AI Security Shifts in LLM Applications: Highlighting the OWASP Top 10 for 2025
The cybersecurity landscape is ever-evolving, and the latest development comes with the introduction of the OWASP Top 10 for Large Language Model (LLM) Applications 2025. This refined and forward-looking understanding of the risks associated with AI models aims to help organizations fortify their defenses against the evolving security challenges of AI and LLM workloads.
Prompt Injection remains at the top of the list in the updated OWASP Top 10 for LLM Applications 2025. This vulnerability, which allows an attacker to manipulate the input to an LLM, poses a significant risk and underscores the importance of input validation and sanitisation.
One of the notable additions to the updated list is Vector and Embedding Weaknesses. This new entry focuses on the vulnerabilities in Retrieval-Augmented Generation (RAG) and embedding-based methods, highlighting the need for careful consideration of these techniques' security implications.
Unbounded Consumption, previously known as Denial of Service, has been expanded in the updated OWASP Top 10 for LLM Applications 2025 to include risks tied to resource management and unexpected operational costs. This expansion underscores the importance of efficient resource management and cost control in LLM deployments.
Training Data Poisoning has been expanded to include Data and Model Poisoning in the updated OWASP Top 10 for LLM Applications 2025. This expansion emphasises the need for robust data validation and protection mechanisms to prevent malicious actors from manipulating training data and, in turn, the LLM's output.
System Prompt Leakage is a new vulnerability entry in the updated OWASP Top 10 for LLM Applications 2025, addressing a critical flaw uncovered through real-world incidents. This vulnerability highlights the importance of protecting sensitive information, even during the LLM's training phase.
Excessive Agency is a revised entry in the updated OWASP Top 10 for LLM Applications 2025, addressing the risks associated with the increased autonomy of LLMs in decision-making processes. As LLMs become more integrated into various applications, the risks associated with their autonomy become increasingly significant, and this entry underscores the need for careful consideration of these risks.
Misinformation in the updated OWASP Top 10 for LLM Applications 2025 has been expanded to address Overreliance, emphasising the dangers of unquestioningly trusting LLM outputs. As LLMs become more prevalent, it is essential to maintain a critical perspective and not blindly trust their outputs.
Sensitive Information Disclosure and Supply Chain have significantly jumped up the list from their positions in the 2023 version, underscoring the importance of protecting sensitive information and securing the supply chain in LLM deployments.
The event aims to help attendees stay ahead of the curve and fortify their AI defenses. The half-day virtual event on December 4th, 2024, will discuss the evolving security challenges of AI and LLM workloads. The event will feature AI & LLM security luminaries such as Steve Wilson, Chief Product Officer, Exabeam, and founder and project leader of the OWASP Top 10 for Large Language Model Applications.
Qualys provides comprehensive vulnerability detection for over 1,200 AI/ML-related vulnerabilities. The company helps organizations secure their AI infrastructure effectively, including assessing risks in LLM deployments and preventing model theft. Over 1.65 million AI-related detections have been made by Qualys.
Mayuresh Dani, Manager, Security Research, Qualys, is a contributor to the OWASP Top 10 for Large Language Model Applications.
Registration for the December 4th event is available. Staying ahead of these risks in AI systems is not just prudent-it's essential as LLMs become integral to countless applications.
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