Skip to content
NewA calmer, faster ZeroHour

DERED Enhances Third-Party Library Detection

Original title: DERED: Enhancing Third-Party Library Detection in Binaries via Deceptive Reuse Mitigation

arXiv cs.CRby Shengjia Chang ()1 min read
AI summary · x-ai/grok-4.7Summaries by Vani AI

DERED mitigates deceptive reuse for improved TPL detection in binaries.

The paper introduces deceptive reuse as a misleading relationship in TPL code sharing. DERED is a novel approach that distinguishes real reuse from deceptive reuse. This enhances accuracy in third-party library detection for binary analysis.

Key facts
  • Deceptive reuse concept defined
  • Novel distinction method proposed
  • Improves TPL detection in binaries
  • Addresses misleading reuse relationships
Analyze this story with Vani AIImpact, who is affected and what defenders should do, in a new chat

From the original article

1 min read at arxiv.org

This paper introduces the concept of deceptive reuse a misleading reuse relationship between recipient software that share common TPL code and proposes a novel approach, DERED, to enhance TPL detection by distinguishing between real reuse and deceptive reuse.

arXiv cs.CR

By Shengjia Chang · arxiv.org

Read the full article at arXiv cs.CR

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.