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.
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.
Prompt filtering alone is failing against indirect injection and tool-abuse attacks in agentic systems. Learn how runtime policy enforcement can block risky actions without breaking legitimate LLM workflows.
Prompt injection, data exfiltration, and tool misuse are no longer edge cases—they’re the failure modes security teams are finding in real LLM rollouts. This piece breaks down the AI red-teaming techniques practitioners are using to catch them before they hit production.
Prompt injection is still the fastest way to turn a helpful assistant into a data exfiltration path, especially when agents can read files, call tools, or browse the web. This post shows the concrete guardrails teams should deploy now—input isolation, tool अनुमति controls, output filtering, and runtime monitoring.
As AI agents move from demos to production, prompt injection is becoming a supply-chain problem, not just a chat bug. Learn how model context firewalls, tool अनुमति controls, and output filtering can block data exfiltration before an agent follows a malicious instruction.
AI agents are starting to call APIs, query databases, and trigger workflows—often with far more access than they need. Learn how least-privilege design, scoped tokens, and tool sandboxing can stop prompt injection from turning an assistant into an attack path.
LLM products can’t treat compliance as an add-on: GDPR may demand meaningful explanations for automated decisions, HIPAA can make prompts containing PHI a regulated data flow, and SOC 2 now has to cover model access, logging, and vendor risk. The hard question is whether your AI system can prove it handles sensitive data safely—even when the model itself is a black box.
AI now writes spear-phishing that looks tailored, timely, and almost indistinguishable from real internal mail, which is why legacy email filters are missing attacks that exploit context instead of keywords. This post shows what behavioral analysis and LLM-based detection can catch—and where human defenders still outperform the model.
An autonomous AI agent is only as safe as its weakest surface: the model, tools, memory, messages, and the human interface each create distinct paths for prompt injection, data exfiltration, and unauthorized action. This post maps those attack vectors end to end—and shows where defenders should place controls before the agent acts on its own.