prompt engineering security Archives

LLM06:2025 Excessive Agency — A Critical Vulnerability in the Age of LLM Autonomy

18 June 2025 by Krishna

The surge of Large Language Model (LLM)-driven applications has revolutionised how businesses interact with data, automate processes, and deliver enhanced user experiences. From autonomous customer service bots to intelligent data summarisation tools and generative co-pilots, LLMs are transforming enterprise workflows at an astonishing pace.
However, this rise has not come without significant risk. Among the top concerns identified in the OWASP Top 10 for LLM Applications v2.0, LLM06:2025 – Excessive Agency stands out as a particularly insidious and business-critical vulnerability. It affects systems where LLMs are entrusted not only with information retrieval or generation but with the ability to act on behalf of users — often through invoking external tools, plugins, or APIs.

LLM05:2025 – Improper Output Handling in LLM Applications: A Business Risk Executive Leaders Must Not Ignore

17 June 2025 by Krishna

At its core, Improper Output Handling refers to inadequate validation, sanitisation, and management of outputs generated by large language models before those outputs are passed downstream—whether to user interfaces, databases, APIs, third-party services, or even human recipients.

LLM03:2025 — Navigating Supply Chain Vulnerabilities in Large Language Model (LLM) Applications

10 June 2025 by Krishna

As the adoption of Large Language Models (LLMs) accelerates across industries—from customer service to legal advisory, healthcare, and finance—supply chain integrity has emerged as a cornerstone for trustworthy, secure, and scalable AI deployment. Unlike traditional software development, the LLM supply chain encompasses training datasets, pre-trained models, fine-tuning techniques, and deployment infrastructures—all of which are susceptible to unique attack vectors.