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.
As LLM apps move from pilots to production, prompt injection is turning AI gateways into a practical control point for filtering malicious inputs, enforcing policy, and logging risky model calls. The real question is no longer whether to deploy one, but how to make it effective without breaking useful workflows.
Retrieval-augmented generation can leak secrets, amplify prompt injection, and surface poisoned documents if its data pipeline is not hardened end to end. This post shows the security controls practitioners need before RAG becomes their next production incident.
Prompt injection remains one of the most reliable ways to steer AI assistants into leaking data or taking unsafe actions, even when basic filters are in place. Learn why defenders are shifting from prompt-only controls to model isolation, tool permissioning, and runtime policy enforcement.
Prompt injection, data exfiltration, and tool misuse are no longer edge cases—they’re the failure modes security teams are finding first in production copilots and agentic systems. This post examines how AI red teaming catches these risks before attackers do, and which tests matter most in 2026.
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.