Covert Channel Data Exfiltration in LLM Agents
Attackers can trick AI agents into leaking data through normal-looking tool calls and image renders.
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13 stories in Data Exfiltration.
Attackers can trick AI agents into leaking data through normal-looking tool calls and image renders.
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 can exfiltrate data through AI tool calls disguised as normal operations.
System prompts leak easily because models treat them as ordinary text with no built-in protections.
Enterprises racing to deploy RAG systems are spending 17 times more on AI tools than securing them.
Permission models dissolve when documents enter vector databases.