Bowei Ning

Bowei Ning, Xuejun Zong, Lian Lian et al.

Critical-infrastructure operators are increasingly expected to assess and remediate vulnerabilities in deployed industrial software. However, much of this software exists as opaque industrial software (OIS), including stripped firmware, proprietary protocol handlers, and compiled control logic without source code, symbols, build environments, or hardware interfaces. While binary analysis can identify vulnerability candidates, existing automated repair systems largely rely on source code, compilable artifacts, sanitizer feedback, or instrumentable builds, leaving a gap between binary-level discovery and validated remediation. This paper presents SCARA, a Semantics-Constrained Autonomous Remediation Agent for OIS. SCARA operates under a source-unavailable defender model and connects upstream binary vulnerability candidates to conditionally validated remedies through a four-stage pipeline. Operational-state-aware verification (OSVA) filters infeasible candidates using a nine-component industrial state model; remediation synthesis (RSA) selects the strongest available remedy across protocol mitigation, binary hardening, and SSCKG-constrained source patches; and correctness validation (CVA) provides conditional correctness evidence via behavioral-coverage preservation, independent replay, and typed rejection feedback. On OIS-RemedBench, a 15-case benchmark spanning firmware, protocol handlers, and ICS/PLC artifacts, SCARA achieves observed 100% precision with no false positives, refutes 20.0% of cases as operationally infeasible, and reaches 88.9% remediation success after targeted reruns. To our knowledge, SCARA is the first end-to-end framework that connects binary vulnerability candidates to conditionally validated remediation for opaque industrial software.

Bowei Ning, Xuejun Zong, Lian Lian et al.

Automated vulnerability detection in critical-infrastructure software confronts a fundamental barrier: industrial software is routinely deployed as stripped, symbol-free binaries that deprive conventional Software Composition Analysis of the source-level transparency it requires. Existing binary analysis techniques close this Semantic Gap only partially -- graph-based detectors preserve structural syntax but discard behavioral semantics, while large language models supply rich semantic cues at the cost of unstable, hallucination-prone inference. To address this gap, we present a semantic-enhanced neuro-symbolic framework that reconstructs behavioral semantics directly from opaque binaries and performs tractable global risk reasoning. Three tightly coupled mechanisms drive this capability: (1) abstract interpretation combined with a reflexive prompting pipeline that structurally constrains a local LLM agent, effectively suppressing hallucinations; (2) a surjective transformation that compresses raw Code Property Graphs into typed Software Supply Chain Knowledge Graphs amenable to scalable reasoning; and (3) a domain-adapted Graphormer that captures long-range vulnerability propagation, augmented by embedding-space subgraph matching to uncover zero-day and APT-style attack patterns. Evaluated across three benchmarks of increasing domain specificity, the framework consistently outperforms all baselines on detection accuracy, semantic lifting fidelity, and APT fingerprint matching. Deployment on a hybrid virtual-physical testbed incorporating production-grade hardware from five ICS vendors further confirms strong detection coverage of high-impact CVEs while substantially reducing false-positive rates relative to leading commercial tools.