arxiv: 2605.09889 · v1 · submitted 2026-05-11 · 💻 cs.MA

Recognition: no theorem link

Skill Description Deception Attack against Task Routing in Internet of Agents

Jiayi He , Xiaofeng Luo , Jiawen Kang , Ruichen Zhang , Jianhang Tang , Dong In Kim

Authors on Pith no claims yet

Pith reviewed 2026-05-12 04:50 UTC · model grok-4.3

classification 💻 cs.MA

keywords skill description deceptioninternet of agentstask routingLLM attackmulti-agent securityagent deceptionsemantic routingsystem vulnerability

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The pith

Malicious agents in Internet of Agents systems can fake skill descriptions to hijack task routing with up to 98 percent success.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that the Internet of Agents paradigm routes tasks to agents based on freely alterable self-declared skill descriptions, leaving the system open to deception where malicious agents bias selection toward themselves. This matters because such manipulation can assign tasks to unqualified agents, disrupt intended collaborations, and reduce overall system reliability. The authors formalize the Skill Description Deception attack and supply an LLM-based framework that automatically crafts convincing false descriptions. Experiments across nine representative domains confirm the attack reaches high success rates, showing the vulnerability is both severe and broad.

Core claim

In IoA systems, agents can strategically manipulate their self-declared skill descriptions to bias routing decisions in their favor, increasing the probability they are selected for task execution and thereby disrupting user tasks and degrading system reliability, as demonstrated by an LLM-enabled attack framework achieving up to 98 percent success across nine domains.

What carries the argument

The Skill Description Deception (SDD) attack, an LLM-enabled method that generates deceptive skill descriptions to exploit unverified self-declarations during task routing.

If this is right

IoA task routing must incorporate mechanisms that prevent manipulation of self-reported skills.
High success rates in nine domains indicate the vulnerability applies generally rather than in isolated cases.
User tasks may be executed by unqualified agents, leading to degraded performance and loss of trust.
Secure semantic routing mechanisms are needed to restore reliability in future IoA deployments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Adding external verification of claimed skills would likely reduce or eliminate the attack's effectiveness.
Similar deception risks may exist in any multi-agent system that selects collaborators based on natural-language self-descriptions.
Performance-history or reputation-weighted routing could serve as a practical countermeasure worth testing.

Load-bearing premise

Task routing decisions in IoA rely primarily on unverified self-declared skill descriptions that agents can freely alter.

What would settle it

A controlled test in which skill descriptions must be verified against external records or performance history before routing, and attack success rate drops substantially below the reported levels.

Figures

Figures reproduced from arXiv: 2605.09889 by Dong In Kim, Jianhang Tang, Jiawen Kang, Jiayi He, Ruichen Zhang, Xiaofeng Luo.

**Figure 2.** Figure 2: Workflow of SDD attack based on LLM techniques. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Comparison of attack performance across nine domains, including ASR, Hit@3, Hit@5, and mean rank under different [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

read the original abstract

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 \emph{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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper shows an LLM-powered attack on IoA task routing via fake skill descriptions that works well in description-only simulations but may not generalize if routers use additional signals.

read the letter

The main point is that the authors formalize a Skill Description Deception attack where malicious agents rewrite their skill text with an LLM to bias routing decisions in Internet of Agents systems, and they report up to 98% success across nine domains in their tests. They also supply an automated generation framework to create the deceptive descriptions at scale. That combination is new enough to be worth noting for people working on agent routing security. The empirical demonstration is straightforward and gives a concrete sense of the risk when routing relies on unverified text claims. They do a decent job of showing the attack is general across domains rather than tuned to one narrow case. The experiments are set up so the router sees only the skill descriptions, which lets the attack reach those high rates. Real deployments often layer in reputation, history, or verification steps, and the paper does not run ablations to show how much the success rate drops when those are present. That assumption about description-only routing is the clearest soft spot. The work is aimed at researchers studying security for LLM-driven multi-agent systems. A reader who needs to evaluate or harden routing mechanisms would find the attack model and generator useful as a starting point for their own tests. It has enough of a clear threat model and reproducible-looking results to deserve peer review, though referees will likely ask for more on mixed-signal robustness. I would send it to referees.

Referee Report

2 major / 1 minor

Summary. The paper introduces the Internet of Agents (IoA) paradigm, where heterogeneous agents collaborate via task routing based on self-declared skill descriptions. It proposes and formalizes the Skill Description Deception (SDD) attack, in which malicious agents manipulate these descriptions to bias routing toward themselves. An LLM-enabled framework is presented to automatically generate deceptive descriptions, and experiments on nine representative domains report attack success rates up to 98%, demonstrating the vulnerability and calling for secure semantic routing mechanisms.

