CyberIntelAI tracks how AI is changing the threat landscape in real time.
Not just new capabilities, but how attackers are actually using them: the incidents, the tools, and the shifts that matter once systems move into production.
If you're defending modern infrastructure, this is where the signal is.
If employees are already pasting sensitive data into AI tools, what is your governance model doing to stop it? CISOs need a practical framework now: classify inputs, codify acceptable use, rehearse AI-specific incident response, and vet AI vendors before a breach starts with a prompt.
An attacker can turn an LLM into an exfiltration relay by hiding secrets in generated text patterns or by forcing tool calls that send data out through webhooks. This post shows the attack patterns, the telemetry that exposes them, and the controls that block leakage before the model becomes a silent data hose.
A structured red team should test four things in order: threat model, adversarial prompts, tool-abuse paths, and output validation gaps. This post shows a repeatable methodology for finding the failure modes attackers are most likely to exploit in 2026.
When the “user” is an AI agent, zero trust means every prompt, tool call, and data request must be verified, scoped, and logged in real time. This post shows how microsegmentation, just-in-time privilege, continuous identity checks, and tamper-evident audit trails stop agents from becoming an enterprise-wide blast radius.
A HuggingFace model can be more dangerous than it looks: malicious weights, unsafe deserialization (like PyTorch pickle CVEs), and tampered LoRA adapters can all turn a download into code execution or silent backdoors. The real question is: how do you verify provenance before an AI model reaches production?
A single malicious document in your vector store can steer answers, leak hidden instructions, or even exfiltrate sensitive data through a carefully crafted query. This post breaks down where RAG breaks first—and the concrete controls that stop poisoned retrieval, indirect prompt injection, and unauthorized data leakage.
A Hong Kong finance worker was tricked by a deepfake video call into wiring millions—now the same playbook is being industrialized with voice clones, synthetic meetings, and targeted social engineering. Which sectors are most exposed, and which controls actually break the fraud chain before money moves?
Role-playing, token manipulation, and many-shot prompting can steer enterprise LLMs past intended safeguards—even when the model appears well-guarded. The real question is how security teams can detect these attacks early and reduce the risk before sensitive data or workflow controls are exposed.
Employees are pasting source code, customer records, and internal strategy into unauthorized AI tools—often before security even knows those tools exist. This post examines the real leakage paths, practical ways to detect shadow AI across SaaS, browsers, and endpoints, and the policies that reduce risk without blocking legitimate work.
Prompt injection isn’t just “bad input” — indirect attacks can hide inside webpages, emails, or documents and override an AI system’s instructions even when the prompt itself looks clean. This post breaks down why traditional sanitization fails and which defenses actually help today: sandboxing, output validation, and privilege separation.