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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

Read the source

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), an attack that manipulates language model (LLM) agents by exploiting their reliance on third-party skills for task selection and planning. The attack can lead to increased token consumption and execution time, while still allowing the agent to complete tasks. This issue was found across multiple LLM backends and 491 held-out tasks.

What the Document Says

  • The text discusses Convergent Detour Hijacking (CDH), a text-based, runtime-independent attack on LLM agents that use third-party skills for task selection and planning.
  • CDH involves an attacker-controlled coordinator, unnecessary benign skills, and a detour to increase token consumption and execution time without significantly affecting task completion.
  • The evaluation of CDH was conducted across multiple LLM backends and 491 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 by 66.91% and execution time by 92.45%, while still maintaining comparable aggregate task completion.

Key Concepts Mentioned

  • Convergent Detour Hijacking (CDH)
  • LLM agents
  • third-party skills
  • token consumption
  • execution time
  • DeepSeek-V4-Pro

Claims Made by the Document

  • CDH is a successful attack on LLM agents that can lead to increased token consumption and execution time without affecting task completion.

Evidence Used

  • The data provided shows an increase in token consumption by 66.91% and execution time by 92.45% while maintaining comparable aggregate task completion on DeepSeek-V4-Pro.

Methods / Approach

  • Evaluation of CDH across multiple LLM backends and 491 held-out tasks

Limitations Stated by the Authors

  • The research only discusses the evaluation of CDH on a specific dataset (DeepSeek-V4-Pro)

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The agent does not have information about how generalizable these findings are to other datasets or types of LLM agents.

Open Questions

  • What would be the impact of CDH on different types of LLM agents and datasets beyond DeepSeek-V4-Pro?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The research highlights that even when an agent completes a task correctly, it doesn't guarantee the integrity or cost-safety of the trajectory.

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.