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新论文 · v1arXiv:2611.14946

LOHRE: Execution-Grounded Type Recovery via LLM-Orchestrated Hybrid Analysis

Hao Jiang, Yujie Yang, Tao Li, Chenhui Cui, Liangfeng Xu, Chengjie Liu, Weiping Wen, Rubing Huang 等 9 位作者

原文摘要

Documenting stripped binary functions for security analysis requires recovering function interfaces and user-defined type (UDT) layouts from machine code. Existing large language model (LLM)-based methods face two limitations: they lack execution-based validation to detect inferred UDTs inconsistent with actual memory layouts, and may overlook evidence about shared UDTs across functions without direct call relationships. We present LLM-Orchestrated Hybrid Reverse Engineering (LOHRE), a framework that treats inferred UDTs as falsifiable hypotheses and tests them against program execution. LOHRE uses a hypothesize-validate-refine loop to test candidate layouts through paired dynamic slice emulation of baseline and mutated inputs. To reduce the cost of processing lengthy execution histories, the Execution-Depth Heuristic (EDH) compresses paired traces into an execution-depth difference and a summary of the tested field's observed uses. A cross-function knowledge base aggregates compatible UDT hypotheses and field-level confidence scores to guide testing and hypothesis refinement. We evaluate LOHRE on RealType at four optimization levels and a Windows remote procedure call (RPC) dataset. LOHRE achieves an average UDT Structural accuracy of 57.85% across RealType's four optimization levels and 35.6% on Windows RPC, outperforming the evaluated static baselines. Disabling dynamic validation reduces accuracy to 19.28% on RealType and 9.2% on Windows RPC. Removing the cross-function knowledge base reduces accuracy to 41.03% on RealType and 10.0% on Windows RPC. Replacing EDH's compressed feedback with full execution traces reduces recovery accuracy while increasing token use by 3.7 times and runtime by 3.0 times. These results indicate that execution-based validation, compact feedback, and cross-function evidence contribute to accurate and efficient UDT recovery.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01721

GDBack: A Group-Delay Backdoor Attack on Speaker Identification via Analytic All-Pass Filtering

Bicheng Xiong, Tao Li, Chenhui Cui, Zedong Xing, Qien Li, Weiping Wen, Rubing Huang, Tsong Yueh Chen

原文摘要

Existing phase-based backdoor attacks on speaker identification rely on shared absolute phase patterns for cross-sample trigger consistency, while other audio backdoor attacks have explored shared transformations that produce input-dependent triggers. Whether phase-based triggers can achieve similar consistency without imposing identical output phase patterns remains unclear. We propose GDBack, a poison-only group-delay backdoor attack using cascaded second-order analytic all-pass filters. Applying a shared transformation across utterances ideally preserves spectral magnitude while introducing a consistent frequency-dependent phase response and group-delay contribution, without forcing identical output phases. Experiments on LibriSpeech and VoxCeleb1 with ECAPA-TDNN, x-vector, and RawNet3 achieve attack success rates above 94% across all six settings (97.00% on average), while maintaining benign accuracy close to clean models. Phase and magnitude interventions, inverse compensation, and parameter-sensitivity analyses support the role of the induced phase transformation in backdoor activation. Speech quality and content evaluations indicate limited perceptual and linguistic distortion, while robustness experiments, including real over-the-air tests, reveal varying effectiveness across conditions and architectures. These findings demonstrate that cross-sample trigger consistency can arise from a shared phase transformation without requiring a common absolute phase pattern.

cs.SD · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34431

SymLift: Neural Semantic Lifting for Compile-Free Vulnerability Detection in Context-Incomplete Code Fragments

Xiarun Chen, Jiaxing Xu, Bozhi Wu, Chenhui Cui, Chunrong Fang, Weiping Wen, Tao Li, Rubing Huang 等 9 位作者

原文摘要

Missing project context can prevent build-dependent analyzers from constructing program relations required for vulnerability detection, while neural predictions alone do not establish auditable derivations. We present SymLift, a compile-free framework centered on Vulnerability-Oriented Neural Semantic Lifting for context-incomplete code fragments. A fine-tuned large language model reconstructs vulnerability-relevant control-flow, data-flow, memory, and resource relations in a constrained LLVM-like intermediate representation (IR), without reconstructing the full project context or directly predicting vulnerability labels. A deterministic parser validates the IR and maps its supported operations and relations into normalized evidence facts. Explicit Datalog rules derive findings with supporting facts and derivation traces, ensuring deterministic reasoning for fixed facts and rules. We evaluate SymLift on FormAI, Juliet, and MegaVul. Reducing complete Juliet programs to function-level fragments lowers Clang compilation success from 100% to 12.00%, whereas SymLift's analysis completion rate decreases by only 1.17 percentage points. After task-specific fine-tuning, the lifter achieves a control-flow intersection over union (IoU) of 96.85% on FormAI. SymLift-Qwen-7B achieves a Target-F1 of 94.41% on FormAI and, across six vulnerability families on MegaVul, a micro-averaged precision of 98.24% and a micro-averaged Target-F1 of 91.66%. These results support accurate and auditable detection in the evaluated context-incomplete settings, even with partial program relations.

cs.SE · cs.CR

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arXiv ↗PDF ↗