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5G-Shark: A Network Security Auditor for 5G Subscriber Privacy and Unauthenticated Signalling Resilience

arXiv cs.CRby Oscar Lasierra ()1 min read2
AI summary · glm-5.3Summaries by Vani AI

5G-Shark audit tool pulls 5G phones onto rogue cells via cell-reselection manipulation, exposing identifier linkability and downgrade flaws in commercial networks.

The paper presents 5G-Shark, a security assessment tool and methodology built from open-source stacks and SDR hardware that manipulates the standardized cell-reselection criterion to pull a target User Equipment onto a self-created rogue cell with minimal service disruption. Evaluated against commercial 5G Standalone deployments, it requests subscriber identifiers, forces Radio Access Technology downgrade via crafted Registration Reject codes, and induces denial-of-service states. It distinguishes protocol-design limitations from implementation non-compliance, and provides empirical evidence that several commercial deployments re-allocate temporary identifiers in near-sequential steps, keeping successive values linkable and enabling persistent tracking despite correct subscriber ID concealment.

Key facts
  • Turns legitimate mobility procedure against subscriber by manipulating cell-reselection criterion
  • Built from open-source stacks and SDR; evaluated against commercial 5G SA deployments
  • Forces RAT downgrade via crafted Registration Reject codes and induces DoS states
  • Near-sequential temporary identifiers in several deployments enable persistent user tracking
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From the original article

1 min read at arxiv.org

The fifth generation of mobile networks was standardised with an explicit mandate to close long-standing privacy and security gaps, mandating the concealment of the subscriber's permanent identity, resistance to generational downgrade, and protection against location tracking.

arXiv cs.CR

By Oscar Lasierra · 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.