As AI-driven technologies evolve, securing Large Language Model (LLM) applications has become a priority. The OWASP Top 10 for LLM Applications framework identifies the most critical security vulnerabilities in AI-powered systems and offers key guidelines for strengthening AI security.
We are often asked about this list and best practices for securing LLMs. Therefore, this blog is designed for security professionals and engineers responsible for protecting LLMs against emerging threats, providing them with foundational guidance on securing their applications and models. As AI becomes embedded in critical workflows, traditional security approaches often fall short. Risks like prompt injection, data poisoning, and system prompt leakage demand proactiveness, specialized mitigation strategies, and secure practices to prevent exploitation. Understanding these vulnerabilities is key to ensuring AI security, compliance, and resilience in modern enterprises.
The OWASP Top 10 has long been a trusted resource for identifying and addressing cybersecurity risks. With the rise of LLM-based applications, a specialized version — OWASP Top 10 for LLM Applications — has been introduced to tackle AI-specific challenges.
What is the OWASP Top 10 for LLM Applications?
Prompt Injection (Data manipulation) occurs when an attacker manipulates an LLM’s input to change its behavior in unintended ways. Unlike traditional input manipulation attacks, prompt injections do not need to be human-readable — they can be embedded in external data, documents, or even emails. This can lead to responses that violate security policies, bypass safety mechanisms, or expose proprietary information.
For example, attackers used CVE-2024-5184, a vulnerability in EmailGPT, to inject malicious prompts into the AI’s API service. This exploit allowed unauthorized access to sensitive information and manipulation of email content, highlighting the risks of prompt injection within AI-driven applications.
Sensitive Information Disclosure (Data leakage) occurs when an LLM unintentionally exposes private, proprietary, or otherwise confidential data in its outputs. This can include personally identifiable information (PII), financial details, security credentials, trade secrets, or sensitive corporate records.
Supply Chain Vulnerabilities (Data input) arise when LLM applications rely on third-parties, frameworks, or external APIs that introduce security weaknesses. The industry’s reliance on open access further amplifies these risks, as attackers may manipulate public repositories or distribute backdoored models to compromise AI applications.
For example, RayAI is a framework used for training, serving, and tuning AI models, running workloads that often include scripts and commands with sensitive credentials. While a lack of authentication was an intentional design choice by developers, it allowed attackers who could access the API to submit and delete jobs, retrieve sensitive data, and execute remote commands, putting exposed deployments at risk.
Data and Model Poisoning occurs when attackers manipulate data used in training, fine-tuning, or embedding to introduce vulnerabilities, biases, or backdoors. This can degrade performance, inject harmful behaviors, or activate hidden triggers that alter outputs under specific conditions. Since LLMs often rely on external and unverified data sources, they are especially vulnerable to poisoning attacks that compromise their integrity and security.
Improper Output Handling (Data output) occurs when LLM-generated responses are not properly validated or sanitized before being used in downstream applications. If unchecked, these outputs can lead to security risks such as executing unintended commands, leaking sensitive data, or injecting malicious content into web applications.
Excessive Agency (Data automation) occurs when LLMs are granted too much autonomy, allowing them to perform actions that should require human oversight. This could involve executing API calls, making decisions, or modifying system settings without sufficient safeguards.
System Prompt Leakage (Data exposure) occurs when hidden instructions inside an LLM are inadvertently exposed. These instructions, or prompts, may include rules or other sensitive details that control how the AI responds. If an attacker gains access to these prompts, they can manipulate LLM behavior, escalate privileges, or alter outputs.
Vector and Embedding Weaknesses (Data poisoning) arise when vulnerabilities in how embeddings are generated, stored, or retrieved create security risks. Embeddings are numerical representations of data that LLMs use to understand relationships and meaning. Attackers can manipulate these representations to inject harmful data, alter retrieval-augmented generation (RAG) outputs, or extract sensitive information. Poorly secured embeddings may also allow unauthorized access to proprietary datasets or introduce adversarial manipulations that degrade model performance.
Misinformation is a prevalent issue when LLMs generate factually incorrect or biased content. Since AI models rely on probabilistic inference rather than verified knowledge, they may produce convincing yet inaccurate information. Overreliance on LLM-generated information without verification can amplify risks, leading to the spread of false narratives and incorrect decision-making in critical applications.
Air Canada’s chatbot provided false information about bereavement fares, misleading a passenger into booking a full-fare ticket under false assumptions. The airline was ultimately held liable for the misinformation, demonstrating how unchecked content can lead to legal and financial ramifications.
Unbounded Consumption (Data exfiltration) happens when LLM applications fail to regulate excessive resource usage, allowing users to conduct unrestricted queries that deplete system resources. Attackers can exploit this by generating prolonged interactions, launching denial-of-service (DoS) attacks, or replicating model behaviors to extract proprietary knowledge. The high computational costs of LLM inference make unregulated usage a significant security and financial risk for cloud-based and on-premises deployments.
Just as the OWASP Top 10 has been crucial for securing cloud applications at the application layer, it now offers similar guidance for securing LLM applications in AI-driven environments. Protecting LLMs requires a proactive approach to mitigate risks identified in the OWASP Top 10. Best practices begin with Zero Trust principles, ensuring that only authorized users and applications can interact with LLMs. Implementing access controls, enforcing least privilege, and maintaining continuous monitoring help defend against prompt injections, excessive agency, and unauthorized access to AI models. Additionally, data loss prevention (DLP) and content filtering play a critical role in preventing the exposure of sensitive information in AI-generated responses. As AI security threats evolve, organizations must adopt a comprehensive approach to protecting LLM applications. Implementing robust access controls, continuous monitoring, and supply chain security measures is essential to mitigating emerging risks. Future blogs will explore specific threats in greater detail and provide practical strategies to enhance AI security in enterprise environments.
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