A comprehensive taxonomy of adversarial attack vectors targeting autonomous AI agent swarms and multi-agent coordination frameworks.
Analyzing how untrusted data retrieved by an agent (from web browsing, incoming emails, or database queries) can contain embedded prompt injection payloads that hijack the agent's core instructions and force it to exfiltrate private credentials.
Architectural patterns for isolating read-only data retrieval tools from dangerous state-modifying tools (such as sending emails, deleting files, or executing financial wire transfers), requiring cryptographic human-in-the-loop signatures for high-impact actions.
How to construct tamper-evident, append-only cryptographic audit trails recording every prompt, tool invocation, and decision made by autonomous agents, satisfying emerging AI governance and liability regulations.