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alizafarbati

agent-honeypot

by alizafarbati

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      Enables deterministic security testing of AI agents that use tools by serving synthetic MCP environments with poisoned data, fake secrets, and privileged actions. Records agent tool calls and evaluates security invariants (e.g., canary leaks, forbidden access, approval binding) without an LLM judge or real systems.
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    • A
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      Enables AI agents to defend against prompt injection by exposing decoy tools and transparently proxying real MCP servers to block injected calls.
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    • A
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      Presents a plausible, tool-rich workspace surface — file reading, credential listing, command execution, database queries, and configuration retrieval — that returns only synthetic results while logging every connection, tool call, and argument received. It enables researchers to measure what MCP clients and agents actually do when handed dangerous-sounding tools, including whether they follow injected instructions or complete a retrieve-then-use credential chain.
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    • A
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      Publishes an MCP Streamable HTTP JSON-RPC endpoint where clients can initialize sessions, list and call synthetic deployment, runtime, cluster-inspection, and audit-log tools. While serving those decoy tools, it records initialization, tool discovery, argument shapes, and multi-tool sequences as evidence for classifying and investigating agent-like behavior.
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    • A
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      Simulates sensitive internal tools as a honeypot to detect unauthorized access and AI agent behaviors using Canarytokens, deployed as a serverless Cloudflare Worker with MCP protocol support.
      21
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