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 Reinforcement Learning (RL) powers everything from trading bots and robotics to game-playing AIs and recommendation engines. But unlike supervised learning, RL depends on continuous feedback to shape behavior. This makes it uniquely vulnerable to data poisoning attacks that manipulate… 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 AI is not just a tool for defenders — it’s now a weapon in the hands of attackers. With the rise of large language models (LLMs), adversaries can now generate functional malware, obfuscated code, and exploit payloads at a… Read More ›
Overview Phishing is no longer riddled with typos and bad grammar. Thanks to large language models (LLMs), attackers can now generate convincing, context-aware, and linguistically flawless phishing content at scale. What was once a human-limited social engineering tactic is now… Read More ›
Overview AI systems aren’t static. Over time, their performance degrades — not because the model changes, but because the world does. This phenomenon is known as model drift or model decay, and it’s one of the most overlooked risks in… 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 ›
Overview Modern AI systems don’t emerge from a vacuum — they’re built on layers of dependencies: public datasets, third-party model weights, code libraries, pre-trained embeddings, and cloud APIs. This complex supply chain introduces a critical risk: AI supply chain attacks… Read More ›