SkillConsist: Detecting Inconsistencies in Agent Skills via Bidirectional Graph Alignment
A recent study presents SkillConsist, a technique aimed at identifying inconsistencies in the Agent Skills utilized by LLM agents. While Agent Skills offer reusable functionalities, inconsistencies may result in hidden hazardous actions or erroneous Skill choices. Current approaches assess behaviors or security-property graphs against set categories or defined scopes, whereas PL-HCL employs an LLM-based framework to learn consistency from metadata, instructions, and resources. Nonetheless, the interplay between declaration and implementation can be convoluted, with a single concise declaration potentially linking to multiple implementation steps. SkillConsist tackles these issues by utilizing an LLM to distinguish between declaration and implementation content, creating behavior records enhanced by static analysis. These records generate declaration and implementation behavior graphs that are aligned bidirectionally to uncover inconsistencies. The paper can be accessed on arXiv under identifier 2608.07639.
Key facts
- SkillConsist is a new method for detecting inconsistencies in Agent Skills.
- Agent Skills provide reusable capabilities to LLM agents.
- Inconsistencies can expose undisclosed dangerous behavior or cause wrong Skill selection.
- Existing methods evaluate behaviors or security-property graphs against predefined categories or declared scopes.
- PL-HCL uses an LLM-based model to learn consistency across metadata, instructions, and resources.
- SkillConsist uses an LLM to separate declaration and implementation content into behavior records.
- Static analysis supplements implementation records.
- The paper is available on arXiv with identifier 2608.07639.
Entities
Institutions
- arXiv