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.
LLM agents are moving from demos into production, but prompt filters alone won't stop unsafe tool calls or data exfiltration. This post explains how runtime policy enforcement can constrain agent actions without breaking useful automation.
Prompt injection is now a practical attack path against LLM apps, agents, and RAG systems—not just a research curiosity. This article shows how teams can test for it, harden tool use, and measure whether guardrails actually block malicious instructions.
As AI agents move from demos to production workflows, prompt injection remains the easiest way to turn a helpful model into a data-leaking one. This post breaks down which defenses—sandboxing, tool अनुमति gating, and output validation—actually reduce risk, and where teams still overtrust them.
Prompt injection is no longer just a chat app problem; it is a control-plane risk for AI agents that can browse, call tools, and act on data. This post shows how policy-grounded agents reduce unsafe actions without killing useful autonomy.
Prompt injection is still one of the easiest ways to make LLM apps leak data or ignore policy, especially when tools, retrieval, and memory are wired together. This post shows what modern AI red teaming needs to test—and why static guardrails fail in real deployments.
As AI agents start handling tickets, code, and customer data, red teaming is shifting from a one-off evaluation to a repeatable control for catching prompt injection, data leakage, and unsafe tool use before production. The real question is whether your AI system can survive an attacker who treats prompts, tools, and memory as one attack surface.
Prompt injection is no longer just a chatbot problem—it can poison retrieval pipelines, leak sensitive context, and steer downstream actions. This post examines practical defenses for securing RAG systems before attackers turn your vector store into an attack path.
As more organizations deploy copilots and RAG apps, prompt injection and data exfiltration have become operational risks, not edge cases. This post asks whether your current testing covers the attack paths that modern AI systems actually expose.
Watermarking is becoming a practical control for identifying text, images, and audio produced by generative AI—but attackers are already testing ways around it. The real question is whether defenders can deploy watermark checks fast enough to flag suspicious content before phishing campaigns, deepfakes, and fraud messages go viral.
Attackers are moving past simple prompt injection and exploiting retrieval, tool calls, and memory to steer LLM apps. This post shows why AI security teams now need retrieval-level controls, policy checks, and continuous red-teaming to keep RAG systems safe.