{
 "public_id": "AIR-2025-0017",
 "incident_id": "2025-04-mcp-tool-poisoning-attacks",
 "date_disclosed": "2025-04-01",
 "date_occurred": null,
 "title": "MCP Tool Poisoning Attacks: hidden instructions in tool descriptions, rug pulls, and cross-server shadowing",
 "class": "research_demo",
 "attack_surface": "mcp_server",
 "initial_vector": "poisoned_tool_description",
 "agent_capability_abused": [
  "tool_invocation",
  "file_read",
  "tool_description_ingestion",
  "tool_schema_ingestion",
  "tool_selection"
 ],
 "target_product": "Model Context Protocol clients (Cursor demonstrated; Claude Desktop, Zapier, Openrouter AI listed as susceptible)",
 "target_vendor": "Anthropic (protocol) / multiple MCP clients",
 "autonomy_level": "copilot_with_approval",
 "impact_realized": false,
 "impact_type": [
  "data_exfiltration",
  "unauthorized_action",
  "credential_theft",
  "agent_hijack"
 ],
 "blast_radius": "any agent connected to an untrusted MCP server",
 "cve": [],
 "taxonomy": {
  "owasp_asi": [
   "ASI01",
   "ASI05",
   "ASI04",
   "ASI02"
  ],
  "mitre_atlas": [
   "AML.CS0054"
  ],
  "labels": [
   "tool_poisoning",
   "rug_pull",
   "cross_server_shadowing",
   "invisible_instructions",
   "mcp",
   "shadowing"
  ]
 },
 "defense_present": "user approval of tool list at connect time; tool descriptions shown truncated in UIs",
 "defense_bypassed": true,
 "fix_shipped": "none at protocol level at time of disclosure; Invariant shipped scanning/guardrails",
 "primary_sources": [
  "https://invariantlabs.ai/blog/mcp-security-notification-tool-poisoning-attacks",
  "https://github.com/invariantlabs-ai/mcp-injection-experiments",
  "https://raw.githubusercontent.com/mitre-atlas/atlas-data/main/dist/v6/ATLAS-2026.07.yaml"
 ],
 "verified_quote": "A malicious server can change the tool description after the client has already approved it. This means that even if a user initially trusts a server, they can still be vulnerable to attacks if the server later modifies the tool description.",
 "confidence": "high",
 "notes": "Foundational disclosure that named the 'MCP rug pull' (tool definitions mutated after approval, via notifications/tools/list_changed) and 'cross-server shadowing' (a malicious server poisoning the behaviour of a co-resident trusted server). Follow-up post (2025-04-07) demonstrated exfiltration of WhatsApp chat history via a shadowed WhatsApp MCP server - that follow-up was not separately cited here. | Merged from duplicate id `2025-04-invariant-mcp-tool-poisoning`. Also reported under the titles: 'MCP Tool Poisoning Attacks: malicious instructions in tool docstrings hijack agents and exfiltrate credentials'; 'MCP Tool Poisoning Attacks: hidden instructions in tool descriptions and cross-server shadowing'.",
 "risk_category": "security_fraud",
 "risk_secondary": [
  "privacy"
 ],
 "risk_subcategory": "Tool & Function Call Extraction / Abuse",
 "risk_sub_secondary": [
  "Prompt Injection",
  "Supply Chain Compromise",
  "Credential & Secret Theft"
 ],
 "ai_entity": "generative",
 "agency": "agent_acted",
 "agency_debatable": false,
 "agency_reason": "the agent read poisoned tool descriptions and invoked tools on them",
 "vector_family": "malicious_component",
 "vector_detail": "poisoned_tool_description",
 "guardrail_kind": [
  "human_approval"
 ],
 "guardrail_outcome": "silent",
 "xrefs": {
  "aiid": [],
  "atlas": [
   "AML.CS0054"
  ],
  "cve": [],
  "ghsa": []
 },
 "primary_asi": "ASI01",
 "agent_framework": "unknown",
 "model_provider": "anthropic",
 "model_name": "",
 "detection_mechanism": "external_researcher",
 "tool_access": "unknown",
 "financial_loss_band": "unknown",
 "eai_category": "unsafe_actuation",
 "eai_domain": "agent_behaviour",
 "eai_secondary": [
  "credential_theft",
  "data_exfiltration_channels",
  "supply_chain_vulnerabilities"
 ],
 "eai_reason": "impact rule: unsafe_actuation",
 "asi_curated": [
  "ASI01",
  "ASI05",
  "ASI04"
 ],
 "asi_scope": "in_scope",
 "asi_provenance": "curated_plus_rule",
 "asi_reason": "curated: ASI01,ASI05,ASI04 | added ASI02 (impact=tool_misuse)",
 "submitted_by": "EAI Internal"
}