mlsanityData to 14 Oct 2026

No-Box Vulnerability Analysis: Description-only Detection of Indirect Prompt Injection Vulnerabilities in MCP Servers

arXiv 2609.10854 ↗First posted 9 Sep 2026cs.CR cs.AIcited by 0

On the map: LLM safety and security → Prompt injection and agent security

Abstract (arXiv)

Conventional vulnerability analysis relies on either system access or dynamic interaction, all of which may be unavailable to third-party analysts auditing closed-source, remotely hosted, critical in situ systems, or commercially gated software. Therefore, we propose a new paradigm of no-box vulnerability analysis in which neither access nor runtime interaction is available, and only functionality metadata is available. Such metadata defines the intended behavior of the system, including its inputs, outputs, and side effects, while constraining the space of implementations consistent with that behavior. We propose hypothesizing about vulnerabilities that exist across all possible implementations of a given system metadata, without observing or interacting with the target system. An analyst can later validate these hypotheses when additional access is available. We showcase the feasibility of no-box vulnerability analysis through implementing a prototype called MCPSEC, which audits Model Context Protocol (MCP) servers for indirect prompt injection vulnerabilities using only the tool metadata exposed at server registration time. We evaluate MCPSEC on 20 widely deployed MCP servers comprising 177 tools, among which human evaluators confirm 95 vulnerable tools. MCPSEC identified 143 tools as vulnerable, and for each vulnerable tool, it produced a hypothesized vulnerability along with exploitation technique. Using metadata alone, MCPSEC predicted 94 (98.9% recall) real verified vulnerabilities, compared against an LLM baseline with 80 (84.2% recall). Overall, our results introduce no-box vulnerability analysis as a new analysis paradigm and demonstrate its practical feasibility in realistic systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security. Builds on 18 of its 58 references that are in the map. First posted on arXiv 9 Sep 2026.

Builds on

18 of 58 references.

Closest ancestors · 11

Same direction, newest first

  1. 2025AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection2 Aug
  2. 2025Prompt Injection Attack to Tool Selection in LLM Agents28 Apr
  3. 2025Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents27 Feb
  4. 2025MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents7 Feb
  5. 2025PromptShield: Deployable Detection for Prompt Injection Attacks25 Jan
  6. 2024AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents19 Jun
  7. 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions19 Apr
  8. 2024Defending Against Indirect Prompt Injection Attacks With Spotlighting20 Mar
  9. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar
  10. 2024StruQ: Defending Against Prompt Injection with Structured Queries9 Feb
  11. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection23 Feb

Other references · 7

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2025The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injectionscited by 129
  4. 2023Abusing Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMscited by 124
  5. 2024Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?cited by 81
  6. 2020Practical No-box Adversarial Attacks against DNNscited by 32
  7. 2022Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformationcited by 9

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  1. 2026Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions
  2. 2026VIPER-MCP: Detecting and Exploiting Taint-Style Vulnerabilities in Model Context Protocol Servers
  3. 2026Unsafe by Flow: Uncovering Bidirectional Data-Flow Risks in MCP Ecosystem
  4. 2026MCP-in-SoS: Risk assessment framework for open-source MCP servers
  5. 2026Exposed by Design: A Dynamic Security Assessment of Internet-Facing MCP Servers at Scale
  6. 2025MCPGuard : Automatically Detecting Vulnerabilities in MCP Servers
  7. 2026Model Context Protocol Threat Modeling and Analyzing Vulnerabilities to Prompt Injection with Tool Poisoning
  8. 2026From Component Manipulation to System Compromise: Understanding and Detecting Malicious MCP Servers