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Shenyang University

Academic institutionasia · cn
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Research library6linked papers
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Selected work

Representative Papers

SCARA: A Semantics-Constrained Autonomous Remediation Agent for Opaque Industrial Software Vulnerabilities

May 19, 2026

This work proposes SCARA, the first end-to-end autonomous repair agent designed to address the challenge of automatically patching vulnerabilities in opaque industrial software (OIS)—systems that lack source code, symbols, and recompilability. Operating entirely at the binary level, SCARA employs a three-stage mechanism comprising operational state-aware validation (OSVA), repair synthesis under semantic constraints (RSA), and correctness verification (CVA). It integrates protocol-level mitigation, binary hardening, and SSCKG-guided patch generation to ensure both feasibility and semantic correctness of repairs. Evaluated on the OIS-RemedBench benchmark, SCARA achieves 100% repair precision with zero false positives, filters out 20.0% of infeasible cases, and attains an 88.9% final repair success rate after retries.

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Securing the Dark Matter: A Semantic-Enhanced Neuro-Symbolic Framework for Supply Chain Analysis of Opaque Industrial Software

May 08, 2026

Industrial critical infrastructure software is often deployed as stripped, unsigned binaries, rendering traditional software composition analysis ineffective and challenging existing methods to jointly capture structural and behavioral semantics. This work proposes a semantics-enhanced neuro-symbolic framework that integrates abstract interpretation with a reflective prompting mechanism to constrain a local large language model and suppress hallucinations. It employs a surjective transformation to compress code property graphs into typed software supply chain knowledge graphs and introduces a domain-adapted Graphormer with embedding-space subgraph matching to enable global risk reasoning and zero-day/APT attack identification in opaque binaries. Evaluated on three progressively challenging benchmarks and a hybrid physical-virtual platform comprising real devices from five industrial control vendors, the approach significantly improves detection rates for high-severity CVEs and semantic fidelity while substantially reducing false positives.

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SCALE-LoRA: Auditing Post-Retrieval LoRA Composition with Residual Merging and View Reliability

May 02, 2026

This work addresses the challenges of parameter incompatibility and unreliable compositional outputs when integrating LoRA adapters retrieved from an open pool. To tackle these issues, the authors propose the SCALE framework, which employs a Layer-Adaptive Sparse Residual Composition (LASRC) mechanism to perform residual merging while preserving critical linear anchors. SCALE further introduces multi-view disagreement analysis as an uncertainty signal and leverages a support-set loss proxy to evaluate reliability and guide adapter selection. Experimental results demonstrate that SCALE significantly enhances single-view performance on benchmarks such as FLAN-T5-Large and BBH, confirming its effectiveness and generalization capability across diverse decoder architectures.

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Recent publications

Latest Papers

SCARA: A Semantics-Constrained Autonomous Remediation Agent for Opaque Industrial Software Vulnerabilities

May 19, 2026

This work proposes SCARA, the first end-to-end autonomous repair agent designed to address the challenge of automatically patching vulnerabilities in opaque industrial software (OIS)—systems that lack source code, symbols, and recompilability. Operating entirely at the binary level, SCARA employs a three-stage mechanism comprising operational state-aware validation (OSVA), repair synthesis under semantic constraints (RSA), and correctness verification (CVA). It integrates protocol-level mitigation, binary hardening, and SSCKG-guided patch generation to ensure both feasibility and semantic correctness of repairs. Evaluated on the OIS-RemedBench benchmark, SCARA achieves 100% repair precision with zero false positives, filters out 20.0% of infeasible cases, and attains an 88.9% final repair success rate after retries.

0 citationsRead paper

Securing the Dark Matter: A Semantic-Enhanced Neuro-Symbolic Framework for Supply Chain Analysis of Opaque Industrial Software

May 08, 2026

Industrial critical infrastructure software is often deployed as stripped, unsigned binaries, rendering traditional software composition analysis ineffective and challenging existing methods to jointly capture structural and behavioral semantics. This work proposes a semantics-enhanced neuro-symbolic framework that integrates abstract interpretation with a reflective prompting mechanism to constrain a local large language model and suppress hallucinations. It employs a surjective transformation to compress code property graphs into typed software supply chain knowledge graphs and introduces a domain-adapted Graphormer with embedding-space subgraph matching to enable global risk reasoning and zero-day/APT attack identification in opaque binaries. Evaluated on three progressively challenging benchmarks and a hybrid physical-virtual platform comprising real devices from five industrial control vendors, the approach significantly improves detection rates for high-severity CVEs and semantic fidelity while substantially reducing false positives.

0 citationsRead paper

SCALE-LoRA: Auditing Post-Retrieval LoRA Composition with Residual Merging and View Reliability

May 02, 2026

This work addresses the challenges of parameter incompatibility and unreliable compositional outputs when integrating LoRA adapters retrieved from an open pool. To tackle these issues, the authors propose the SCALE framework, which employs a Layer-Adaptive Sparse Residual Composition (LASRC) mechanism to perform residual merging while preserving critical linear anchors. SCALE further introduces multi-view disagreement analysis as an uncertainty signal and leverages a support-set loss proxy to evaluate reliability and guide adapter selection. Experimental results demonstrate that SCALE significantly enhances single-view performance on benchmarks such as FLAN-T5-Large and BBH, confirming its effectiveness and generalization capability across diverse decoder architectures.

0 citationsRead paper