Artificial intelligence is moving from experimentation to infrastructure. In many companies, Large Language Models are already embedded into customer support, internal knowledge systems, development workflows, and decision support tools. Yet one pattern continues to repeat itself: teams underestimate how easily these systems can expose sensitive information or be manipulated.
Over the past few years, the security community has been closely studying the real-world risks of deploying LLMs at scale. The Open Worldwide Application Security Project, widely known as OWASP, published its first OWASP Top 10 for Large Language Model Applications in 2023. Since then, the list has evolved as organizations gained practical experience deploying AI systems in production.
The 2026 landscape makes one thing clear: AI security is no longer theoretical. It is operational.
Below are the ten most important security risks every organization building or deploying LLM-powered systems should understand.
1. Prompt Injection
Prompt injection remains the number one threat to AI systems. At its core, prompt injection is a manipulation technique where an attacker convinces the model to ignore its original instructions. LLMs operate based on prompts that define their behavior, often called system prompts. If an attacker successfully overrides or manipulates those instructions, the AI can be coerced into revealing sensitive data, bypassing safeguards, or performing unintended actions.
The challenge is that prompt injection attacks can be subtle. Attackers may disguise malicious instructions inside legitimate requests, creative formats, or even documents that the model is asked to summarize.
2. Sensitive Information Disclosure
Sensitive information disclosure has become a significantly larger concern than many teams initially expected. Many organizations fine-tune models or build AI systems on top of proprietary data such as customer records, financial documents, healthcare information, or internal knowledge bases. If the model is improperly protected, a determined attacker can extract fragments of that data through repeated querying.
In extreme cases, attackers can perform what is known as model inversion, gradually reconstructing sensitive information or proprietary logic embedded within the model.
3. Supply Chain Vulnerabilities
Supply chain vulnerabilities represent another growing risk. Most companies do not train foundation models themselves; instead they rely on pre-trained models, libraries, and tooling from external ecosystems such as the widely used platform Hugging Face. While this ecosystem accelerates development, it also introduces trust challenges.
Models, datasets, and plugins may contain hidden vulnerabilities or malicious components. Without clear provenance and proper vetting of external dependencies, organizations may unknowingly introduce risk into their AI stack.
4. Data and Model Poisoning
Data and model poisoning attacks target the lifeblood of machine learning systems: training data. If attackers can introduce manipulated or malicious data into a training pipeline or knowledge base, the model's behavior can be subtly altered. In some cases, poisoned data can cause systematic bias, incorrect recommendations, or the promotion of specific products or viewpoints.
This risk also affects systems that rely on Retrieval-Augmented Generation (RAG), where the model retrieves external documents to answer questions. If those documents are compromised, the model will confidently produce incorrect or malicious responses.
5. Improper Output Handling
Improper output handling is another surprisingly common mistake. Many teams integrate LLM output directly into software pipelines, code generation tools, or automation workflows. However, LLM output should always be treated as untrusted input.
AI-generated code can contain vulnerabilities such as SQL injection or cross-site scripting flaws. Allowing unverified output to run directly in production systems introduces unnecessary risk.
6. Excessive Agency
Excessive agency occurs when AI systems are granted too much power. Modern AI agents can call APIs, interact with internal services, trigger workflows, and sometimes even control physical infrastructure. When a model with broad permissions is manipulated through prompt injection or hallucination, the consequences extend beyond incorrect answers.
The AI could trigger destructive actions or expose sensitive systems. Limiting permissions and enforcing strict boundaries is critical.
7. System Prompt Leakage
System prompt leakage is another subtle but dangerous issue. The system prompt defines the hidden instructions guiding the model's behavior. In poorly designed systems, these prompts sometimes contain confidential information such as API keys, internal URLs, or operational instructions.
If attackers manage to extract the system prompt, they gain insight into how the AI operates and potentially access to other connected systems.
8. Vector Database and Embedding Weaknesses
Vector database and embedding weaknesses are particularly relevant for organizations using RAG architectures. Vector databases store encoded representations of documents that models retrieve when generating responses. If malicious or unauthorized data enters this system, it can persist and influence outputs long after the initial compromise.
Over time, this contamination can degrade trust in the entire knowledge system.
9. Misinformation
Misinformation is an inherent challenge with generative models. LLMs are designed to predict plausible text rather than verify factual accuracy. This means hallucinations are inevitable. In low-stakes contexts this may be harmless, but in business environments misinformation can lead to incorrect decisions, legal exposure, or reputational damage.
AI systems should always be paired with verification mechanisms and human oversight.
10. Unbounded Consumption
Finally, unbounded consumption represents a financial and operational threat. LLMs require substantial computational resources. Attackers can exploit this by sending large volumes of complex queries, effectively creating a denial-of-service scenario. In cloud-based environments this often manifests as “denial of wallet,” where the primary damage comes from runaway infrastructure costs rather than system downtime.
Conclusion
The most important takeaway is that AI security is not a single control or tool. It is a continuous discipline. Organizations deploying LLMs must think about security across the entire lifecycle: training data, model selection, infrastructure, application logic, and user interaction.
The organizations that succeed with AI over the next decade will not just build powerful models. They will build secure systems around them.
Understanding the risks is the first step. Designing resilient AI architectures is the next.