Overview AI agents are no longer static models — they are autonomous systems that plan, reason, and act across digital environments. Whether managing emails, deploying code, or navigating internal tools, these agents are often given privileged access and decision-making capabilities…. Read More ›
Cybersecurity Blog
Exfiltration via AI Channels — Hiding Data in AI Prompts and Outputs
Overview Modern security teams monitor emails, file uploads, and network traffic for signs of exfiltration — but AI models open up a new covert channel. By embedding data inside prompts or manipulating model outputs, attackers can sneak information out of… Read More ›
AI in Malware — LLMs Embedded in Payloads and Toolchains
Overview AI was once a tool for defenders — helping classify malware, detect anomalies, and improve SOC workflows. But now, attackers are embedding language models directly into malware, enabling dynamic payloads, evasive scripting, and autonomous decision-making during intrusions. This new… Read More ›
Autonomous Reconnaissance — How AI Agents Scout for Vulnerabilities Without Human Help
Overview Reconnaissance is the first phase of nearly every cyberattack — gathering information about systems, users, and infrastructure. Traditionally, this required a human attacker. But now, AI agents can automate and accelerate reconnaissance at scale, with alarming precision. Autonomous reconnaissance… Read More ›
Synthetic Identity Fraud Powered by AI — Faking People with Language and Pixels
Overview Identity fraud is nothing new — but AI has made it terrifyingly scalable. From fake resumes to deepfake selfies, synthetic identities are now being crafted by generative models that produce convincing, fully fabricated humans: names, photos, voice, and background… Read More ›
Model Denial of Service (MoDoS) — Overloading AI with Adversarial Input
Overview Denial-of-service attacks are a classic threat to servers and web infrastructure — but as AI models are deployed across APIs, apps, and agents, they too have become targets of disruption. Model Denial of Service (MoDoS) refers to attacks that… Read More ›
Prompt Injection Supply Chains — Exploiting AI Plugins, Extensions, and Integrations
Overview Prompt injection attacks are no longer limited to chat windows. As AI becomes embedded into **complex systems — through plugins, APIs, extensions, and automation layers — prompt injection becomes a *supply chain threat*. Attackers can now manipulate AI behavior… Read More ›
Model Theft — How Attackers Clone Your AI via Query APIs
Overview Deploying an AI model through a public or private API can deliver massive value — enabling chatbots, recommendation engines, fraud detection, and countless other services. But exposing your model through an API also creates a tempting attack surface. Model… Read More ›
Data Poisoning in Open Datasets — When Public Resources Become Attack Vectors
Overview AI systems thrive on data — and much of that data comes from open, publicly available datasets. Whether for training large language models, computer vision systems, or recommendation engines, these datasets are often the backbone of modern machine learning… Read More ›
Adversarial Audio Attacks — Fooling Voice Assistants and Speech Models
Overview We often think of adversarial attacks targeting images or text, but audio models — including voice assistants, speech recognition systems, and audio classifiers — are just as vulnerable. Adversarial audio attacks exploit small, often imperceptible changes in sound to… Read More ›