How Adversarial Environments Mislead Agentic AI?
Researchers have identified a critical 'Trust Gap' in agentic AI, where reliance on external tools creates a vulnerability called Adversarial Environmental Injection (AEI). Using the POTEMKIN harness, the study reveals how agents can be deceived into false beliefs or trapped in infinite loops through environmental manipulation.
Computer Science > Artificial Intelligence
Title:How Adversarial Environments Mislead Agentic AI?
View PDF HTML (experimental)Abstract:Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack surface. Current evaluations benchmark capability in benign settings, asking "can the agent use tools correctly" but never "what if the tools lie". We identify this Trust Gap: agents are evaluated for performance, not for skepticism. We formalize this vulnerability as Adversarial Environmental Injection (AEI), a threat model where adversaries compromise tool outputs to deceive agents. AEI constitutes environmental deception: constructing a "fake world" of poisoned search results and fabricated reference networks around unsuspecting agents. We operationalize this via POTEMKIN, a Model Context Protocol (MCP)-compatible harness for plug-and-play robustness testing. We identify two orthogonal attack surfaces: The Illusion (breadth attacks) poison retrieval to induce epistemic drift toward false beliefs, while The Maze (depth attacks) exploit structural traps to cause policy collapse into infinite loops. Across 11,000+ runs on five frontier agents, we find a stark robustness gap: resistance to one attack often increases vulnerability to the other, demonstrating that epistemic and navigational robustness are distinct capabilities.
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Source: arXiv cs.AI Recent
