LLM Security
Most organisations securing AI applications are doing it wrong not because they're careless, but because they're applying web application security thinking to a fundamentally different attack surface. A standard pentest doesn't test prompt injection. It doesn't test whether your RAG system leaks
LLM Security
Large Language Model (LLM) security has become a critical concern as organizations deploy AI systems into production environments that handle sensitive data, internal workflows, and user-facing logic. While many teams rely on prompt filtering, content moderation, or policy-based guardrails, these approaches often fail against real threats. Modern LLM attacks are
LLM Security
Large Language Models (LLMs) have become central to next-generation applications, powering everything from customer service chatbots to complex decision support tools. But with increased use comes increased risk. Adversaries are not just exploiting single prompt bugs; they are actively probing, manipulating, and breaking models using systematic adversarial techniques. This article
LLM Security
Jailbreaking Isn’t a Prompting Problem For a long time, jailbreaks were treated as a curiosity. Someone found a clever prompt, it circulated online, a filter was adjusted, and the incident was written off as an edge case. If you’ve spent years in application security, this likely felt familiar