arXiv subjects

Jieshuai Yang

Publications and source records attributed to Jieshuai Yang.

4 recordsLinked to original sources

arXiv2026

Weaponizing Ground Truth: Data Poisoning Attacks by Exploiting Boundary Misalignment Between Antivirus Software and Learning-Based Detectors

Machine-learning (ML)-based malware detectors are commonly trained using labels obtained from antivirus (AV) engines and aggregation services (e.g., VirusTotal). This practice assumes AV-generated labels provide reliable supervision. However, small byte-level modifications can substantially alter AV verdicts while leaving the representations perceived by downstream ML detectors largely unchanged, producing label-feature inconsistencies that can contaminate training datasets and create poisoning opportunities for ML-based malware detection. We present Bi-Iocane, a black-box poisoning framework that exploits the reliance of malware-labeling pipelines on AV-generated labels. Bi-Iocane identifies AV-sensitive bytes and modifies them to induce label changes. It rewrites such bytes in malware to obtain benign labels (evasion-oriented poisoning) and injects malware-associated byte patterns into benign software to obtain malicious labels (defamation-oriented poisoning). These poisoned samples and their lightly modified variants corrupt training data and cause selected targets to be misclassified. We evaluate Bi-Iocane with 13 AV engines simulating AV aggregation services and eight ML detectors. For 30 malware and 30 benign clean targets, Bi-Iocane combines AV-specific manipulations to generate malware-to-benign and benign-to-malware poisoned samples whose all tested AV-based labels are flipped. After these poisoned samples and variants are used for downstream training, the resulting ML models misclassify 92.08% of the original clean targets on average with only a 0.06\% poisoning budget per target. Meanwhile, the poisoned models largely preserve clean-set performance, and six evaluated poisoning defenses show only limited mitigation. VirusTotal evaluation further confirms practical defamation risk and reveals potential evasion risk in real-world AV-to-ML labeling supply chains.

cs.CR↗

arXiv2026

Breaking the Black Box: Byte-Level Boundary Inference of Real-World Antivirus Systems

Existing approaches for understanding the detection logic of real-world antivirus (AV) software infer only binary malware/benign decisions from black-box queries, providing limited insight into the fine-grained decision-critical regions that govern AV detection. In this paper, we present \textbf{AVHunter}, the first framework for inferring byte-level decision-critical regions of real-world AV products under a black-box threat model. AVHunter constructs the first large-scale Byte-Level AV Boundary Dataset (BABD) by systematically probing 11 real-world AV products, revealing that modern AV detections are largely associated with a small number of compact decision-critical byte regions. Leveraging BABD, AVHunter trains AV-specific models that not only reproduce binary AV decisions, but also localize the decision-critical byte regions underlying these decisions, achieving an average boundary prediction recall of 85.07% while maintaining 97.43% detection agreement with the target AVs. We further validate that the predicted regions capture genuine AV decision knowledge through boundary-guided malware evasion, false-positive induction on benign executables, and a seven-month longitudinal study demonstrating that the inferred regions remain largely stable as AV products evolve. Overall, AVHunter moves beyond conventional binary-label AV modeling by enabling fine-grained boundary-region localization and revealing a new form of AV knowledge leakage with important implications for malware analysis, AV security, and boundary-aware defenses.

cs.CR↗

arXiv2026

LogICL: Demonstration Retrieval to Bridge the Semantic Gap in Cross-Domain Log Anomaly Detection

Effective log anomaly detection is critical to sustaining reliability in large-scale IT infrastructures. Transformer-based language models have advanced the field but require substantial computational resources and labeled data, exacerbating the cold-start problem in target domains with scarce logs. Cross-domain methods alleviate data scarcity by leveraging source-domain logs, but their reliance on surface lexical similarity limits generalization when structural and terminological differences obscure semantic equivalence. We propose LogICL, a demonstration-retrieval framework for cross-domain log anomaly detection. During training, LogICL constructs a sparse delta matrix by measuring the signed change in a frozen LLM's prediction error between zero-shot and one-shot inference for selected query-demonstration pairs. This outcome-level feedback supervises a lightweight encoder through a demonstration-utility loss, while maximum mean discrepancy and supervised contrastive losses promote domain alignment and anomaly discrimination. At inference, the encoder retrieves similarity-based anchors and expands them with candidates having the highest scores aggregated over precomputed positive delta relations. Thus, LogICL retrieves high-utility demonstrations without candidate-wise LLM evaluation and provides them to a frozen LLM for in-context anomaly prediction. Experiments on few-shot and target-domain zero-shot transfer benchmarks demonstrate strong performance across heterogeneous log systems. In the few-shot setting, LogICL outperforms Random-ICL and kNN-ICL in three of four transfers under the fixed split, while the benefit remains setting-dependent. Further analysis shows that LogICL can identify demonstrations with positive predictive utility despite low surface lexical similarity, helping bridge the semantic gap across logging systems.

cs.SE↗

arXiv2026

MACAA: Belief-Revision Multi-Agent Reasoning for Code Authorship Verification

Code authorship attribution (CAA) supports software forensics, plagiarism detection, and intellectual property protection. However, existing supervised CAA approaches suffer from scarce training data and closed-world assumptions: they require sufficient labeled code from fixed candidate-author sets, making training difficult in low-data cases and predictions unreliable for open-world test pairs with unseen samples, or heterogeneous code pairs. Large language models remove task-specific training, but direct prompting depends on costly expert-designed prompts, can hallucinate over complex heterogeneous code pairs, and rarely yields auditable evidence traces. We propose MACAA, a belief-revision-based multi-agent framework for training-free code authorship verification. MACAA comprises a Coordinator and four Expert Agents analyzing layout, lexical, syntactic, and programming-pattern evidence. The Coordinator gathers expert signals for expansion, discounts unreliable evidence through contraction, and resolves conflicts through revision to preserve belief consistency, replacing direct LLM judgment with auditable hypothesis refinement. MACAA achieves 89.15\% F1 on same-language benchmarks and 80.00\% on mixed cross-language pairs, outperforming the baselines overall in both same-language and cross-language evaluations.

cs.SE↗