafted inputs to fool ML models into misclassifying data, missing threats or behaving unpredictably. In classical ML settings, these attacks are typically gradient-based (white-box) or query-based (black-box). In LLM systems, the relevant variants include jailbreaking techniques and adversarial prompt suffixes — inputs engineered to bypass safety behaviors or elicit unintended outputs — which test model robustness in ways that don't always map to traditional adversarial example research.

Prompt injection is an LLM vulnerability in which malicious instructions in user input or retrieved content override or interfere with intended system behavior. The risk grows when the system can access tools or move work downstream, because the attack can shape not just what the system produces but the actions it attempts to take.

Read AI Agent Security Explained to learn more about agentic AI risks, why securing agent workflows matters, and best practices for keeping autonomous systems safe and controlled.

Model theft and extraction involve stealing model weights outright or replicating model behavior through repeated API queries. Related privacy attacks — including model inversion and membership inference — can expose information about training data while also creating intellectual property risk for organizations that have invested in proprietary models.

AI supply chain risk enters through pre-trained models, tainted data sets, external APIs, orchestration layers, plug-ins, connectors and other third-party dependencies. This is why the idea of an AI bill of materials (AI-BOM) is becoming more useful — teams need a way to enumerate the components that influence system behavior before a hidden dependency becomes a security gap.

Unlike software bills of materials (SBOMs), which have established NTIA and CISA guidance, AI-BOM practices are still emerging and not yet formally standardized — but the underlying need to track model provenance, data set sources and third-party dependencies is the same.

Shadow AI refers to unauthorized or unreviewed use of AI tools inside the enterprise, including assistants, copilots and lightweight internal agents connected to company data without formal security review. This includes chat assistants and browser-based copilots used outside policy as well as internally deployed agents or integrations connected to company data without formal security review.

The risk profile differs: consumer tools primarily raise data exfiltration and confidentiality concerns, while ungoverned internal agents can also affect system integrity and access control. IBM's 2025 Cost of a Data Breach research found that among organizations reporting AI-related incidents, 97% indicated gaps in access controls and 63% reported lacking formal governance policies to manage AI or prevent the proliferation of shadow AI.

Because AI security risks emerge across data, models, prompts, dependencies and usage patterns, mitigation has to be layered. The goal is to make the system observable, governable and resilient enough that vulnerabilities are harder to exploit and easier to contain.

No single framework fully addresses all AI security risks. Organizations usually need one reference for governance, another for adversarial threats and another for application-specific vulnerabilities, especially in systems that use LLMs or agents. It’s useful to select a small set of complementary references to make security decisions more consistent.

The NIST AI Risk Management Framework is a voluntary framework for managing AI risk across the lifecycle. Its four functions — Govern, Map, Measure and Manage — give teams a structured way to connect governance, contextual analysis, testing and ongoing risk treatment.

MITRE ATLAS is a living knowledge base of adversary tactics and techniques against AI-enabled systems based on real-world attack observations. In practice, it helps teams think about AI-specific attack behavior with more precision than a generic threat model allows.

The OWASP LLM Top 10 focuses on common vulnerabilities in LLM and generative AI applications, including prompt injection, supply chain risk, data and model poisoning, improper output handling and excessive agency. It is especially useful when teams need to move from high-level AI governance language into application review.

These are not the only AI security references teams use. Many organizations also map AI security work to standards such as ISO/IEC 42001 and to control references such as the Cloud Security Alliance’s AI Controls Matrix.

Strong AI security programs usually begin with a clear taxonomy of risks. Once teams can identify the main risk categories, they can assign owners, test likely failure paths and apply governance to the systems that matter most.

That creates the conditions for better security decisions across the lifecycle. A team can assess whether a weakness sits in training data, model access, prompt handling, retrieval logic or third-party dependencies, then apply controls that fit the actual exposure rather than treating AI risk as a broad, indistinct issue.