Covert Channel Data Exfiltration in LLM Agents
Attackers can trick AI agents into leaking data through normal-looking tool calls and image renders.
Attackers can trick AI agents into leaking data through normal-looking tool calls and image renders.
Employees paste confidential data into unsanctioned chatbots faster than security teams can stop it.
Regulators now require LLM systems to log prompts, outputs, and policy decisions—not just API calls.
System prompts leak faster and easier than teams expect, through five distinct attack vectors.
Attackers exploit markdown rendering to steal data from LLM outputs without user detection.
Models memorize training data and leak it when prompted the right way.
Most sensitive data now enters AI tools, and legacy security tools can't detect it.
Three attack pathways let chatbots leak customer data despite traditional security.
Enterprise LLM logs leak personal data at nearly every stage of the pipeline.
Attackers exploit legitimate tool calls to exfiltrate data without touching your firewall.
Attackers exploited over 90 organizations' AI tools to steal credentials and cryptocurrency in 2025.
Platforms claiming framework alignment often lack the runtime controls to prove it.