Overview Modern AI models don’t just process surface-level patterns — they operate in complex mathematical landscapes called latent spaces, where abstract concepts and relationships are embedded. But what if those hidden spaces are deliberately poisoned? Latent space backdoors are a… Read More ›
Overview AI systems are increasingly fortified with safety features to prevent abuse — from refusing to answer dangerous prompts to avoiding hate speech and misinformation. But as defenses evolve, so do attacks. Welcome to the rise of Jailbreak-as-a-Service (JaaS) —… Read More ›
Overview AI-generated text can be fluent, confident, and completely wrong. This phenomenon — known as hallucination — is one of the most discussed weaknesses of large language models (LLMs). But attackers aren’t just exploiting it passively. Increasingly, they are weaponizing… Read More ›
Overview AI models are not static — especially those integrated into dynamic systems like continuous learning pipelines, data feedback loops, or retraining cycles. Over time, the model you deployed may no longer behave like the model you tested. This phenomenon… Read More ›
Overview As AI assistants become embedded in customer service, legal review, code generation, and sensitive decision-making, much of their behavior is controlled by hidden system instructions or prompts. These prompts define tone, role, boundaries, and safety mechanisms. But what happens… Read More ›
Overview AI models are often trained on sensitive data: medical records, financial histories, customer chats, or internal documents. But what if someone could reverse-engineer that training data from the model itself? Welcome to the world of model inversion attacks —… Read More ›
Overview As generative AI tools become more accessible, a new insider risk has quietly emerged in enterprise environments: shadow models. These are unofficial, internally trained AI models created by employees using corporate data — often without approval, oversight, or security… Read More ›
Overview As AI adoption accelerates, so does the unauthorized development of AI models inside organizations. These are known as shadow models — AI systems trained or fine-tuned by internal teams or individuals outside official governance structures. Like shadow IT, these… Read More ›
Overview As organizations adopt AI tools across critical operations, a new threat vector has emerged from within: the prompt engineer. These individuals have deep access to AI systems, know how to influence outputs, and often manage the prompts that control… Read More ›