Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents
Researchers have uncovered a new attack method called Convergent Detour Hijacking that manipulates LLM agents into inefficient or costly task execution by exploiting third-party skill descriptions.
- Convergent Detour Hijacking (CDH) is a text-only attack that manipulates LLM agent behavior through third-party skill descriptions and instructions.
- The attack exploits the progressive-disclosure design of LLM agents, enabling resource amplification or inefficient task execution without code changes.
- CDH is runtime-independent, meaning it can affect any LLM agent relying on external skills, regardless of the underlying model or platform.
- The research underscores the need for stricter validation and sandboxing of third-party skills to prevent such attacks.
A new research paper from arXiv introduces Convergent Detour Hijacking (CDH), a text-only attack targeting LLM agents that rely on third-party skills. The attack exploits the progressive-disclosure design of these systems, where natural-language skill descriptions and instructions can be manipulated to steer agents toward unnecessarily costly or inefficient task trajectories. Unlike prior work that examined selection manipulation or malicious instructions in isolation, CDH demonstrates how these vulnerabilities can be composed end-to-end, posing a runtime-independent threat that does not require code changes to execute.
The paper highlights that LLM agents increasingly depend on external skills for planning and execution, creating a chain of trust between users, agents, and skill publishers. CDH specifically targets the static skill descriptions and instruction bodies, which are often treated as trusted inputs. By crafting deceptive descriptions or instructions, attackers can subtly redirect an agent's workflow, leading to resource amplification or task degradation without triggering obvious failure modes. This attack is particularly concerning given the growing adoption of modular, skill-based LLM systems in production environments.
The researchers emphasize that CDH operates independently of the underlying LLM or runtime, making it a persistent and adaptable threat. The paper calls for improved validation mechanisms for skill descriptions and instructions, as well as stricter sandboxing and monitoring of third-party skills to mitigate such attacks.
Developers must implement stricter validation and sandboxing for third-party skills to prevent CDH attacks.
Businesses deploying LLM agents should audit their skill ecosystems for potential CDH vulnerabilities to avoid costly inefficiencies.
Investors should consider the security risks of skill-based LLM systems when evaluating AI-driven products.
This attack highlights the hidden risks in modular AI systems that rely on third-party components.
- LLM agents
- AI systems that use large language models to perform tasks autonomously or semi-autonomously, often relying on external skills or tools.
- Progressive-disclosure design
- A system design where information is revealed gradually, such as exposing skill descriptions before instructions in LLM agents.
- Resource amplification
- A scenario where an attack causes an AI system to consume excessive computational or financial resources while performing a task.
Wearables Powered by Artificial Intelligence: Latest Security Issue – RACmonitor - MedLearn Publishing
SecurityTerabytes of credentials leaked in massive supply-chain attack
Intelligence Community CIOs Warn Autonomous AI Agents Are Reshaping Cyber Threat Landscape - ExecutiveGov
Marine Corps, Coast Guard Lay Groundwork for AI Operations - GovCIO Media & Research
SecurityResearchers found a way to hijack devices through Zoom screen sharing
State Board roundup, 8.12.26: Board approves AI standards for K-12 schools - Idaho Education News
Idaho’s State Board has approved new AI standards for K-12 schools, aiming to integrate artificial intelligence into education curricula.
AI ResearchAI Access Control for Enterprise AI: Turning Policy Into Runtime Enforcement
Ken Walger discusses AI access control for enterprise AI, focusing on policy-driven runtime enforcement.
Target Appoints Its First-Ever AI Exec as the Retailer Pushes Deeper Into Artificial Intelligence. What It Means for TGT Stock. - Barchart.com
Target has appointed its first AI executive to spearhead its artificial intelligence strategy, signaling a major push into AI-driven retail innovation.
DSU launches new programs in artificial intelligence - Madison Daily Leader
South Dakota University has launched new programs in artificial intelligence to enhance student education and research capabilities.
Strong majority of Japanese firms have yet to fully embrace AI: Reuters poll - Reuters
A Reuters poll reveals that most Japanese firms have not yet fully integrated AI into their operations.
Youth advocates gather in New York to launch new AI standards - UN News
A coalition of youth advocates has convened in New York to introduce a new framework for AI governance, aiming to shape ethical standards before regulatory gaps widen.