Inferring 1-Minimal Trigger Configurations for Assessing Linux Kernel CVE Triggerability
Tongjie Wei, Peng Zhang, Zhiwen Hu, Xupu Hu, Chen Lyu, Gangyan Zeng
Abstract
Vendors assessing Linux kernel CVEs need to know whether a bug is triggerable under production-tailored configurations, not merely whether a version is affected, yet upstream reproducers and vulnerability databases rarely provide configuration-level context. We study minimal trigger-configuration inference: given a CVE entry and a target kernel version (optionally a baseline .config), we synthesize a Kconfig-satisfiable option set that remains effective after make olddefconfig and, when a reproducer is available, still triggers under a specified evaluation protocol; we then prune it to a 1-minimal (subset-minimal) boundary for evaluation. Our framework FCC links vulnerability cues to build-system symbols, completes implicit prerequisites under olddefconfig feedback to avoid silent rollback, and performs runtime-validated minimization guided by dependency topology. We evaluate on KernJC and KernelCTF, totaling 88 CVEs across multiple kernel versions. On the 88-CVE set, FCC improves the post-make olddefconfig configuration success rate from 62.5% (55/88) to 96.6% (85/88) over an olddef-only injection baseline; on the KernJC set, FCC reduces the average candidate set size by 78.7% compared to KernJC (Avg. 14.72 vs. 69.00 options per CVE). A stage-wise analysis of time and token costs shows that Stage I dominates overhead, while CVE-focused evidence selection substantially reduces this cost. By returning an effective and auditable 1-minimal configuration boundary, FCC helps vendors scope triggerability against their deployment configurations with a clear, tool-supported decision line.
Create a lesson
Related papers
Analog Pin Directionality as an Exfiltration Attack Surface in Mixed-Signal ICs
Ramana Ranganatham, Chirag Adiga, Michael Zuzak et al.
Characterizing Network Centralization and Observability in the Remote MCP Ecosystem
Muhammad Abdullah Sohail
When Agents Look Like Beacons: NIDS Evasion by Model Context Protocol Traffic
Muhammad Abdullah Sohail
Hamming Ideals and Grobner Bases for ISD-like Syndrome Decoding
Roberto La Scala, Marco Marchesin, Sharwan K. Tiwari
ASLEval: Measuring Privacy Exposure Displacement in LLM Agent Sessions
Guosen Wu, Huizhen Huang, Guoxiong Long et al.
CASHEWS: Source Preprocessor for LLM-based Malicious Package Detection
Jean-Charles Noirot Ferrand, David Adei, Anders Møller et al.