Problem
Research questions and friction points this paper is trying to address.
vulnerability
validation
impact assessment
5G cores
Drishti: AI-Led Human-Directed Vulnerability Auditing for 5G Cores
🤖 AI Summary
Drishti框架通过AI辅助和人类指导的方法，解决了5G核心软件中漏洞验证和影响评估的稀缺问题，包括反模式验证、可达性分析、影响验证及修复完整性审查。
📝 Abstract
Candidate generation for open-source vulnerabilities is no longer scarce. AI-assisted code review now produces defect candidates cheaply, and industry programs pair them with expert human triage. The remaining scarcity is validation and impact assessment, and the gap is largest in critical-infrastructure software like 5G cores. Here, validation has four costs: verification, reachability, impact, and fix-completeness. We present Drishti, an AI-led human-directed vulnerability audit framework with four components, one per cost: (i) an anti-pattern catalog for verification, (ii) critical-path triage for reachability, (iii) concentric validation for impact, and (iv) patch-review for fix-completeness. Across audits of Open5GS and free5GC, Drishti produced three findings. The first is a pre-authentication NULL-dereference in the Open5GS NRF multipart parser, fixed upstream with a CVE requested. The second is an ASN.1-PER memory amplification in the free5GC NGAP decoder. A 2-byte input from a rogue gNodeB OOM-kills the AMF in 6.2 seconds. The third is a defective patch on CVE-2025-69248 whose defense-in-depth check is dead code before authentication.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-led human-directed
vulnerability audit
5G cores
anti-pattern catalog
concentric validation
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