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Incomplete MCP Tool-Call Attack Detection

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Analyzed
Paper-reported evidence
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Paper-evaluated models(3)

  • GPT-5.6 Luna
  • DeepSeek V4 Flash
  • DeepSeek V4-Pro
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Description

The MTGuard study evaluates unsafe Model Context Protocol tool calls originating from compromised server data, host-side execution changes, and malicious user-controlled resources. Its hybrid monitor combines pre-execution parameter inspection, behavioral observation, and post-execution result verification across browser-automation and financial-analysis agents.

Examples

Evaluate using mocked MCP tools, synthetic credentials, controlled browser and financial tasks, inert service effects, and independently labeled unsafe calls; see https://arxiv.org/html/2607.25297 (opens in a new tab).

Impact

Across 99 instrumented cases and 240 confirmed unsafe tool calls, MTGuard flags 116 calls (48.3%), compared with 20 calls (8.3%) for the stronger tested baseline. The experiment records denial decisions counterfactually while still allowing calls to execute, and some detections occur after the side effect; these results establish detection on collected traces, not demonstrated prevention or rollback.

Affected Systems

  • Browser-automation and financial-analysis agents using Model Context Protocol tools
  • MCP servers, MCP hosts, tool metadata, declared tool arguments, execution hooks, and tool results
  • MTGuard, Tool Call Governance, and Tool Result Inspection benchmark configurations

Mitigation Steps

  • Enforce trusted pre-execution authorization checks on actual tool names, arguments, destinations, and user-approved scope.
  • Compare declared tool invocations with observed runtime behavior to detect host-side substitution or execution-layer tampering.
  • Treat MCP server metadata and tool results as untrusted inputs with explicit provenance boundaries.
  • Block unsafe calls before irreversible side effects; do not mistake post-execution detection or counterfactual denial for prevention.
  • Measure residual missed-call rates, false positives, latency, and attack-surface-specific coverage before deployment.

Evidence

Research context and provenance

Catalog identifier
LMVD-a5c42467
Internal research identifier, not an official CVE identifier.
Evidence and verification
Paper-reported; independent reproduction is not documented.
Primary source plus a dedicated evidence section.
Severity
Not rated by this catalog.
Source and publication type
arXiv · Research preprint.
Peer-review status is not provided by this source.
Author and publication status
Author metadata is not stored; see the primary paper.
Threat model and attacker access
Ability to influence untrusted model inputs or connected content.
Related deployment categories
Agent workflows; Model Context Protocol; Computer-use agents; Model APIs
Taxonomy labels only; paper-specific deployment prerequisites are not inferred.
Affected systems
Browser-automation and financial-analysis agents using Model Context Protocol tools MCP servers, MCP hosts, tool metadata, declared tool arguments, execution hooks, and tool results MTGuard, Tool Call Governance, and…

Research Paper

Hybrid Analysis for Secure MCP Tool Use in LLM Agents

Primary source: arXiv. Findings are reported by the cited research and have not been independently verified.

View Paper