Significance. If the results hold under realistic conditions, the work is significant for identifying a novel, practical attack surface in an emerging LLM-driven agent ecosystem. The multi-domain empirical evaluation provides concrete evidence of severity and generality, which could usefully inform the design of trustworthy IoA systems. The LLM-based attack generation approach is timely and directly relevant to the technology studied.

major comments (2)

[§4 (Attack Model)] §4 (Attack Model): The SDD attack is formalized under the assumption that routing decisions are made primarily or exclusively on the basis of unverified, self-declared skill descriptions. This assumption is load-bearing for the central claim of high ASR, yet the manuscript provides no analysis of how success rates change when the routing function incorporates auxiliary signals such as reputation, interaction history, or capability verification.
[Experimental Results] Experimental Results (nine domains): The reported up to 98% ASR is obtained by supplying only the deceptive skill descriptions as input to the router. Without ablations that relax the single-input assumption, it remains unclear whether the attack remains effective against multi-factor routing systems that real IoA deployments are likely to employ.

minor comments (1)

[Abstract] The abstract states results on 'nine representative domains' but does not name them; listing the domains (or a representative subset) in the abstract or early introduction would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the attack model assumptions and experimental design. We address each major comment point by point below, with planned revisions to clarify the scope and limitations of our work.

read point-by-point responses

Referee: [§4 (Attack Model)] The SDD attack is formalized under the assumption that routing decisions are made primarily or exclusively on the basis of unverified, self-declared skill descriptions. This assumption is load-bearing for the central claim of high ASR, yet the manuscript provides no analysis of how success rates change when the routing function incorporates auxiliary signals such as reputation, interaction history, or capability verification.

Authors: The attack model in §4 is defined for the fundamental IoA routing paradigm that relies on self-declared skill descriptions, as this constitutes the novel and overlooked attack surface in the paper. The reported ASR demonstrates the attack's potency under this baseline setting. We agree that auxiliary signals could influence outcomes in deployed systems. In the revised manuscript, we will add a new paragraph to §4 providing a qualitative analysis of how signals such as reputation or verification might mitigate SDD attacks, along with suggestions for integrating them into routing functions. This will strengthen the connection to the paper's call for secure semantic routing. revision: yes
Referee: [Experimental Results] The reported up to 98% ASR is obtained by supplying only the deceptive skill descriptions as input to the router. Without ablations that relax the single-input assumption, it remains unclear whether the attack remains effective against multi-factor routing systems that real IoA deployments are likely to employ.

Authors: The experiments isolate the effect of skill description deception under the single-input routing model to establish a clear baseline across the nine domains. This design choice highlights the vulnerability even without additional factors. We acknowledge the absence of explicit multi-factor ablations. In the revision, we will expand the experimental results section with a dedicated discussion subsection that explores the implications for multi-factor systems, proposes how the attack framework could be extended to target combined signals, and positions the 98% ASR as an indicator of severity in basic configurations. We will also update the limitations and future work to emphasize the need for robust multi-factor routers. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical attack evaluation rests on explicit modeling assumptions, not self-referential derivations

full rationale

The paper formalizes the SDD attack as a threat model where malicious agents alter self-declared skill descriptions to bias task routing, then implements an LLM-based generator and measures success rates empirically on nine domains (up to 98% ASR). No equations, predictions, or first-principles derivations are present that reduce the attack success, routing bias, or framework output to fitted parameters or prior results by construction. The central assumption—that routing decisions rely primarily on unverified skill descriptions—is stated explicitly as the attack surface rather than derived from the paper's own outputs. Experimental results are obtained by direct simulation against that assumption and do not collapse into self-definition or self-citation chains. This is a standard empirical security analysis with independent external benchmarks (simulated routers), warranting a score of 0.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that routing uses raw self-declared descriptions and on the newly introduced SDD attack concept; no numerical free parameters are fitted and no new physical entities are postulated.

axioms (1)

domain assumption Task routing decisions in IoA systems are driven by self-declared skill descriptions without mandatory external verification.
Stated as the basis for the attack surface in the abstract.

invented entities (1)

Skill Description Deception (SDD) attack no independent evidence
purpose: To model and enable systematic generation of deceptive skill descriptions that bias routing decisions.
Newly formalized attack model introduced in the paper.

pith-pipeline@v0.9.0 · 5493 in / 1245 out tokens · 37462 ms · 2026-05-12T04:50:13.651875+00:00 · methodology

discussion (0)

Reference graph

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