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Researchers report Log-To-Leak prompt injection on MCP agents

2 sources · 2 articlesFirst reported Updated ()
What's new

First merged summary.

Merged summary · x-ai/grok-4.7 · rewritten as coverage arrivesSummaries by Vani AI

A paper finds Log-To-Leak prompt injection succeeds on most tested MCP model-server pairs, with high residual success after defenses.

Researchers describe Log-To-Leak, a post-hoc prompt-injection attack on tool-using LLM agents that use the Model Context Protocol. Poisoned natural-language tool metadata induces an extra attacker-controlled call after the last legitimate call, targeting the user query, MCP server name, server response, and agent response. Across 18 model-server pairs with valid tool execution, they report attack success of 62.64–100% and conditional logging accuracy of 76.55–95.64%. Sixteen pairs change target-task completion by at most 6.63 percentage points. Three defenses still leave residual success above 55% on GitHub MCP with GPT-4o, and an instruction detector flags 85.53% of benign descriptions.

Key facts
  • Payload is built from Trigger, Tool Binding, Justification, and Pressure.
  • Five MCP servers and four LLM agents were evaluated with five metrics.
  • Attack success was 62.64–100% on 18 model-server pairs.
  • Sixteen pairs shift task completion by at most 6.63 percentage points.
  • An instruction detector flags 85.53% of benign tool descriptions.
  • Authors argue hosts should authorize secondary tool calls.
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Timeline

dated events in the reporting · oldest first
  1. Oct 26, 2026arXiv paper describes the Log-To-Leak MCP prompt-injection attack. · arXiv cs.CR

What each outlet adds

beyond the shared facts
  • arXiv cs.CRAttack design, success rates, task-completion impact, and residual defense results.
Products
Model Context ProtocolGitHub MCP
Malware
Log-To-Leak
AI models
GPT-4o

Coverage

oldest first · one row per article · UTC
  1. · 4d ago arXiv cs.CRFirst report

    Log-To-Leak prompt injection extracts MCP agent data after real calls

    Across 18 model-server pairs, attack success is 62.64–100%, and defenses still exceed 55% ASR on GitHub MCP with GPT-4o.

  2. · 1d ago CSO Online

    Opinion: lock down MCP servers before an LLM rollout

    The piece treats MCP hosts like database drivers and lists controls from input checks to human approval of high-risk actions.