mlsanityData to 7 Oct 2026

Skill Description Deception Attack against Task Routing in Internet of Agents

arXiv 2605.09889 ↗First posted 11 May 2026cs.MAcited by 1

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

A new paradigm, Internet of Agents (IoA), is transforming networked systems into LLM-driven service networks, where heterogeneous agents collaborate through task routing based on their self-declared skill descriptions. Although this promising paradigm enables agentic, distributed, and advanced intelligence, it also exposes a new and overlooked attack surface. In particular, malicious agents can strategically manipulate their skill descriptions to bias routing decisions and increase their probability of being selected for task execution, thereby disrupting user tasks and degrading system reliability. To characterize this threat, we propose and formalize a new attack model, termed Skill Description Deception (SDD) attack. We further design an LLM-enabled SDD attack framework that automatically generates deceptive skill descriptions, enabling systematic vulnerability assessment of IoA systems. Experimental results on nine representative domains show that the proposed attack can achieve up to 98% attack success rate, demonstrating the severity and generality of the attack. Our paper reveals a new security vulnerability in IoA and calls for secure and trustworthy semantic routing mechanisms for future IoA systems.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 16 of its 19 references that are in the map. First posted on arXiv 11 May 2026.

Builds on

16 of 19 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025MPMA: Preference Manipulation Attack Against Model Context Protocol16 May
  2. 2025MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits2 Apr
  3. 2025Red-Teaming LLM Multi-Agent Systems via Communication Attacks20 Feb
  4. 2025G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems16 Feb

Other references · 12

  1. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  2. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  3. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
  4. 2023C-Pack: Packed Resources For General Chinese Embeddingscited by 556
  5. 2025MasRouter: Learning to Route LLMs for Multi-Agent Systemscited by 80
  6. 2025LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?cited by 47
  7. 2025MCP-Zero: Active Tool Discovery for Autonomous LLM Agentscited by 32
  8. 2025Internet of Agents: Fundamentals, Applications, and Challengescited by 31
  9. 2025Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directionscited by 22
  10. 2025Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systemscited by 15
  11. 2024Toward Democratized Generative AI in Next-Generation Mobile Edge Networkscited by 8
  12. 2025BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answeringcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks
  2. 2026SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement
  3. 2026SkillBloat: Token Amplification Attacks via Skill Injection in LLM Coding Agents
  4. 2026SkillTrojan: Backdoor Attacks on Skill-Based Agent Systems
  5. 2026RouteGuard: Internal-Signal Detection of Skill Poisoning in LLM Agents
  6. 2026Hiding in Plain Sight: Decoupling Pretext from Actuation for Skill Poisoning in LLM Agents
  7. 2026SkillHarm: Lifecycle-Aware Skill-Based Attacks via Automated Construction
  8. 2026SkillsMetric: Mapping the Detection Boundary of Static Analysis for Malicious Agent Skills