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Source note3 min read

Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents

Source

arXiv

arxiv.org/abs/2608.12273v1

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Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Junliang Liu, Ruoyu Li, Wenxin Tang, Jingyu Xiao, Zhenyu Liu, Jingheng Xu, Laizhong Cui
  • Venue: arXiv: cs.CR
  • Published: 2026-08-12
  • Collected: 2026-08-13
  • URL: http://arxiv.org/abs/2608.12273v1
  • PDF: https://arxiv.org/pdf/2608.12273v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

Researchers introduce Convergent Detour Hijacking (CDH), a text-based attack that manipulates third-party skills in LLM agents to make tasks unnecessarily expensive and time-consuming without affecting their final outcome.

What the Document Says

  • LLM agents utilize third-party skills with natural language descriptions for selection and planning.
  • There are two control points that can be manipulated by untrusted publishers: static skill and task instruction bodies.
  • Prior work has studied selection manipulation, malicious instructions, and tool-chain resource amplification separately, leaving their end-to-end composition uncertain.
  • The paper introduces Convergent Detour Hijacking (CDH), an attack that couples these stages under shared semantic cover.
  • CDH attracts an attacker-controlled coordinator alongside legitimate skills, recruits unnecessary benign skills into a detour, and then re-enters the original route to preserve task completion.
  • The evaluation was conducted across multiple LLM backends and 491 held-out tasks under single-task and multi-turn conditions.
  • On DeepSeek-V4-Pro, the matched coordinator is selected in 80.02% of tasks, leading to increased token consumption (66.91%) and execution time (92.45%) without affecting aggregate task completion.

Key Concepts Mentioned

  • LLM agents
  • third-party skills
  • Convergent Detour Hijacking (CDH)
  • selection manipulation
  • malicious skill instructions
  • tool-chain resource amplification

Claims Made by the Document

  • Prior work leaves the end-to-end composition of selection manipulation, malicious instructions, and tool-chain resource amplification unclear.
  • CDH can attract an attacker-controlled coordinator, recruit unnecessary skills, and preserve task completion.
  • On DeepSeek-V4-Pro, CDH increases token consumption and execution time without affecting aggregate task completion.

Evidence Used

  • The study evaluates CDH across multiple LLM backends and 491 tasks on DeepSeek-V4-Pro.

Methods / Approach

  • Introduction of Convergent Detour Hijacking (CDH) attack.
  • Evaluation of CDH's effectiveness in increasing token consumption and execution time.

Limitations Stated by the Authors

  • The study was conducted on a specific LLM backend, DeepSeek-V4-Pro.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The study may not be applicable to other types of LLM backends or different evaluation methodologies.
  • It is unknown if the results would generalize to real-world scenarios with varying task complexity and adversary capabilities.

Open Questions

  • How effective would CDH be on other LLM backends or real-world scenarios?
  • What countermeasures can be implemented to prevent CDH attacks?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • It is important to note that the study focuses on a specific attack technique called Convergent Detour Hijacking (CDH) and its impact on LLM agents.
  • The study does not provide information on how this attack might be prevented or mitigated.

Raw Abstract / Excerpt

LLM agents increasingly rely on third-party skills, using natural-language descriptions for selection and instruction bodies for planning. This progressive-disclosure design exposes two sequential control points to untrusted publishers: a static skill may steer an otherwise correct task onto an unnecessarily costly trajectory. Prior work studies selection manipulation, malicious skill instructions, and tool-chain resource amplification largely separately, leaving their end-to-end composition unclear. We introduce Convergent Detour Hijacking (CDH), a text-only, runtime-independent attack that couples these stages. Under shared semantic cover, a description establishes relevance during selection, while an aligned body reuses that rationale to fabricate plausible dependencies during planning. CDH attracts an attacker-controlled coordinator alongside legitimate skills, recruits unnecessary benign skills into a bounded detour, and then re-enters the original route to preserve task completion. We evaluate it across multiple LLM backends and 491 held-out tasks under single-task and multi-turn conditions. On DeepSeek-V4-Pro, the matched coordinator is selected in 80.02% of tasks; among coordinator-hit runs that complete tasks, token consumption and end-to-end execution time increase by 66.91% and 92.45%, respectively, while aggregate task completion remains comparable. Thus, correct outcomes do not guarantee trajectory integrity or cost safety.